From 57eacb8cc2dfd049023edf04e7ed6235cb57aa7f Mon Sep 17 00:00:00 2001 From: smorabit Date: Mon, 18 May 2026 19:20:27 +0200 Subject: [PATCH 1/4] Rebuild pkgdown site: add quickstart article, remove stale articles Ran pkgdown::build_site() to regenerate docs/ from the current vignettes/ directory. 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  • Reference
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    Page not found (404)

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    Site built with pkgdown 2.1.1.

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    Site built with pkgdown 2.2.0.

    diff --git a/docs/404.md b/docs/404.md new file mode 100644 index 0000000..5107f89 --- /dev/null +++ b/docs/404.md @@ -0,0 +1,3 @@ +Content not found. Please use links in the navbar. + +# Page not found (404) diff --git a/docs/CLAUDE.html b/docs/CLAUDE.html new file mode 100644 index 0000000..b63e282 --- /dev/null +++ b/docs/CLAUDE.html @@ -0,0 +1,99 @@ + +NA • compact + Skip to contents + + +
    +
    +
    + +

    NA

    + +
    + + +
    +

    Overview

    +

    compact is an R package to perform in-silico gene expression perturbation analysis in single-cell RNA-seq data, building off our previous method hdWGCNA. compact applies a primary perturbation to a set of selected genes, and leverages the gene co-expression network structure to propagate the signal to other linked genes in the network. compact infers the downstream consequences on the cell phenotypic manifold by calculating cell-cell transition probabilities, similar to RNA Velocity, and by performing Markov chain analyses.

    +
    +
    +

    Main funcitonality

    +

    The main function of compact is ModulePerturbation. This function consists of three major steps: ApplyPerturbation to simulate the direct perturbation to selected genes, ApplyPropagation to propagate the signal to linked genes via the network structure, and PerturbationTransitions to calculate the cell-cell transition probabilities. These three steps are highly modular and can be used in different contexts, for instance in the function TFPertrubation to perform perturbations of transcription factors and their downstream target genes.

    +
    +
    +

    Markov chain analyses

    +

    PredictCellFates.R contains the functions to perform different types of cell-fate analyses.

    +
    +
    +

    Vector field visualizations

    +

    Similar to RNA velocity, we can visualize the in-silico perturbation results directly on a two-dimensional view of the scRNA-seq dataset (ie, PCA or UMAP) as a vector field plot (relevant functions in PlotTransitionVectors.R)

    +
    +
    +

    Implementation

    +

    We implement this as an R package formatted with roxygen2 and pkgdown for the documentation website. compact relies on Seurat objects as the underlying data structure.

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    +
    +

    Testing

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    • Tests use testthat via devtools
      • +
    • Run tests with: devtools::test()
      • +
    • Check package with: devtools::check()
      • +
    • Test files live in tests/testthat/
      • +
    • Prefer testing exported functions over internal helpers
      • +
    • After writing testing code, ensure to run all tests. If errors are encountered, write solutions to those errors before continuing.
      • +
    • After testing and debugging, push the changes to Git on the bugfixes branch
      • +
    +
    +

    Code style

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    • All functions should have detailed docstrings.
      • +
    • Inline comments should be lowercase (with the exception of acronyms, function names, etc) and without excessive punctuation.
      • +
    +
    +

    Running code

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    • Code should be run within the compact_fresh conda environment.
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    + + +
    + + +
    + + + + + + + diff --git a/docs/articles/MPA_basics_microglia.html b/docs/articles/MPA_basics_microglia.html deleted file mode 100644 index 254da67..0000000 --- a/docs/articles/MPA_basics_microglia.html +++ /dev/null @@ -1,1404 +0,0 @@ - - - - - - - -Module Perturbation Analysis • compact - - - - - - - - - - Skip to contents - - -
    - - - - -
    -
    - -

    Module Perturbation Analysis

    - - - -
    MPA_basics_microglia.Rmd
    -
    - - - -

    Compiled: 22-03-2024

    -

    Source: vignettes/basic_tutorial.Rmd

    -

    Load the required R libraries

    -
    -# conda activate hdWGCNA
-
-# load R packages
-library(Seurat)
-library(ggplot2)
-library(cowplot)
-library(viridis)
-library(dplyr)
-library(tictoc)
-library(Matrix)
-library(hdWGCNA)
-theme_set(theme_cowplot())
-
-# RNA velocity packages that we need for some helper functions
-options(warn=-1)
-library(velocyto.R)
-library(velociraptor)
-
-# load the perturbation functions 
-source('/dfs7/swaruplab/smorabit/analysis/scWGCNA/bin/perturbation_functions.R')
-
-# spatial plotting functions (not sure if needed here)
-source("/pub/smorabit/hdWGCNA/bin/spatial_functions.R")
-
-# output directories
-setwd('/dfs7/swaruplab/smorabit/analysis/scWGCNA/perturb/')
-data_dir <- "data/"
-fig_dir <- 'figures/'
-
-# re-load MG dataset
-seurat_mg <- readRDS(file='/dfs7/swaruplab/smorabit/analysis/scWGCNA/microglia/data/AD_MG_scWGCNA.rds')
-
-# update the path to the hdWGCNA TOM
-net <- GetNetworkData(seurat_mg)
-net$TOMFiles <- '/dfs7/swaruplab/smorabit/analysis/scWGCNA/microglia/TOM/_ConsensusTOM-block.1.rda'
-seurat_mg <- SetNetworkData(seurat_mg, net)
-
-# make a cell neighborhood graph:
-seurat_mg <- FindNeighbors(seurat_mg, reduction='harmony')
-
-
-# load AD dataset (Morabito, Mathys, and Zhou integrated)
-#seurat_AD <- readRDS(file="/dfs7/swaruplab/smorabit/analysis/ADDS_2021/splitseq/integration/data/AD_integrated.rds" )
    -

    test

    -
    -seurat_obj <- seurat_mg
-
-# settings for upregulation
-mod <- 'MG-M4'
-perturb_dir <- 1 # downreg
-perturbation_name <- 'M4_up'
-graph <- 'RNA_nn'
-n_hubs <- 10
-n_iters <- 2
-delta_scale <- 0.2
-corr_sigma <- 0.05
-n_threads <- 4
-slot <- 'counts'
-assay <- 'RNA'
-wgcna_name <- 'MG'
-group.by <- NULL
-group_name <- NULL
-
-
-# module perturbation code
-
-# switch to this assay:
-orig_assay <- DefaultAssay(seurat_obj)
-DefaultAssay(seurat_obj) <- assay
-
-# get the TOM:
-TOM <- GetTOM(seurat_obj, wgcna_name)
-
-# get modules 
-modules <- GetModules(seurat_obj, wgcna_name)
-
-# get top n hub genes in selected module 
-hubs <- GetHubGenes(seurat_obj, n=n_hubs, wgcna_name=wgcna_name) %>% 
-    subset(module == mod)
-hub_genes <- hubs$gene_name
-
-# get the non-hubs in this module:
-non_hub_genes <- subset(modules, module == mod & !(gene_name %in% hub_genes)) %>% .$gene_name
-module_genes <- subset(modules, module == mod) %>% .$gene_name
-
-# which cells are we selecting to apply the perturbation?
-if(is.null(group.by)){
-    cells_use <- colnames(seurat_obj)
-} else{
-    cells_use <- seurat_obj@meta.data %>% 
-        subset(get(group.by) %in% group_name) %>%
-        rownames
-}
-
-###########################################################################
-# Set up the observed expression matrix
-# TODO: make this into its own function
-###########################################################################
-
-# get the expression matrix:
-exp <- GetAssayData(seurat_obj, slot=slot, assay = assay)
-
-###########################################################################
-# Part 1: apply the perturbation to the selected hub genes:
-###########################################################################
-
-print('Applying in-silico perturbation to hub genes...')
-exp_per <- ApplyPerturbation(
-    seurat_obj,
-    exp,
-    features = hub_genes,
-    perturb_dir = perturb_dir,
-    cells_use = cells_use,
-    # group.by = group.by,
-    # group_name = group_name,
-    slot = slot,
-    assay = assay
-)
-
-
-
-###########################################################################
-# Part 2: apply signal propagation throughout this module 
-###########################################################################
-
-# get the TOM for the genes in this module
-cur_TOM <- TOM[module_genes, module_genes]
-
-# set the TOM to zero for non hub genes so only the connections with the
-# perturbed hub genes play a role
-#cur_TOM[,non_hub_genes] <- 0
-# cur_TOM[hub_genes,] <- 0
-
-print('Applying signal propagation throughout co-expression network...')
-exp_prop <- ApplyPropagation(
-    seurat_obj,
-    exp[module_genes,cells_use],
-    exp_per[module_genes,cells_use],
-    network = cur_TOM,
-    perturb_dir = perturb_dir,
-    delta_scale = delta_scale,
-    n_iters = n_iters
-)
-
-exp_prop2 <- exp_prop
-#  print(mean(exp_prop['PTPN2',]))
-
-# something messed up happens after here
-
-if(!all(colnames(seurat_obj) %in% cells_use)){
-    exp_prop_other <- exp[module_genes,setdiff(colnames(seurat_obj), cells_use)]
-    exp_prop <- cbind(exp_prop, exp_prop_other)
-    exp_prop <- exp_prop[,colnames(seurat_obj)]
-}
-
-# append the expression matrices:
-exp_simulated <- rbind(
-    exp_per[!(rownames(exp_per) %in% module_genes),], # genes that aren't in this module
-    exp_prop # genes from this module with perturbations
-)
-
-# make sure the order matches the original expression matrix
-exp_simulated <- exp_simulated[rownames(seurat_obj),colnames(seurat_obj)]
-
-# add perturbation assay to the Seurat object:
-perturb_assay <- CreateAssayObject(
-    exp_simulated,
-    assay = perturbation_name
-)
-seurat_obj[[perturbation_name]] <- perturb_assay
-
-# normalize the perturbation assay
-seurat_obj <- NormalizeData(seurat_obj, perturbation_name)
-
-
-###########################################################################
-# Part 3: compute transition probabilities
-# break code out of the function
-###########################################################################
-
-graph <- 'RNA_nn'
-features <- module_genes
-
-# check for selected Graph 
-cell_graph <- Graphs(seurat_obj, slot=graph)
-cell_graph <- Matrix::Matrix(cell_graph)
-diag(cell_graph) <- 1
-
-
-# get the observed and the perturbed expression matrices:
-exp_obs <- GetAssayData(seurat_obj, assay=assay, slot=slot)
-exp_per <- GetAssayData(seurat_obj, assay=perturbation_name, slot=slot)
-
-# subset by selected features
-# and convert to dense matrix (TODO: figure out a way to avoid this?)
-exp_obs <- as.matrix(exp_obs[features,])
-exp_per <- as.matrix(exp_per[features,])
-
-delta <- exp_per - exp_obs
-
-# run the Velocyto colDeltaCor function
-cc <- colDeltaCor(exp_obs, delta, nthreads=n_threads)
-
-# write this to a file just for comparison purposes
-#save(cc, file = paste0(data_dir, perturbation_name, '_colDeltaCor.rda'))
-
-# transfer from HPC to google drive (run in bash)
-# rclone copy data/M4_up_colDeltaCor.rda gDrive:
-
-# fill the diagnoal with zeros (cells won't transition to self)
-diag(cc) <- 0
-
-# compute transition probs between cells
-tp <- exp(cc / corr_sigma) * cell_graph
-tp <- t(t(tp)/Matrix::colSums(tp)); # tp shows transition from a given column cell to different row cells
-tp <- as(tp,'dgCMatrix') # cast to sparse matrix
-
-# add rownames / colnames 
-rownames(tp) <- colnames(seurat_obj)
-colnames(tp) <- colnames(seurat_obj)
-
-# convert to Seurat Graph object, and add it to the Seurat object with the perturbation name
-tp <- Seurat::as.Graph(tp)
-graph_name <- paste0(perturbation_name, '_tp')
-seurat_obj@graphs[graph_name] <- tp
-
-
-# load the other TP:
-# rclone copy gDrive:M4_up_tp.rda ./
-load('data/M4_up_tp.rda')
-tp <- Seurat::as.Graph(tp)
-graph_name <- paste0(perturbation_name, '_tp')
-seurat_obj@graphs[graph_name] <- tp
-
-#-------------------------------------------------------------------------#
-# Plotting the result as a vector field plot on top of the UMAP
-# (need to make this into proprer functions later)
-#-------------------------------------------------------------------------#
-
-# This function gives us the dataframe with vector coordinates 
-# (ars) and vector distances (arsd)
-vectors <- PerturbationVectors(
-  seurat_obj,
-  perturbation_name = perturbation_name,
-  reduction = 'umap', # default
-  arrow_scale = 1 # default
-)
-ars <- vectors$ars 
-arsd <- vectors$arsd
-
-# Get the UMAP embedding from the seurat object 
-# and subset to only keep cells that we have arrows for.
-emb <- seurat_obj@reductions$umap@cell.embeddings
-emb <- emb[rownames(ars),]
-
-# run the velociraptor function to make a grid of arrows instead of one 
-# arrow per cell
-grid.df <- velociraptor::gridVectors(emb, arsd)
-
-
-# compute the length of each arrow 
-distances <- sqrt(
-  ((grid.df$end.xd - grid.df$start.UMAP_1)^2) - 
-  ((grid.df$end.yd - grid.df$start.UMAP_2)^2) 
-)
-distances <- ifelse(is.nan(distances), 0, distances)
-grid.df$length <- distances
-
-# make a dataframe to plot with ggplot
-plot_df <- as.data.frame(emb)
-plot_df$pseudotime <- seurat_obj@meta.data[rownames(emb), 'pseudotime']
-
-# plot the scatter plot
-p <- ggplot(plot_df, aes(x=UMAP_1, y=UMAP_2, color=pseudotime)) +
-  geom_point(alpha=0.25, size=2) +
-  scale_color_gradientn(colors=plasma(256))
-  
-# add the arrows
-p <- p +
-  geom_segment(
-    data = grid.df,
-    inherit.aes=FALSE,
-    aes(
-      x=start.UMAP_1, 
-      y=start.UMAP_2, 
-      xend=end.xd, 
-      yend=end.yd,
-      alpha= length
-    ), 
-    arrow=grid::arrow(length=unit(0.1, "cm")), size=0.25
-  ) +
-  scale_alpha_continuous(range=c(0.8,1)) +
-  coord_equal() +
-  umap_theme() +
-  NoLegend() +
-  ggtitle(paste0(mod))
-
-pdf(paste0(fig_dir, 'vectorfield_', perturbation_name, '_testing2.pdf'), width=6, height=3)
-p
-dev.off()
    -

    Example 1: Downregulation of microglia module M4

    -
    -# options:
-perturbation_name = 'M4_down'
-seurat_obj = seurat_mg
-mod = 'MG-M4'
-wgcna_name <- 'MG'
-n_hubs <- 10
-perturb_dir <- -1
-delta_scale <- 1
-n_iters=3
-
-# run a knock-down of the top 10 hub genes from MG-M4
-# should take 2-3 min to run
-seurat_mg <- ModulePerturbation(
-    seurat_mg, 
-    mod = mod,
-    perturb_dir = perturb_dir,
-    n_hubs=n_hubs,
-    perturbation_name = perturbation_name,
-    graph = 'RNA_nn',
-    delta_scale = delta_scale,
-    wgcna_name = wgcna_name
-)
-
-#-------------------------------------------------------------------------#
-# Plotting the result as a vector field plot on top of the UMAP
-# (need to make this into proprer functions later)
-#-------------------------------------------------------------------------#
-
-# This function gives us the dataframe with vector coordinates 
-# (ars) and vector distances (arsd)
-vectors <- PerturbationVectors(
-  seurat_mg,
-  perturbation_name = perturbation_name,
-  reduction = 'umap', # default
-  arrow_scale = 1 # default
-)
-ars <- vectors$ars 
-arsd <- vectors$arsd
-
-# Get the UMAP embedding from the seurat object 
-# and subset to only keep cells that we have arrows for.
-emb <- seurat_mg@reductions$umap@cell.embeddings
-emb <- emb[rownames(ars),]
-
-# run the velociraptor function to make a grid of arrows instead of one 
-# arrow per cell
-grid.df <- velociraptor::gridVectors(emb, arsd)
-
-
-# compute the length of each arrow 
-distances <- sqrt(
-  ((grid.df$end.xd - grid.df$start.UMAP_1)^2) - 
-  ((grid.df$end.yd - grid.df$start.UMAP_2)^2) 
-)
-distances <- ifelse(is.nan(distances), 0, distances)
-grid.df$length <- distances
-
-# make a dataframe to plot with ggplot
-plot_df <- as.data.frame(emb)
-plot_df$pseudotime <- seurat_mg@meta.data[rownames(emb), 'pseudotime']
-
-# plot the scatter plot
-p <- ggplot(plot_df, aes(x=UMAP_1, y=UMAP_2, color=pseudotime)) +
-  geom_point(alpha=0.25, size=2) +
-  scale_color_gradientn(colors=plasma(256))
-  
-# add the arrows
-p <- p +
-  geom_segment(
-    data = grid.df,
-    inherit.aes=FALSE,
-    aes(
-      x=start.UMAP_1, 
-      y=start.UMAP_2, 
-      xend=end.xd, 
-      yend=end.yd,
-      alpha= length
-    ), 
-    arrow=grid::arrow(length=unit(0.1, "cm")), size=0.25
-  ) +
-  scale_alpha_continuous(range=c(0.8,1)) +
-  coord_equal() +
-  umap_theme() +
-  NoLegend() +
-  ggtitle(paste0(mod))
-
-pdf(paste0(fig_dir, 'vectorfield_', perturbation_name, '.pdf'), width=6, height=3)
-p
-dev.off()
-
-#-------------------------------------------------------------------------#
-# Make another vector field plot showing the arrows on each cell 
-#-------------------------------------------------------------------------#
-
-# make a dataframe to plot with ggplot
-plot_df <- as.data.frame(emb)
-plot_df$pseudotime <- seurat_mg@meta.data[rownames(emb), 'pseudotime']
-
-# plot the scatter plot
-p <- ggplot(plot_df, aes(x=UMAP_1, y=UMAP_2, color=pseudotime)) +
-  geom_point(alpha=0.25, size=1) +
-  scale_color_gradientn(colors=plasma(256))
-  
-# add the arrows
-p <- p +
-  geom_segment(
-    data = ars,
-    inherit.aes=FALSE,
-    aes(x=x0, y=y0, xend=x1, yend=y1), arrow=grid::arrow(length=unit(0.1, "cm")), size=0.1
-  ) +
-  scale_alpha_continuous(range=c(0.8,1)) +
-  coord_equal() +
-  umap_theme() +
-  NoLegend() +
-  ggtitle(paste0(mod))
-
-pdf(paste0(fig_dir, 'vectorfield_', perturbation_name, '_all_arrows.pdf'), width=10, height=5)
-p
-dev.off()
    -

    Example 1.1: Upregulation of microglia module M4

    -
    -# options:
-perturbation_name = 'M4_up'
-seurat_obj = seurat_mg
-mod = 'MG-M4'
-wgcna_name <- 'MG'
-n_hubs <- 10
-perturb_dir <- 1
-n_iters=2
-
-# what are the hub genes?
-GetHubGenes(seurat_mg, n_hubs=10) %>% subset(module == mod) %>% .$gene_name
-
-# run a knock-in of the top 10 hub genes from MG-M4
-# should take 2-3 min to run
-seurat_mg <- ModulePerturbation(
-    seurat_mg, 
-    mod = mod,
-    perturb_dir = perturb_dir,
-    n_hubs=n_hubs,
-    perturbation_name = perturbation_name,
-    graph = 'RNA_nn',
-    wgcna_name = wgcna_name
-)
-
-#-------------------------------------------------------------------------#
-# Plotting the result as a vector field plot on top of the UMAP
-# (need to make this into proprer functions later)
-#-------------------------------------------------------------------------#
-
-# This function gives us the dataframe with vector coordinates 
-# (ars) and vector distances (arsd)
-vectors <- PerturbationVectors(
-  seurat_mg,
-  perturbation_name = perturbation_name,
-  reduction = 'umap', # default
-  arrow_scale = 1 # default
-)
-ars <- vectors$ars 
-arsd <- vectors$arsd
-
-# Get the UMAP embedding from the seurat object 
-# and subset to only keep cells that we have arrows for.
-emb <- seurat_mg@reductions$umap@cell.embeddings
-emb <- emb[rownames(ars),]
-
-# run the velociraptor function to make a grid of arrows instead of one 
-# arrow per cell
-grid.df <- velociraptor::gridVectors(emb, arsd)
-
-
-# compute the length of each arrow 
-distances <- sqrt(
-  ((grid.df$end.xd - grid.df$start.UMAP_1)^2) - 
-  ((grid.df$end.yd - grid.df$start.UMAP_2)^2) 
-)
-distances <- ifelse(is.nan(distances), 0, distances)
-grid.df$length <- distances
-
-# make a dataframe to plot with ggplot
-plot_df <- as.data.frame(emb)
-plot_df$pseudotime <- seurat_mg@meta.data[rownames(emb), 'pseudotime']
-
-# plot the scatter plot
-p <- ggplot(plot_df, aes(x=UMAP_1, y=UMAP_2, color=pseudotime)) +
-  geom_point(alpha=0.25, size=2) +
-  scale_color_gradientn(colors=plasma(256))
-  
-# add the arrows
-p <- p +
-  geom_segment(
-    data = grid.df,
-    inherit.aes=FALSE,
-    aes(
-      x=start.UMAP_1, 
-      y=start.UMAP_2, 
-      xend=end.xd, 
-      yend=end.yd,
-      alpha= length
-    ), 
-    arrow=grid::arrow(length=unit(0.1, "cm")), size=0.25
-  ) +
-  scale_alpha_continuous(range=c(0.8,1)) +
-  coord_equal() +
-  umap_theme() +
-  NoLegend() +
-  ggtitle(paste0(mod))
-
-pdf(paste0(fig_dir, 'vectorfield_', perturbation_name, '_test.pdf'), width=6, height=3)
-p
-dev.off()
-
-#-------------------------------------------------------------------------#
-# Make another vector field plot showing the arrows on each cell 
-#-------------------------------------------------------------------------#
-
-# make a dataframe to plot with ggplot
-plot_df <- as.data.frame(emb)
-plot_df$pseudotime <- seurat_mg@meta.data[rownames(emb), 'pseudotime']
-
-# plot the scatter plot
-p <- ggplot(plot_df, aes(x=UMAP_1, y=UMAP_2, color=pseudotime)) +
-  geom_point(alpha=0.25, size=1) +
-  scale_color_gradientn(colors=plasma(256))
-  
-# add the arrows
-p <- p +
-  geom_segment(
-    data = ars,
-    inherit.aes=FALSE,
-    aes(x=x0, y=y0, xend=x1, yend=y1), arrow=grid::arrow(length=unit(0.1, "cm")), size=0.1
-  ) +
-  scale_alpha_continuous(range=c(0.8,1)) +
-  coord_equal() +
-  umap_theme() +
-  NoLegend() +
-  ggtitle(paste0(mod))
-
-pdf(paste0(fig_dir, 'vectorfield_', perturbation_name, '_all_arrows.pdf'), width=10, height=5)
-p
-dev.off()
    -

    Debugging the weird behavior of the upregulated modules

    -
    -DefaultAssay(seurat_mg) <- 'M4_up'
-
-
-# group variable for vln plots
-group.by <- 'pseudotime_bins_10'
-
-# one of the hub genes to be perturbed
-cur_gene <- 'PTPN2'
-
-# one of the indirectly pertubed genes
-GetHubGenes(seurat_mg, n_hubs=15) %>% subset(module == mod)
-cur_gene <- 'ACER3'
-
-p1 <- VlnPlot(seurat_mg, features = cur_gene, pt.size=0.1, group.by = group.by, assay='RNA') +
-  NoLegend() + xlab('') + ylab('') +  ylim(0,5) + 
-  theme(
-    axis.text.x = element_blank(),
-    plot.margin=margin(c(0,0,0,0))
-  )
-
-p2 <- VlnPlot(seurat_mg, features = cur_gene, pt.size=0.1, group.by = group.by, assay=perturbation_name) +
-  NoLegend()  + ylab('') + ylim(0,5) + theme(
-    plot.margin=margin(c(0,0,0,0))) + ggtitle('') + xlab('Pseudotime -->')
-
-
-pdf(paste0(fig_dir, 'vln_compare_', perturbation_name, '_', cur_gene, '2.pdf'), width=6, height=4)
-print(p1 / p2)
-dev.off()
-
-#-------------------------------------------------------------------------#
-# Debugging: Check expression of hub genes (PTPN2)
-#-------------------------------------------------------------------------#
-
-n_hubs = 10
-wgcna_name = 'MG'
-assay = 'RNA'
-slot = 'counts'
-
-    # get the TOM:
-    TOM <- GetTOM(seurat_obj, wgcna_name)
-
-    # get modules 
-    modules <- GetModules(seurat_obj, wgcna_name)
-
-
-  # get top n hub genes in selected module 
-  hubs <- GetHubGenes(seurat_obj, n=n_hubs, wgcna_name=wgcna_name) %>% 
-      subset(module == mod)
-  hub_genes <- hubs$gene_name
-
-  # get the non-hubs in this module:
-  non_hub_genes <- subset(modules, module == mod & !(gene_name %in% hub_genes)) %>% .$gene_name
-  module_genes <- subset(modules, module == mod) %>% .$gene_name
-
- 
-    cells_use <- colnames(seurat_obj)
-
-
-  # get the expression matrix:
-  exp <- GetAssayData(seurat_obj, slot=slot, assay = assay)
-
-  ###########################################################################
-  # Part 1: apply the perturbation to the selected hub genes:
-  ###########################################################################
-
-  print('Applying in-silico perturbation to hub genes...')
-  exp_per <- ApplyPerturbation(
-      seurat_mg,
-      exp,
-      features = hub_genes,
-      perturb_dir = perturb_dir,
-      cells_use = cells_use,
-      # group.by = group.by,
-      # group_name = group_name,
-      slot = slot,
-      assay = assay
-  )
-
-    module_genes <- subset(modules, module == mod) %>% .$gene_name
-  cur_TOM <- TOM[module_genes, module_genes]
-
-
-
-  ###########################################################################
-  # Part 2: apply the propaga
-  ###########################################################################
-
-
-  network <- cur_TOM
-  exp = exp[module_genes,cells_use]
-  exp_per = exp_per[module_genes,cells_use]
-  n_iters <- 3
-  
-  penalty <- 0.2
-
-    cur_gene <- 'ACER3'
-
-
-  # compute the difference between the perturbation and the observed expression
-  delta <- exp_per - exp
-
-  # backups
-  delta_orig <- delta 
-  exp_orig <- exp 
-  exp_per_orig <- exp_per
-
-  # what is the sign of the perturbation dir?
-  perturb_sign <- sign(perturb_dir)
-
-    # run the signaling processing step iteratively
-    for(i in 1:n_iters){  
-
-        # compute the dot product between the TOM coefficients and the exp matrix
-        delta <- network %*% delta
-
-        # add a penalty to the delta so the values don't explode
-        delta <- delta * penalty
-
-        # update the expression matrix:
-        exp_update <- exp_per + delta # (delta * perturb_sign)
-        exp_update[exp_update < 0] <- 0
-
-        exp_update <- round(exp_update)
-
-        # update the delta
-        delta <- exp_update - exp_per
-
-    }
-
-    print(mean(exp[cur_gene,]))
-    print(mean(exp_per[cur_gene,]))
-    print(mean(exp_update[cur_gene,]))
-
-    # add perturbation assay to the Seurat object:
-  perturb_assay <- CreateAssayObject(
-      exp_per,
-      assay = 'test'
-  )
-  seurat_mg[['test']] <- perturb_assay
-
-  seurat_mg <- NormalizeData(seurat_mg, 'test')
-
-
-# one of the hub genes to be perturbed
-cur_gene <- 'PTPN2'
-
-p1 <- VlnPlot(seurat_mg, features = cur_gene, pt.size=0.1, group.by = group.by, assay='RNA') +
-  NoLegend() + xlab('') + ylab('') +  ylim(0,5) + 
-  theme(
-    axis.text.x = element_blank(),
-    plot.margin=margin(c(0,0,0,0))
-  )
-
-p2 <- VlnPlot(seurat_mg, features = cur_gene, pt.size=0.1, group.by = group.by, assay='test') +
-  NoLegend()  + ylab('') + ylim(0,5) + theme(
-    plot.margin=margin(c(0,0,0,0))) + ggtitle('') + xlab('Pseudotime -->')
-
-
-
-pdf(paste0(fig_dir, 'vln_compare_', perturbation_name, '_', cur_gene, '_test.pdf'), width=6, height=4)
-print(p1 / p2)
-dev.off()
-
-
-sum(exp[cur_gene,] == 0) / ncol(seurat_mg)
-sum(exp_per[cur_gene,] == 0) / ncol(seurat_mg)
-
-
-#-------------------------------------------------------------------------#
-# Debugging applyperturbation
-#-------------------------------------------------------------------------#
-
-  seurat_obj <- seurat_mg
-  features = hub_genes  
-  cells_use=colnames(seurat_obj)
-
-
-  # split the exp matrix based on selected features
-  exp_hubs <- exp[features,cells_use]
-  exp_non <- exp[!(rownames(exp) %in% features),cells_use]
-
-  # initialize progress bar
-  pb <- utils::txtProgressBar(min = 0, max = length(features), style = 3, width = 50, char = "=")
-
-  # for each feature, model expression 
-  # TODO: add options for other models aside from ZINB like Poisson etc.
-  # TODO: add option to return the model (just in case?)
-  # TODO: should the SampleZINB function return a sparse vector?
-  sim_data <- do.call(rbind, lapply(1:length(features), function(i){
-      
-      feature <- features[i]
-
-      # get the obserbed gene expression
-      yobs <- exp_hubs[feature,]
-
-      # what is the proportion of zeros for this gene?
-      zero_prop <- sum(yobs == 0) / ncol(exp_hubs)
-      zero_prop <- zero_prop * 0.75
-
-      # model expression as a ZINB
-      model <- ModelZINB(seurat_obj, feature=feature, cells_use=cells_use, slot=slot)
-      
-      # simulate data by samping the distribution 
-      ysim <- SampleZINB(model, yobs)
-
-      # update progress bar
-      setTxtProgressBar(pb, i)
-
-      # penalization:
-      #ysim <- round(ysim * 0.1)
-
-      # just set a bunch of them to zero
-     # test <- sample(1:length(ysim), round(length(ysim)*zero_prop))
-    #  ysim[test] <- 0
-
-      # apply the perturbation
-     # ysim <- ysim * perturb_dir
-      #ysim <- 
-
-      # return the simulated data
-      ysim
-
-  }))
-
-
-# plot_df <- rbind(
-#   data.frame(
-#     value = yobs,
-#     group = 'obs'
-#   ),
-#   data.frame(
-#     value = ysim,
-#     group = 'sim'
-#   )
-# )
-
-
-
-# p <- ggplot(plot_df, aes(y=value, x=group)) + 
-#   geom_violin() + 
-#   facet_wrap(~group, scales='free')
-
-
-#   pdf(paste0(fig_dir, 'vln_compare_', perturbation_name, '_', cur_gene, '_test_sim.pdf'), width=6, height=4)
-#   print(p)
-#   dev.off()
-
-    # close progress bar
-    close(pb)
-
-    # convert to sparse matrix:
-    sim_data <- Matrix(sim_data, sparse=TRUE)
-
-    # is this a knock-in or knock-down?
-    sim_data <- sim_data * perturb_dir
-
-    # apply the perturbation to the expression matrix
-    delta_hub <- exp_hubs + sim_data
-
-    # if in a knock-down experiment any feature fell below 0 expression, make it 0.
-    delta_hub[delta_hub < 0 ] <- 0
-
-    # update the expression matrix with the perturbed values
-    exp_new <- rbind(delta_hub, exp_non)
-
-    #get the data for the cells that we did not perturb
-    if(!all(colnames(seurat_obj) %in% cells_use)){
-      exp_other <- exp[,!(colnames(exp) %in% cells_use)]
-      exp_new <- cbind(exp_new, exp_other)
-    }
-
-    exp_new <- exp_new[rownames(exp),colnames(seurat_obj)]
-
-
-  exp_per <- exp_new
-
-    # add perturbation assay to the Seurat object:
-  perturb_assay <- CreateAssayObject(
-      exp_per,
-      assay = 'test'
-  )
-  seurat_mg[['test']] <- perturb_assay
-
-  seurat_mg <- NormalizeData(seurat_mg, 'test')
-
-
-
-# one of the hub genes to be perturbed
-cur_gene <- 'PTPN2'
-
-p1 <- VlnPlot(seurat_mg, features = cur_gene, pt.size=0.1, group.by = group.by, assay='RNA') +
-  NoLegend() + xlab('') + ylab('') +  ylim(0,5) + 
-  theme(
-    axis.text.x = element_blank(),
-    plot.margin=margin(c(0,0,0,0))
-  )
-
-p2 <- VlnPlot(seurat_mg, features = cur_gene, pt.size=0.1, group.by = group.by, assay='test') +
-  NoLegend()  + ylab('') + ylim(0,5) + theme(
-    plot.margin=margin(c(0,0,0,0))) + ggtitle('') + xlab('Pseudotime -->')
-
-
-
-pdf(paste0(fig_dir, 'vln_compare_', perturbation_name, '_', cur_gene, '_test.pdf'), width=6, height=4)
-print(p1 / p2)
-dev.off()
-
-
-sum(exp[cur_gene,] == 0) / ncol(seurat_mg)
-sum(exp_per[cur_gene,] == 0) / ncol(seurat_mg)
    -
    -

    How to access the perturbed expression info -

    -

    After running ModulePerturbation, we have created a new gene -expression matrix containing the perturbed expression values and we have -a cell-cell transition probability matrix.

    -
    -# access perturbed expression matrix using the name 
-# that you gave to the perturbation
-X <- GetAssayData(seurat_obj, assay = 'M4_down')
-
-# to access the transition probability graph, use the 
-# name of the perturbation with _tp appended to the end
-graph_name <- paste0(perturbation_name, '_tp')
-tp <- Graphs(seurat_mg, graph_name)
-
-# tp is a cells by cells matrix containing transition probabilities
-dim(tp)
    -

    Plot the change in gene expression between observed and perturbed for -each gene in module M4 vs the kME

    -
    -mod <- "MG-M4"
-
-# get the kME for all the genes in module M4
-hubs <- GetHubGenes(seurat_mg, n_hubs=Inf) %>% 
-  subset(module == mod)
-hub_genes <- hubs %>% slice_max(kME, n=10) %>% .$gene_name
-hubs$hub <- ifelse(hubs$gene_name %in% hub_genes, 'hub', 'other')
-
-# get the observed and perturbed expression matrices and compute the deltas
-X <- GetAssayData(seurat_mg, assay='RNA', slot='counts')[hubs$gene_name,]
-X_norm <- GetAssayData(seurat_mg, assay='RNA', slot='data')[hubs$gene_name,]
-X_per <- GetAssayData(seurat_mg, assay=perturbation_name, slot='counts')[hubs$gene_name,]
-delta <- X_per - X
-
-# compute the average observed expression for each gene:
-avg_exp <- rowSums(X_norm) / ncol(X_norm)
-
-# compute the mean delta value for each gene across all the cells:
-plot_df <- reshape2::melt(as.matrix(delta)) 
-plot_df <- plot_df%>% 
-  group_by(Var1) %>% 
-  summarise(delta = mean(value)) %>%
-  dplyr::rename(gene_name = Var1)
-
-# join this df with the kME df
-plot_df <- dplyr::left_join(
-  x = plot_df,
-  y = hubs,
-  by = 'gene_name'
-)
-
-# add col for avg exp 
-plot_df$avg_exp <- avg_exp
-
-# only label hub genes
-plot_df$label <- ifelse(plot_df$hub == 'hub', plot_df$gene_name, '')
-
-# plot with ggplot
-p1 <- plot_df %>% 
-  ggplot(aes(x = delta, y = kME, color = hub, size=avg_exp)) + 
-  geom_point() + 
-  geom_smooth(
-    data = plot_df, inherit.aes = FALSE,
-    aes(x=delta, y=kME),
-    method = 'lm',
-    color = 'black'
-    ) + 
-    ggpubr::stat_cor(
-      data = plot_df, inherit.aes = FALSE,
-    aes(x=delta, y=kME)
-    ) + 
-    geom_label_repel(
-      label=plot_df$label, 
-      max.overlaps=Inf,
-      color='black',
-      size=3
-    ) +
-    theme(
-      axis.line.x = element_blank(),
-      axis.line.y = element_blank(),
-      panel.border = element_rect(color='black', fill=NA, linewidth=1),
-      panel.grid.major = element_line(linewidth=0.25, color='lightgrey')
-    ) + 
-    xlab(bquote("Perturbation delta")) +
-    ylab('kME') + labs(color="")
-
-# Similar plot but delta vs average expression in observed
-p2 <- plot_df %>% 
-  ggplot(aes(x = delta, y = avg_exp, color = hub, size=kME)) + 
-  geom_point() + 
-  geom_smooth(
-    data = plot_df, inherit.aes = FALSE,
-    aes(x=delta, y=kME),
-    method = 'lm',
-    color = 'black'
-    ) +  
-    ggpubr::stat_cor(
-      data = plot_df, inherit.aes = FALSE,
-      aes(x=delta, y=avg_exp)
-    ) + 
-    geom_label_repel(
-      label=plot_df$label, 
-      max.overlaps=Inf,
-      color='black',
-      size=3
-    ) +
-    theme(
-      axis.line.x = element_blank(),
-      axis.line.y = element_blank(),
-      panel.border = element_rect(color='black', fill=NA, linewidth=1),
-      panel.grid.major = element_line(linewidth=0.25, color='lightgrey')
-    ) + 
-    xlab(bquote("Perturbation delta")) +
-    ylab('Average Expression') + labs(color="")
-
-pdf(paste0(fig_dir, 'kME_delta_', perturbation_name,'.pdf'), width=12, height=4)
-p1 + p2
-dev.off()
    -

    Plot violin plots of some individual genes of interest

    -
    -# group variable for vln plots
-group.by <- 'pseudotime_bins_10'
-
-# one of the hub genes to be perturbed
-cur_gene <- 'CD163'
-
-# another gene in module MG-M4 which has a high kME but is not a hub gene 
-cur_gene <- 'ACER3'
-
-p1 <- VlnPlot(seurat_mg, features = cur_gene, pt.size=0.1, group.by = group.by, assay='RNA') +
-  NoLegend() + xlab('') + ylab('') +  ylim(0,5) + 
-  theme(
-    axis.text.x = element_blank(),
-    plot.margin=margin(c(0,0,0,0))
-  )
-
-p2 <- VlnPlot(seurat_mg, features = cur_gene, pt.size=0.1, group.by = group.by, assay=perturbation_name) +
-  NoLegend()  + ylab('') + ylim(0,5) + theme(
-    plot.margin=margin(c(0,0,0,0))) + ggtitle('') + xlab('Pseudotime -->')
-
-
-pdf(paste0(fig_dir, 'vln_compare_', perturbation_name, '_', cur_gene, '.pdf'), width=6, height=4)
-print(p1 / p2)
-dev.off()
    -

    Example 2: Upregulation of module MG-M2

    -
    -# options:
-perturbation_name = 'M2_up'
-seurat_obj = seurat_mg
-mod = 'MG-M2'
-wgcna_name <- 'MG'
-n_hubs <- 10
-perturb_dir <- 1
-n_iters=3
-
-# run a knock-in of the top 10 hub genes from MG-M2
-# should take 2-3 min to run
-seurat_mg <- ModulePerturbation(
-    seurat_mg, 
-    mod = mod,
-    perturb_dir = perturb_dir,
-    n_hubs=n_hubs,
-    perturbation_name = perturbation_name,
-    graph = 'RNA_nn',
-    wgcna_name = wgcna_name
-)
-
-#-------------------------------------------------------------------------#
-# Plotting the result as a vector field plot on top of the UMAP
-# (need to make this into proprer functions later)
-#-------------------------------------------------------------------------#
-
-# This function gives us the dataframe with vector coordinates 
-# (ars) and vector distances (arsd)
-vectors <- PerturbationVectors(
-  seurat_mg,
-  perturbation_name = perturbation_name
-)
-ars <- vectors$ars 
-arsd <- vectors$arsd
-
-# Get the UMAP embedding from the seurat object 
-# and subset to only keep cells that we have arrows for.
-emb <- seurat_mg@reductions$umap@cell.embeddings
-emb <- emb[rownames(ars),]
-
-# run the velociraptor function to make a grid of arrows instead of one 
-# arrow per cell
-grid.df <- velociraptor::gridVectors(emb, arsd)
-
-
-# compute the length of each arrow 
-distances <- sqrt(
-  ((grid.df$end.xd - grid.df$start.UMAP_1)^2) - 
-  ((grid.df$end.yd - grid.df$start.UMAP_2)^2) 
-)
-distances <- ifelse(is.nan(distances), 0, distances)
-grid.df$length <- distances
-
-# make a dataframe to plot with ggplot
-plot_df <- as.data.frame(emb)
-plot_df$pseudotime <- seurat_mg@meta.data[rownames(emb), 'pseudotime']
-
-# plot the scatter plot
-p <- ggplot(plot_df, aes(x=UMAP_1, y=UMAP_2, color=pseudotime)) +
-  geom_point(alpha=0.25, size=2) +
-  scale_color_gradientn(colors=plasma(256))
-  
-# add the arrows
-p <- p +
-  geom_segment(
-    data = grid.df,
-    inherit.aes=FALSE,
-    aes(
-      x=start.UMAP_1, 
-      y=start.UMAP_2, 
-      xend=end.xd, 
-      yend=end.yd,
-      alpha= length
-    ), 
-    arrow=grid::arrow(length=unit(0.1, "cm")), size=0.25
-  ) +
-  scale_alpha_continuous(range=c(0.8,1)) +
-  coord_equal() +
-  umap_theme() +
-  NoLegend() +
-  ggtitle(paste0(mod))
-
-pdf(paste0(fig_dir, 'vectorfield_', perturbation_name, '.pdf'), width=6, height=3)
-p
-dev.off()
-
-
-#-------------------------------------------------------------------------#
-# Make another vector field plot showing the arrows on each cell 
-#-------------------------------------------------------------------------#
-
-
-# make a dataframe to plot with ggplot
-plot_df <- as.data.frame(emb)
-plot_df$pseudotime <- seurat_mg@meta.data[rownames(emb), 'pseudotime']
-
-# plot the scatter plot
-p <- ggplot(plot_df, aes(x=UMAP_1, y=UMAP_2, color=pseudotime)) +
-  geom_point(alpha=0.25, size=1) +
-  scale_color_gradientn(colors=plasma(256))
-  
-# add the arrows
-p <- p +
-  geom_segment(
-    data = ars,
-    inherit.aes=FALSE,
-    aes(x=x0, y=y0, xend=x1, yend=y1), arrow=grid::arrow(length=unit(0.1, "cm")), size=0.1
-  ) +
-  scale_alpha_continuous(range=c(0.8,1)) +
-  coord_equal() +
-  umap_theme() +
-  NoLegend() +
-  ggtitle(paste0(mod))
-
-pdf(paste0(fig_dir, 'vectorfield_', perturbation_name, '_all_arrows.pdf'), width=10, height=5)
-p
-dev.off()
    -

    Example 3: Downregulation of module MG-M2

    -
    -# options:
-perturbation_name = 'M2_down'
-seurat_obj = seurat_mg
-mod = 'MG-M2'
-wgcna_name <- 'MG'
-n_hubs <- 10
-perturb_dir <- -1
-n_iters=3
-
-# run a knock-in of the top 10 hub genes from MG-M2
-# should take 2-3 min to run
-seurat_mg <- ModulePerturbation(
-    seurat_mg, 
-    mod = mod,
-    perturb_dir = perturb_dir,
-    n_hubs=n_hubs,
-    perturbation_name = perturbation_name,
-    graph = 'RNA_nn',
-    wgcna_name = wgcna_name
-)
-
-#-------------------------------------------------------------------------#
-# Plotting the result as a vector field plot on top of the UMAP
-# (need to make this into proprer functions later)
-#-------------------------------------------------------------------------#
-
-# This function gives us the dataframe with vector coordinates 
-# (ars) and vector distances (arsd)
-vectors <- PerturbationVectors(
-  seurat_mg,
-  perturbation_name = perturbation_name
-)
-ars <- vectors$ars 
-arsd <- vectors$arsd
-
-# Get the UMAP embedding from the seurat object 
-# and subset to only keep cells that we have arrows for.
-emb <- seurat_mg@reductions$umap@cell.embeddings
-emb <- emb[rownames(ars),]
-
-# run the velociraptor function to make a grid of arrows instead of one 
-# arrow per cell
-grid.df <- velociraptor::gridVectors(emb, arsd)
-
-
-# compute the length of each arrow 
-distances <- sqrt(
-  ((grid.df$end.xd - grid.df$start.UMAP_1)^2) - 
-  ((grid.df$end.yd - grid.df$start.UMAP_2)^2) 
-)
-distances <- ifelse(is.nan(distances), 0, distances)
-grid.df$length <- distances
-
-# make a dataframe to plot with ggplot
-plot_df <- as.data.frame(emb)
-plot_df$pseudotime <- seurat_mg@meta.data[rownames(emb), 'pseudotime']
-
-# plot the scatter plot
-p <- ggplot(plot_df, aes(x=UMAP_1, y=UMAP_2, color=pseudotime)) +
-  geom_point(alpha=0.25, size=2) +
-  scale_color_gradientn(colors=plasma(256))
-  
-# add the arrows
-p <- p +
-  geom_segment(
-    data = grid.df,
-    inherit.aes=FALSE,
-    aes(
-      x=start.UMAP_1, 
-      y=start.UMAP_2, 
-      xend=end.xd, 
-      yend=end.yd,
-      alpha= length
-    ), 
-    arrow=grid::arrow(length=unit(0.1, "cm")), size=0.25
-  ) +
-  scale_alpha_continuous(range=c(0.8,1)) +
-  coord_equal() +
-  umap_theme() +
-  NoLegend() +
-  ggtitle(paste0(mod))
-
-pdf(paste0(fig_dir, 'vectorfield_', perturbation_name, '.pdf'), width=6, height=3)
-p
-dev.off()
-
-#-------------------------------------------------------------------------#
-# Plotting the kME vs Delta
-#-------------------------------------------------------------------------#
-
-# get the kME for all the genes in module M4
-hubs <- GetHubGenes(seurat_mg, n_hubs=Inf) %>% 
-  subset(module == mod)
-hub_genes <- hubs %>% slice_max(kME, n=10) %>% .$gene_name
-hubs$hub <- ifelse(hubs$gene_name %in% hub_genes, 'hub', 'other')
-
-# get the observed and perturbed expression matrices and compute the deltas
-X <- GetAssayData(seurat_mg, assay='RNA', slot='counts')[hubs$gene_name,]
-X_norm <- GetAssayData(seurat_mg, assay='RNA', slot='data')[hubs$gene_name,]
-X_per <- GetAssayData(seurat_mg, assay=perturbation_name, slot='counts')[hubs$gene_name,]
-delta <- X_per - X
-
-# compute the average observed expression for each gene:
-avg_exp <- rowSums(X_norm) / ncol(X_norm)
-
-# compute the mean delta value for each gene across all the cells:
-plot_df <- reshape2::melt(as.matrix(delta)) 
-plot_df <- plot_df%>% 
-  group_by(Var1) %>% 
-  summarise(delta = mean(value)) %>%
-  dplyr::rename(gene_name = Var1)
-
-# join this df with the kME df
-plot_df <- dplyr::left_join(
-  x = plot_df,
-  y = hubs,
-  by = 'gene_name'
-)
-
-# add col for avg exp 
-plot_df$avg_exp <- avg_exp
-
-# only label hub genes
-plot_df$label <- ifelse(plot_df$hub == 'hub', plot_df$gene_name, '')
-
-# plot with ggplot
-p1 <- plot_df %>% 
-  ggplot(aes(x = delta, y = kME, color = hub, size=avg_exp)) + 
-  geom_point() + 
-  geom_smooth(
-    data = plot_df, inherit.aes = FALSE,
-    aes(x=delta, y=kME),
-    method = 'lm',
-    color = 'black'
-    ) + 
-    ggpubr::stat_cor(
-      data = plot_df, inherit.aes = FALSE,
-    aes(x=delta, y=kME)
-    ) + 
-    geom_label_repel(
-      label=plot_df$label, 
-      max.overlaps=Inf,
-      color='black',
-      size=3
-    ) +
-    theme(
-      axis.line.x = element_blank(),
-      axis.line.y = element_blank(),
-      panel.border = element_rect(color='black', fill=NA, linewidth=1),
-      panel.grid.major = element_line(linewidth=0.25, color='lightgrey')
-    ) + 
-    xlab(bquote("Perturbation delta")) +
-    ylab('kME') + labs(color="")
-
-# Similar plot but delta vs average expression in observed
-p2 <- plot_df %>% 
-  ggplot(aes(x = delta, y = avg_exp, color = hub, size=kME)) + 
-  geom_point() + 
-  geom_smooth(
-    data = plot_df, inherit.aes = FALSE,
-    aes(x=delta, y=kME),
-    method = 'lm',
-    color = 'black'
-    ) +  
-    ggpubr::stat_cor(
-      data = plot_df, inherit.aes = FALSE,
-      aes(x=delta, y=avg_exp)
-    ) + 
-    geom_label_repel(
-      label=plot_df$label, 
-      max.overlaps=Inf,
-      color='black',
-      size=3
-    ) +
-    theme(
-      axis.line.x = element_blank(),
-      axis.line.y = element_blank(),
-      panel.border = element_rect(color='black', fill=NA, linewidth=1),
-      panel.grid.major = element_line(linewidth=0.25, color='lightgrey')
-    ) + 
-    xlab(bquote("Perturbation delta")) +
-    ylab('Average Expression') + labs(color="")
-
-pdf(paste0(fig_dir, 'kME_delta_', perturbation_name,'.pdf'), width=12, height=4)
-p1 + p2
-dev.off()
    -
    -
    -
    - - - - -
    - - - - - - - diff --git a/docs/articles/TF_tutorial.html b/docs/articles/TF_tutorial.html deleted file mode 100644 index bafe82a..0000000 --- a/docs/articles/TF_tutorial.html +++ /dev/null @@ -1,253 +0,0 @@ - - - - - - - -transcription factor perturbation analysis • compact - - - - - - - - - - Skip to contents - - -
    - - - - -
    -
    - -

    transcription factor perturbation analysis

    - - - -
    TF_tutorial.Rmd
    -
    - - - -

    Compiled: 28-10-2024

    -

    Source: vignettes/basic_tutorial.Rmd

    -
    -

    Introduction -

    -

    This tutorial covers the basics of using -compact to perform transcription factor -(TF) perturbation analysis in single-cell transcriptomic data.

    -

    For this tutorial we will use an integrated single-nucleus RNA-seq -dataset of microglia from three different studies of Alzheimer’s -disease, as we described previously in the hdWGCNA -paper.

    -

    The in-silico perturbations that we demonstrate in this tutorial are -based on a TF regulatory networks, which represent regulatory -relationships between TFs and downstream target genes. In order to run -these pertubations, we first need to perform co-expression network -analysis and group genes into modules by running hdWGCNA, -and then we need to construct -a TF regulatory network. For this tutorial, co-expression network -analysis has already been run but we will demonstrate constructing the -TF regulatory network.

    -
    -
    -

    Load the dataset -

    -

    Download the tutorial dataset from these Google Drive links:

    - -

    First we load the required R libraries and the tutorial dataset. When -loading the tutorial dataset, you need to update the filepath to the -co-expression adjacency matrix (TOM) within the Seurat object (shown -below).

    -
    -library(tidyverse)
-library(cowplot)
-library(patchwork)
-library(Seurat)
-library(velocyto.R)
-library(hdWGCNA)
-library(compact)
-theme_set(theme_cowplot())
-
-# set random seed for reproducibility
-set.seed(12345)
-
-# load the processed alzheimer's microglia dataset
-seurat_obj <- readRDS(file='AD_MG_hdWGCNA_COMPACT_tutorial.rds')
-
-# update the path to the co-expression network adjacency matrix
-net <- GetNetworkData(seurat_obj)
-net$TOMFiles <- 'AD_MG_TOM.rda' # it is better to use the absolute path
-seurat_obj <- SetNetworkData(seurat_obj, net)
    -

    If you have not already done so, we need to construct a -nearest-neighbors graph in the Seurat object. This is a key step in the -typical single-cell clustering pipeline.

    -
    seurat_obj <- FindNeighbors(
-    seurat_obj,
-    reduction='harmony',nearest neighbors?
-    assay = 'RNA',
-    annoy.metric = 'cosine'
-)
    -
    -
    -

    Construct TF Regulatory Network -

    -

    First we construct a TF regulatory network using hdWGCNA by following -this -tutorial, and we first described this approach in our paper Childs -& Morabito et al., Cell Reports (2024). We include the code -below, but please visit the hdWGCNA -TF network tutorial for further explanations about this -analysis.

    -
    -Run the TF regulatory network code -

    Part 1: Identify TFs in promoter regions

    -
    -# load these packages into R:
-library(JASPAR2020)
-library(motifmatchr)
-library(TFBSTools)
-library(EnsDb.Hsapiens.v86)
-library(BSgenome.Hsapiens.UCSC.hg38)
-library(GenomicRanges)
-library(xgboost)
-
-# get the set of Motifs from JASPAR2020
-pfm_core <- TFBSTools::getMatrixSet(
-  x = JASPAR2020,
-  opts = list(collection = "CORE", tax_group = 'vertebrates', all_versions = FALSE)
-)
-
-# scan promoter regions for TF motifs
-seurat_obj <- MotifScan(
-  seurat_obj,
-  species_genome = 'hg38',
-  pfm = pfm_core,
-  EnsDb = EnsDb.Hsapiens.v86
-)
    -

    Part 2: Construct TF Regulatory Network

    -
    -# get the motif df:
-motif_df <- GetMotifs(seurat_obj)
-
-# keep all TFs, and then remove all genes from the grey module
-tf_genes <- unique(motif_df$gene_name)
-modules <- GetModules(seurat_obj)
-nongrey_genes <- subset(modules, module != 'grey') %>% .$gene_name
-genes_use <- c(tf_genes, nongrey_genes)
-
-# update the gene list and re-run SetDatExpr
-seurat_obj <- SetWGCNAGenes(seurat_obj, genes_use)
-seurat_obj <- SetDatExpr(seurat_obj)
-
-# define model params:
-model_params <- list(
-    objective = 'reg:squarederror',
-    max_depth = 1,
-    eta = 0.1,
-    nthread=8,
-    alpha=0.5
-)
-
-# construct the TF network
-seurat_obj <- ConstructTFNetwork(seurat_obj, model_params)
    -

    Part 3: Define TF Regulons

    -
    -# define the TF regulons
-seurat_obj <- AssignTFRegulons(
-    seurat_obj,
-    strategy = "A",
-    reg_thresh = 0.01,
-    n_tfs = 10
-)
-
-# calculate expression scores for TF regulonspositive regulons
-seurat_obj <- RegulonScores(
-    seurat_obj,
-    target_type = 'positive',
-    ncores=8
-)
-seurat_obj <- RegulonScores(
-    seurat_obj,
-    target_type = 'negative',
-    cor_thresh = -0.05,
-    ncores=8
-)
    -
    -
    -
    -

    Perform in-silico TF perturbations -

    -

    Resulting from the previous step, we now have the TF regulatory -network for the microglia dataset. Let’s briefly check some of these -results before performing perturbation analysis.

    -
    -
    -
    - - - - -
    - - - - - - - diff --git a/docs/articles/basic_tutorial.html b/docs/articles/basic_tutorial.html deleted file mode 100644 index fb4bdf0..0000000 --- a/docs/articles/basic_tutorial.html +++ /dev/null @@ -1,572 +0,0 @@ - - - - - - - -co-expression module perturbation analysis • compact - - - - - - - - - - Skip to contents - - -
    - - - - -
    -
    - -

    co-expression module perturbation analysis

    - - - -
    basic_tutorial.Rmd
    -
    - - - -

    Compiled: 28-10-2024

    -

    Source: vignettes/basic_tutorial.Rmd

    -
    -

    Introduction -

    -

    This tutorial covers the basics of using -compact to perform module perturbation -analysis on co-expression network/module on single-cell data.

    -

    This tutorial covers the basics of using -compact to perform in-silico pertubation -experiments in single-cell transcriptomics data. For this tutorial we -will use an integrated single-nucleus RNA-seq dataset of microglia from -three different studies of Alzheimer’s disease, as we described -previously in the hdWGCNA -paper.

    -

    The in-silico perturbations that we demonstrate in this tutorial are -based on gene co-expression modules, which are unbiased clusters of -genes that are highly co-expressed in a gene-gene co-expression network. -In order to run these pertubations, we first need to perform -co-expression network analysis and group genes into modules by running -hdWGCNA. -This has already been run on the tutorial microglia dataset, where we -have identified four gene modules.

    -
    -
    -

    Load the dataset -

    -

    Download the tutorial dataset from these Google Drive links:

    - -

    First we load the required R libraries and the tutorial dataset. When -loading the tutorial dataset, you need to update the filepath to the -co-expression adjacency matrix (TOM) within the Seurat object (shown -below).

    -
    -library(tidyverse)
-library(cowplot)
-library(patchwork)
-library(Seurat)
-library(velocyto.R)
-library(hdWGCNA)
-library(compact)
-theme_set(theme_cowplot())
-
-# set random seed for reproducibility
-set.seed(12345)
-
-# load the processed alzheimer's microglia dataset
-seurat_obj <- readRDS(file='AD_MG_hdWGCNA_COMPACT_tutorial.rds')
-
-# update the path to the co-expression network adjacency matrix
-net <- GetNetworkData(seurat_obj)
-net$TOMFiles <- 'AD_MG_TOM.rda' # it is better to use the absolute path
-seurat_obj <- SetNetworkData(seurat_obj, net)
    -

    If you have not already done so, we need to construct a -nearest-neighbors graph in the Seurat object. This is a key step in the -typical single-cell clustering pipeline.

    -
    seurat_obj <- FindNeighbors(
-    seurat_obj,
-    reduction='harmony',nearest neighbors?
-    assay = 'RNA',
-    annoy.metric = 'cosine'
-)
    -

    Next we visualize the clusters in this dataset as a UMAP plot.

    -
    -# Get the UMAP embedding from the seurat object
-emb <- Reductions(seurat_obj, 'umap')@cell.embeddings
-
-# make a dataframe to plot with ggplot
-plot_df <- as.data.frame(emb)
-plot_df$functional_anno <- seurat_obj$functional_anno
-
-# plot with ggplot
-p <- plot_df %>%
-  ggplot(aes(x=UMAP_1, y=UMAP_2, color=functional_anno)) +
-  scale_color_viridis_d(option = "plasma") +  
-  rasterise(geom_point(size=2, alpha=0.5), dpi = 300) +
-  ggtitle("microglia functional_anno") +
-  hdWGCNA::umap_theme()
-
-p
    -

    -
    -
    -

    Perform in-silico module perturbation -

    -

    In this example, we will perform an in-silico knock-down for one of -our microglia co-expression modules. First, let’s look at the expression -level of these modules (module eigengene).

    -
    -# add module expression levels (MEs) to the seurat metadata
-MEs <- GetMEs(seurat_obj)
-meta <- seurat_obj@meta.data
-seurat_obj@meta.data <- cbind(meta, MEs)
-
-# dotplot
-p <- DotPlot(
-    seurat_obj, features=c('MG-M4', 'MG-M1', 'MG-M3', 'MG-M2'), 
-    group.by='functional_anno'
-) +
-    coord_flip() + 
-    scale_color_gradient2(high='black', mid='grey75', low='grey99') +
-    theme(
-        axis.text.x = element_blank(),
-        axis.ticks.x = element_blank()
-    ) + ylab('Trajectory →') + xlab('') + NoLegend()
-
-p
    -

    -

    The expression of microglia module M4 is concentrated at the end of -the trajectory, within the activated microglia population. Next we will -perform a knock-down experiment of this module, and we will predict how -this perturbation will alter cellular states.

    -
    -

    Apply a knock-down using ModulePerturbation -

    -

    Here we use the function ModulePerturbation to apply an -in-silico knock-down of module MG-M4. This function consists of three -main steps, which are all conveniently included in this function.

    -
      -

    • Apply a primary perturbation to the hub genes of the -co-expression module. For each hub gene, we perform zero-inflated count -data regression to model the observed gene expression distribution. We -simulate a perturbation by sampling from this distribution, then adding -or subtracting the sampled counts to the observed counts for a knock-in -or a knock-down respectively.

      • -

    • Apply a secondary perturbation to all other genes in the -co-expression module. The primary perturbation is propagated to other -genes by exploiting the co-expression network structure. Genes with -stronger connections in the network will exhibit a stronger -perturbation.

      • -

    • Calculate cell-cell transition probabilities based on the -observed gene expression matrix and the perturbed gene expression -matrix.

      • -
    -

    For this analysis we will perform the primary perturbation on the top -10 hub genes of module MG-M4. Let’s check which genes we will be -perturbing.

    -
    -hub_genes <- GetHubGenes(seurat_obj, n_hubs=10) %>% subset(module == 'MG-M4')
-hub_genes$gene_name
    -
    [1] "PTPN2"   "SLC11A1" "ACSL1"   "SPATA6"  "TBC1D14" "TMEM163" "ERC2"   
-[8] "DPYD"    "CD163"   "TLR2"
    -
    -# provide a name for the perturbation, which will be stored in the Seurat object
-perturbation_name <- 'M4_down'
-
-# run a knock-down of the top 10 hub genes from MG-M4
-# should take 2-3 min to run
-seurat_obj <- ModulePerturbation(
-    seurat_obj,
-    mod = "MG-M4",
-    perturb_dir = -1, # negative indicates knock-down, positive indicates knock-in
-    perturbation_name = perturbation_name,
-    graph = 'RNA_nn', 
-    n_hubs = 10
-)
    -

    The main ouputs of this function are a “perturbed” gene expression -matrix, and a cell-cell transition matrix, which we can access from the -Seurat object using the following code.

    -
    -# get the perturbed expression matrix
-X_per <- GetAssayData(
-  seurat_obj, assay=perturbation_name,
-  layer='counts' 
-)
-
-# get the cell-cell transition matrix
-tp <- Graphs(seurat_obj, paste0(perturbation_name, '_tp'))
    -

    Next, let’s compare the observed and perturbed gene expression -distributions for one of the MG-M4 hub genes, TMEM163. We expect that in -the pertubed matrix, the expression of TMEM163 will be lower than in the -observed matrix.

    -
    -# Generate 10 colors from the plasma palette
-plasma_colors <- viridis::viridis(10, option = "plasma")
-
-# group variable for vln plots
-group.by <- 'functional_anno'
-
-# one of the hub genes to be perturbed
-cur_gene <- 'TMEM163'
-
-p1 <- VlnPlot(seurat_obj, features = cur_gene, pt.size=0.1, group.by = group.by, assay='RNA') +
-  scale_fill_manual(values = plasma_colors) +  # Use the extracted colors here
-  NoLegend() + xlab('') + ylab('') +  ylim(0,5) +
-  theme(
-    axis.text.x = element_blank(),
-    plot.margin=margin(c(0,0,0,0))
-  )
-
-p2 <- VlnPlot(seurat_obj, features = cur_gene, pt.size=0.1, group.by = group.by, assay=perturbation_name) +
-  scale_fill_manual(values = plasma_colors) +  # Use the extracted colors here
-  NoLegend()  + ylab('') + ylim(0,5) + theme(
-    plot.margin=margin(c(0,0,0,0))) + ggtitle('') + xlab('Functional Annotation / Pseudotime -->')
-
-
-p1 / p2
    -

    -
    -
    -

    Visualize predicted cell transitions after perturbation -

    -

    Here we will visualize the predicted cell-cell transitions after the -in-silico knock-down of MG-M4. Similar to other methods like RNA Velocity or -CellOracle, we -visualize the inferred transitions as a vector field overlaid on a -dimensionality reduction. We include the function -PlotTransitionVectors to generate this plot. This function -leverages some code from the R package velocyto.R, however -in a future update we would like to remove this dependency since this -package can be difficult to install on some machines.

    -
    -p <- PlotTransitionVectors(
-    seurat_obj,
-    perturbation_name = perturbation_name,
-    reduction = 'umap',
-    color.by='pseudotime',
-    grid_resolution = 25 
-)
-
-p <- p + 
-    NoLegend() + 
-    scale_color_gradientn(colors=viridis::plasma(256)) + 
-    coord_equal() + 
-    ggtitle(perturbation_name)
-
-p
    -

    -

    To generate the vector field, the cell-cell transition probabilities -computed for this perturbation are used to infer the cells with similar -expression profiles, and vectors are shown between these cells. In order -to avoid overcrowding the plot, these vectors are averaged in a grid -rather than showing a vector for each cell.

    -
    -
    -

    Compare cell state similarity before and after perturbation -

    -

    To further analyze the effect of the perturbation, we compute the -E-distance between different functional annotations within the original -and perturbed Seurat objects. The E-distance helps assess the similarity -between different functional cell states present in the dataset.

    -

    First, we calculate energy distance between clusters in the observed -gene expression matrix using the function -ComputeDistance.

    -
    -# Calculate similarity distance for original scRNA-seq data, method selected "edist" - energy distance
-df_edist_original <- ComputeDistance(
-  seurat_obj, 
-  groupby = 'functional_anno', 
-  reduction = 'pca', 
-  method = "edist", 
-  verbose = TRUE  
-)
-
-# # Define your custom order
-custom_order <- levels(seurat_obj$functional_anno)
-
-# Rearrange rows and columns of df_edist_observed based on the custom order
-df_edist_original_reordered <- df_edist_original[custom_order, custom_order]
-
-# View the reordered dataframe
-df_edist_original_reordered
    -
    -Output -
    #               Homeostasis Surveillance1 Surveillance2 Surveillance3
-# Homeostasis      0.000000     10.140967     17.220091    12.1832043
-# Surveillance1   10.140967      0.000000      2.866413     3.1124290
-# Surveillance2   17.220091      2.866413      0.000000     1.2850047
-# Surveillance3   12.183204      3.112429      1.285005     0.0000000
-# Surveillance4    8.723064      4.462216      3.529919     0.7661988
-# Surveillance5    5.735601      7.660914      8.219343     3.6764471
-# Activated1      13.835812     10.549585      7.522241     3.5081678
-# Activated2      22.369534     18.084185     13.030198     8.7718675
-# Activated3      17.792197     20.061153     17.297088    11.6027919
-# Phagocytosis    26.392094     29.687218     25.688176    19.5139817
-#               Surveillance4 Surveillance5 Activated1 Activated2 Activated3
-# Homeostasis       8.7230645      5.735601  13.835812  22.369534  17.792197
-# Surveillance1     4.4622158      7.660914  10.549585  18.084185  20.061153
-# Surveillance2     3.5299193      8.219343   7.522241  13.030198  17.297088
-# Surveillance3     0.7661988      3.676447   3.508168   8.771868  11.602792
-# Surveillance4     0.0000000      1.301993   1.908912   6.800320   8.081606
-# Surveillance5     1.3019930      0.000000   2.087510   6.593177   5.495311
-# Activated1        1.9089124      2.087510   0.000000   2.014579   3.216930
-# Activated2        6.8003204      6.593177   2.014579   0.000000   1.950235
-# Activated3        8.0816058      5.495311   3.216930   1.950235   0.000000
-# Phagocytosis     15.4674423     12.337110   8.521408   5.171625   2.154714
-#               Phagocytosis
-# Homeostasis      26.392094
-# Surveillance1    29.687218
-# Surveillance2    25.688176
-# Surveillance3    19.513982
-# Surveillance4    15.467442
-# Surveillance5    12.337110
-# Activated1        8.521408
-# Activated2        5.171625
-# Activated3        2.154714
-# Phagocytosis      0.000000
    -

    Next, we perform a similar analysis on the perturbed Seurat assay -‘M4_down’

    -
    -# Set the default assay to the perturbed assay and prepare the data
-seurat_obj_perturbed <- seurat_obj # set up a new seurat object to avoid confusion
-
-DefaultAssay(seurat_obj_perturbed) <- "M4_down"
-
-seurat_obj_perturbed <- FindVariableFeatures(seurat_obj_perturbed, selection.method = "vst", nfeatures = 2000)
-seurat_obj_perturbed <- ScaleData(seurat_obj_perturbed)
    -

    Run PCA for the perturbed data and please remember to change the -‘reduction.name’

    -
    -seurat_obj_perturbed <- RunPCA(seurat_obj_perturbed, features = VariableFeatures(object = seurat_obj_perturbed), reduction.name = "M4_down_pca")
-
-seurat_obj_perturbed@reductions
    -
    -Output -
    > seurat_obj_perturbed@reductions
-$umap
-A dimensional reduction object with key UMAP_
- Number of dimensions: 2
- Projected dimensional reduction calculated:  FALSE
- Jackstraw run: FALSE
- Computed using assay: RNA
-
-$pca
-A dimensional reduction object with key PC_
- Number of dimensions: 50
- Projected dimensional reduction calculated:  FALSE
- Jackstraw run: FALSE
- Computed using assay: RNA
-
-$harmony
-A dimensional reduction object with key harmony_
- Number of dimensions: 50
- Projected dimensional reduction calculated:  TRUE
- Jackstraw run: FALSE
- Computed using assay: RNA
-
-$M4_down_pca
-A dimensional reduction object with key m4_down_pca_
- Number of dimensions: 50
- Projected dimensional reduction calculated:  FALSE
- Jackstraw run: FALSE
- Computed using assay: M4_down
-
    -

    Compute similarity distance for the perturbed data

    -
    -# ComputeDistance
-df_edist_perturbed <- ComputeDistance(seurat_obj_perturbed, groupby = 'functional_anno', reduction = 'M4_down_pca', method = "edist", verbose = TRUE)
-
-df_edist_perturbed
-
-
-# Rearrange rows and columns of df_edist_observed based on the custom order
-df_edist_perturbed_reordered <- df_edist_perturbed[custom_order, custom_order]
-
-# View the reordered dataframe
-df_edist_perturbed_reordered
    -
    -Output -
    > df_edist_perturbed
-              Phagocytosis Surveillance3 Surveillance2 Activated1 Surveillance1
-Phagocytosis     0.0000000     4.1887166     5.3468811  1.9114647      6.367927
-Surveillance3    4.1887166     0.0000000     0.3544971  0.8140641      0.702068
-Surveillance2    5.3468811     0.3544971     0.0000000  1.6971191      0.444851
-Activated1       1.9114647     0.8140641     1.6971191  0.0000000      2.354567
-Surveillance1    6.3679270     0.7020680     0.4448510  2.3545673      0.000000
-Surveillance4    3.3193247     0.2185912     0.8524824  0.3940233      1.108151
-Activated2       1.6043523     1.2675249     2.0982363  0.3861708      2.864140
-Surveillance5    2.9806149     0.5994561     1.5770397  0.4120399      1.629711
-Homeostasis      5.0451679     1.9123500     2.8172922  2.0402917      1.892958
-Activated3       0.9335111     1.9314490     3.0544205  0.5010339      3.670300
-              Surveillance4 Activated2 Surveillance5 Homeostasis Activated3
-Phagocytosis      3.3193247  1.6043523     2.9806149    5.045168  0.9335111
-Surveillance3     0.2185912  1.2675249     0.5994561    1.912350  1.9314490
-Surveillance2     0.8524824  2.0982363     1.5770397    2.817292  3.0544205
-Activated1        0.3940233  0.3861708     0.4120399    2.040292  0.5010339
-Surveillance1     1.1081507  2.8641397     1.6297111    1.892958  3.6702999
-Surveillance4     0.0000000  0.9458629     0.2065312    1.271465  1.2788325
-Activated2        0.9458629  0.0000000     0.8679631    2.958652  0.3551624
-Surveillance5     0.2065312  0.8679631     0.0000000    1.029319  0.9134280
-Homeostasis       1.2714649  2.9586525     1.0293188    0.000000  2.7488030
-Activated3        1.2788325  0.3551624     0.9134280    2.748803  0.0000000
-
    -

    We visualize the similarity distance matrices for both the original -and perturbed data using a heatmap. This will allow us to compare the -functional clusters’ similarity or difference before and after the -perturbation

    -
    -#---------------------------
-# NOTE plot heatmap
-
-library(ggplot2)
-library(reshape2)
-library(scales)
-library(patchwork)  # For arranging plots side by side
-
-
-# # Example usage with default color palette
-order_level <- custom_order
-
-# HeatmapDistance()
-ht <- HeatmapDistance(df_original = df_edist_original, df_perturbed = df_edist_perturbed, custom_order = order_level)
-
-ht
    -

    -

    We can see that the distance is smaller in the heatmap on the right, -which indicates that the activated cells have become transcriptomically -more similar to the homeostatic cells after our in-silco -perturbation.

    -
    -
    -

    Apply a knock-in -

    -

    In this step, we will simulate another in-silico perturbation with -ModulePerturbation to up-regulate the same co-expression -module, and we will visualize the resulting transition vector field with -PlotTransitionVectors. The only difference here is that we -set perturb_dir=1 for an up-regulation whereas previously -we had perturb_dir=-1 for a down-regulation.

    -
    -# apply the up-regulation of module M4
-perturbation_name <- 'M4_up'
-seurat_obj <- ModulePerturbation(
-    seurat_obj,
-    mod = "MG-M4",
-    perturb_dir = 1, # positive value indicates knock-in, all 
-    perturbation_name = perturbation_name,
-    graph = 'RNA_nn', 
-    n_hubs = 10, 
-)
-
-# plot the transition vectors
-p1 <- PlotTransitionVectors(
-    seurat_obj,
-    perturbation_name = "M4_up",
-    reduction = 'umap',
-    color.by='pseudotime',
-    grid_resolution = 25,
-    point_size=2 ) + 
-NoLegend() + 
-scale_color_gradientn(colors=viridis::plasma(256)) + 
-coord_equal() + 
-ggtitle("M4 knock-in")
-
-# plot the transition vectors
-p2 <- PlotTransitionVectors(
-    seurat_obj,
-    perturbation_name = "M4_down",
-    reduction = 'umap',
-    color.by='pseudotime',
-    grid_resolution = 25,
-    point_size=2 ) + 
-NoLegend() + 
-scale_color_gradientn(colors=viridis::plasma(256)) + 
-coord_equal() + 
-ggtitle("M4 knock-down")
-
-p1 | p2
    -

    -

    As expected, we can broadly see that the transition vectors indicate -opposite affects of the knock-in and knock-down on the predicted cell -states.

    -
    -
    -
    -

    Conclusion -

    -

    Here we demonstrated the basics of the -compact method to apply an in-silico -knockdown of a the microglial activation module M4. By down-regulating -this module, the method predicted that cells would shift to a more -homeostatic-like state.

    -
    -
    -
    - - - - -
    - - - - - - - diff --git a/docs/articles/index.html b/docs/articles/index.html index 0e3fd0f..89173a4 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -1,5 +1,5 @@ -Articles • compact +Articles • compact Skip to contents @@ -17,8 +17,7 @@ @@ -52,7 +49,7 @@

    All vignettes

    -

    Site built with pkgdown 2.1.1.

    +

    Site built with pkgdown 2.2.0.

    diff --git a/docs/articles/index.md b/docs/articles/index.md new file mode 100644 index 0000000..057a692 --- /dev/null +++ b/docs/articles/index.md @@ -0,0 +1,9 @@ +# Articles + +### All vignettes + +- [compact Quick + Start](https://smorabit.github.io/compact/articles/quickstart.md): + + A minimal end-to-end introduction to compact: from a preprocessed + Seurat object to a perturbation vector field in under 10 minutes. diff --git a/docs/articles/quickstart.html b/docs/articles/quickstart.html new file mode 100644 index 0000000..bf84adc --- /dev/null +++ b/docs/articles/quickstart.html @@ -0,0 +1,557 @@ + + + + + + +compact Quick Start • compact + + + + + + + + + + Skip to contents + + +
    + + + + +
    +
    + +

    compact Quick Start

    + + + +
    quickstart.Rmd
    +
    + + + +
    +

    Introduction +

    +

    compact (co-expression +module perturbation +analysis for cellular +transcriptomes) is an R package for in-silico gene +perturbation analysis in single-cell RNA-seq data. Given a co-expression +module, compact silences or activates the module’s hub genes, propagates +the signal through the gene co-expression network, and computes +cell-cell transition probabilities allowing you to ask: if this +module were knocked down, which cell states would cells move +toward?

    +

    This quickstart walks through the minimal workflow: load a dataset, +build a neighborhood graph, run one perturbation, and plot the resulting +vector field. All key parameters are annotated; everything else is left +at its default. For algorithmic depth, multiple modules, and downstream +Markov-chain analyses, see the Basics Simulation +vignette.

    +
    +

    Prerequisite: The dataset used here already has +co-expression modules computed with hdWGCNA. If you need to +run hdWGCNA on your own data first, start with the hdWGCNA +tutorial.

    +
    +
    +
    +
    +

    Section 1: Load Libraries and Data +

    + +
    Dataset generation: simulation and preprocessing steps (click to +expand) +

    The dataset was generated from scratch using +splatter to simulate a linear single-cell trajectory, +then processed through Seurat and hdWGCNA. These steps are provided here +for full reproducibility; they do not need to be re-run if you are +loading the pre-processed object below.

    +

    Step 1: Simulate a linear trajectory with +splatter

    +
    +library(splatter)
+library(scater)
+
+params <- newSplatParams()
+params <- setParam(params, "nGenes", 1500)          # 1,500 genes
+params <- setParam(params, "batchCells", 500)        # 500 cells
+params <- setParam(params, "lib.loc", 8)
+params <- setParam(params, "path.nonlinearProb", 0.1)
+
+sim_path <- splatSimulatePaths(
+  params,
+  group.prob = c(0.2, 0.2, 0.2, 0.2, 0.2),   # 5 equally-sized groups
+  de.prob    = 0.75,
+  de.facLoc  = 0.2,
+  path.from  = c(0, 1, 2, 3, 4),              # linear chain: 1 → 2 → 3 → 4 → 5
+  seed       = 12345,
+  verbose    = FALSE
+)
+
+# remove genes with no zero counts (fully non-sparse genes confound ZINB modeling)
+X <- counts(sim_path)
+exclude_genes <- names(which(rowMins(X) > 0))
+X <- X[setdiff(rownames(X), exclude_genes), ]
+
+seurat_obj <- CreateSeuratObject(
+  counts    = X,
+  meta.data = as.data.frame(colData(sim_path))
+)
    +

    Step 2: Standard Seurat preprocessing

    +
    +seurat_obj <- NormalizeData(seurat_obj)
+seurat_obj <- FindVariableFeatures(seurat_obj)
+seurat_obj <- ScaleData(seurat_obj)
+seurat_obj <- RunPCA(seurat_obj)
+seurat_obj <- FindNeighbors(seurat_obj, reduction = 'pca', annoy.metric = 'cosine')
+seurat_obj <- RunUMAP(seurat_obj, dims = 1:10)
    +

    Step 3: Co-expression network analysis with +hdWGCNA

    +
    +library(hdWGCNA)
+enableWGCNAThreads(nThreads = 8)
+
+# set up hdWGCNA using all genes
+seurat_obj <- SetupForWGCNA(
+  seurat_obj,
+  features   = rownames(seurat_obj),
+  wgcna_name = "linear"
+)
+
+# construct metacells within each group for robust co-expression estimation
+seurat_obj <- MetacellsByGroups(
+  seurat_obj      = seurat_obj,
+  group.by        = c("Group"),
+  reduction       = 'pca',
+  k               = 10,
+  max_shared      = 5,
+  ident.group     = 'Group',
+  target_metacells = 100,
+  min_cells       = 5
+)
+seurat_obj <- NormalizeMetacells(seurat_obj)
+
+# fit soft-power threshold and construct the co-expression network
+seurat_obj <- SetDatExpr(seurat_obj)
+seurat_obj <- TestSoftPowers(seurat_obj)
+seurat_obj <- ConstructNetwork(
+  seurat_obj,
+  soft_power   = 4,
+  tom_name     = 'sim_linear',
+  overwrite_tom = TRUE
+)
+
+# compute module eigengenes and hub gene connectivity scores
+seurat_obj <- ModuleEigengenes(seurat_obj)
+seurat_obj <- ModuleConnectivity(seurat_obj)
+
+saveRDS(seurat_obj, file = 'data/simulation_linear.rds')
    +

    Load the simulated linear-trajectory dataset. This object has been +processed through Seurat (normalization, PCA, UMAP) and hdWGCNA +(co-expression network, module identification). The co-expression +network (TOM) is stored as a separate file; update the path below if +your working directory differs.

    +
    +# TODO: update this block once this Seurat object is included in the package
+seurat_obj <- readRDS('/home/groups/singlecell/smorabito/analysis/COMPACT/data/simulation_linear.rds')
+
+# Update the TOM file path to match your local directory
+net <- GetNetworkData(seurat_obj)
+net$TOMFiles <- '/home/groups/singlecell/smorabito/analysis/COMPACT/TOM/sim_linear_TOM.rda'
+seurat_obj <- SetNetworkData(seurat_obj, net)
    +

    Visualize the dataset on the PCA embedding, colored by pseudotime +group. The five groups (Group1–Group5) represent ordered positions along +a single linear differentiation path simulated with +splatter.

    +
    +DimPlot(
+  seurat_obj,
+  reduction = 'pca',
+  group.by  = 'Group',
+  label     = TRUE,
+  pt.size   = 1.5
+) + NoLegend() + coord_equal()
    +

    +

    To see which region of the embedding each co-expression module is +active in, plot the module eigengenes (hMEs):

    +
    +plot_list <- ModuleFeaturePlot(
+  seurat_obj,
+  reduction = 'pca',
+  features  = 'MEs',
+  order     = TRUE
+)
+#> [1] "turquoise"
+#> [1] "brown"
+#> [1] "blue"
+wrap_plots(plot_list, ncol = 3)
    +

    +

    Note which module shows the strongest gradient from Group1 to Group5 +that is the module we will perturb.

    +
    +
    +
    +

    Section 2: Build the KNN Graph +

    +

    compact needs a cell-cell neighborhood graph to compute transition +probabilities: transition probabilities are only evaluated between +neighboring cells. Run FindNeighbors once; all subsequent +ModulePerturbation calls reuse the same graph.

    +
    +seurat_obj <- FindNeighbors(
+  seurat_obj,
+  reduction    = 'pca',
+  dims         = 1:10,
+  k.param      = 20,        
+  annoy.metric = 'cosine'  
+)
    +

    FindNeighbors stores two graphs in the Seurat object: +RNA_nn (KNN) and RNA_snn (shared nearest +neighbors). We use the SNN graph (RNA_snn) for +ModulePerturbation in the next section.

    +
    +

    Tip: If you ran FindNeighbors during +standard Seurat preprocessing, you can skip this step and pass the +existing graph directly to ModulePerturbation. Check which +graphs are available with names(seurat_obj@graphs).

    +
    +
    +
    +
    +

    Section 3: Run the Perturbation +

    +

    ModulePerturbation is the core function of compact. +Internally it:

    +
      +
    1. +ApplyPerturbation modifies expression +of the module’s top hub genes in the direction specified by +perturb_dir +
      2. +
    2. +ApplyPropagation diffuses that signal +through the gene co-expression network in log-normalized space
      3. +
    3. +PerturbationTransitions computes +cell-cell transition probabilities on the KNN graph
      4. +
    +

    Here we knock down the blue module (the module with +the highest pseudotime gradient) using perturb_dir = -1. +The result is stored as a new assay and a new transition probability +graph inside the Seurat object.

    +
    +seurat_obj <- ModulePerturbation(
+  seurat_obj,
+  mod              = 'blue',   # co-expression module to perturb
+  perturb_dir      = -1,            # negative = knock-down (ZINB mode)
+  perturbation_name = 'blue_down',
+  graph            = 'RNA_snn',     # neighborhood graph for transition probabilities
+  n_hubs           = 10,            # top 10 hub genes receive the primary perturbation
+  delta_scale      = 0.2,           # propagation dampening  keep ≤ 0.2 to avoid saturation
+  n_iters          = 3              # number of propagation iterations
+) 
+#> [1] "Applying primary in-silico perturbation to hub genes..."
+#>   |                                                          |                                                  |   0%  |                                                          |=====                                             |  10%  |                                                          |==========                                        |  20%  |                                                          |===============                                   |  30%  |                                                          |====================                              |  40%  |                                                          |=========================                         |  50%  |                                                          |==============================                    |  60%  |                                                          |===================================               |  70%  |                                                          |========================================          |  80%  |                                                          |=============================================     |  90%  |                                                          |==================================================| 100%
+#> [1] "Applying log-space signal propagation throughout co-expression network..."
+#> [1] "Computing cell-cell transition probabilities based on the perturbation..."
    +

    Expected runtime: 1–2 minutes on a laptop with a +500-cell dataset.

    +

    Key parameters:

    + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ParameterValueWhat it controls
    perturb_dir-1Direction: negative = knock-down, positive = knock-in, +0 = knock-out
    n_hubs10Number of hub genes to perturb directly; signal diffuses from these +to the rest
    delta_scale0.2Propagation dampening; lower values reduce risk of signal +saturation
    n_iters3Propagation steps through the network
    +
    +

    Troubleshooting graph name mismatch: If you see an +error like “Graph ‘RNA_snn’ not found”, the graph name in your +object may differ. Check with names(seurat_obj@graphs) and +update the graph= argument to match.

    +
    +
    +
    +
    +

    Section 4: Visualize the Vector Field +

    +

    PlotTransitionVectors projects the cell-cell transition +probabilities onto the 2D embedding as a vector field analogous to an +RNA velocity plot. Each arrow summarizes the weighted average direction +of transitions within that grid region.

    +
    +PlotTransitionVectors(
+  seurat_obj,
+  perturbation_name = 'blue_down',
+  reduction         = 'pca',
+  color.by          = 'Group',        # color cells by pseudotime group
+  plot_mode = 'cells',
+  arrow_scale       = 1.5,
+  arrow_size = 0.5           
+) + ggtitle('blue knock-down') + coord_equal()
    +

    +

    What to look for: In a linear trajectory, knocking +down the module with the highest pseudotime gradient should produce +arrows that point against the differentiation direction that +is, toward earlier groups (Group1 and Group2) rather than later ones +(Group4 and Group5). This confirms that the module drives late-stage +differentiation, and removing it would redirect cells toward an earlier +state.

    +
    +

    Tip: arrow_scale controls only the +visual arrow length it does not affect the underlying transition +probabilities. Adjust it freely until the arrows are legible. If arrows +appear too small, increase it; if they overlap, decrease it.

    +
    +
    +
    +
    +

    Section 5: Next Steps +

    +

    This quickstart covered the minimal compact workflow. The following +vignettes build on it:

    +
      +

    • Basics Simulation +(simulation_tutorial.Rmd): full pipeline on a branching +trajectory both perturbation directions, all modules, vector field +coherence scoring, and all four Markov-chain analyses +(PredictPerturbationTime, PredictAttractors, +PredictFates, PredictCommitment).

      • +

    • Basics Real Dataset +(basic_tutorial.Rmd): compact applied to a real Alzheimer’s +disease microglia atlas AlertSystemScore, +ComputeDistance, and FindShapKeyDriver for +interpretable driver gene prioritization.

      • +

    • Advanced Use Cases +(TF_tutorial.Rmd): TFPerturbation for +transcription factor–based perturbations using a regulatory network, and +CustomPerturbation for arbitrary gene sets.

      • +
    +
    +sessionInfo()
+#> R version 4.5.3 (2026-03-11)
+#> Platform: x86_64-conda-linux-gnu
+#> Running under: CentOS Linux 7 (Core)
+#> 
+#> Matrix products: default
+#> BLAS/LAPACK: /home/groups/singlecell/smorabito/.conda/envs/compact_fresh/lib/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
+#> 
+#> locale:
+#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
+#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
+#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
+#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
+#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
+#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+#> 
+#> time zone: Europe/Madrid
+#> tzcode source: system (glibc)
+#> 
+#> attached base packages:
+#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
+#> [8] base     
+#> 
+#> other attached packages:
+#>  [1] future_1.70.0               compact_0.0.3              
+#>  [3] hdWGCNA_0.4.11              enrichR_3.4                
+#>  [5] SummarizedExperiment_1.40.0 Biobase_2.70.0             
+#>  [7] MatrixGenerics_1.22.0       matrixStats_1.5.0          
+#>  [9] GenomicRanges_1.62.1        Seqinfo_1.0.0              
+#> [11] IRanges_2.44.0              S4Vectors_0.48.0           
+#> [13] BiocGenerics_0.56.0         generics_0.1.4             
+#> [15] GeneOverlap_1.46.0          UCell_2.14.0               
+#> [17] tidygraph_1.3.0             ggraph_2.2.2               
+#> [19] igraph_2.3.1                WGCNA_1.74                 
+#> [21] fastcluster_1.3.0           dynamicTreeCut_1.63-1      
+#> [23] ggrepel_0.9.8               harmony_2.0.2              
+#> [25] Rcpp_1.1.1-1.1              patchwork_1.3.2            
+#> [27] cowplot_1.2.0               lubridate_1.9.5            
+#> [29] forcats_1.0.1               stringr_1.6.0              
+#> [31] dplyr_1.2.1                 purrr_1.2.2                
+#> [33] readr_2.2.0                 tidyr_1.3.2                
+#> [35] tibble_3.3.1                ggplot2_4.0.3              
+#> [37] tidyverse_2.0.0             Seurat_5.5.0               
+#> [39] SeuratObject_5.4.0          sp_2.2-1                   
+#> 
+#> loaded via a namespace (and not attached):
+#>   [1] fs_2.1.0                    spatstat.sparse_3.1-0      
+#>   [3] bitops_1.0-9                httr_1.4.8                 
+#>   [5] RColorBrewer_1.1-3          doParallel_1.0.17          
+#>   [7] tools_4.5.3                 sctransform_0.4.3          
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+#>  [11] lazyeval_0.2.3              uwot_0.2.4                 
+#>  [13] withr_3.0.2                 gridExtra_2.3              
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+#>  [17] cli_3.6.6                   textshaping_1.0.5          
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+#>  [23] S7_0.2.2                    spatstat.data_3.1-9        
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+#>  [37] gtools_3.9.5                ica_1.0-3                  
+#>  [39] spatstat.random_3.4-5       car_3.1-5                  
+#>  [41] Matrix_1.7-5                abind_1.4-8                
+#>  [43] lifecycle_1.0.5             yaml_2.3.12                
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+#>  [53] pillar_1.11.1               knitr_1.51                 
+#>  [55] rjson_0.2.23                xgboost_3.2.0.1            
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+#>  [59] glue_1.8.1                  spatstat.univar_3.1-7      
+#>  [61] data.table_1.18.2.1         vctrs_0.7.3                
+#>  [63] png_0.1-9                   spam_2.11-3                
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+#>  [69] mime_0.13                   survival_3.8-6             
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+#> [105] base64enc_0.1-6             fastmap_1.2.0              
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+#> [109] BBmisc_1.13.1               shiny_1.13.0               
+#> [111] farver_2.1.2                jquerylib_0.1.4            
+#> [113] zoo_1.8-15                  jsonlite_2.0.0             
+#> [115] BiocParallel_1.44.0         magrittr_2.0.5             
+#> [117] Formula_1.2-5               dotCall64_1.2              
+#> [119] viridis_0.6.5               reticulate_1.46.0          
+#> [121] stringi_1.8.7               MASS_7.3-65                
+#> [123] plyr_1.8.9                  parallel_4.5.3             
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+#> [147] broom_1.0.12                xtable_1.8-8               
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+#> [153] ragg_1.5.2                  memoise_2.0.1              
+#> [155] cluster_2.1.8.2             timechange_0.4.0           
+#> [157] globals_0.19.1
    +
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    +
    + + + + +
    + + + + + + + diff --git a/docs/articles/quickstart.md b/docs/articles/quickstart.md new file mode 100644 index 0000000..ffac1b8 --- /dev/null +++ b/docs/articles/quickstart.md @@ -0,0 +1,462 @@ +# compact Quick Start + +## Introduction + +**`compact`** (**co**-expression **m**odule **p**erturbation +**a**nalysis for **c**ellular **t**ranscriptomes) is an R package for +in-silico gene perturbation analysis in single-cell RNA-seq data. Given +a co-expression module, compact silences or activates the module’s hub +genes, propagates the signal through the gene co-expression network, and +computes cell-cell transition probabilities allowing you to ask: *if +this module were knocked down, which cell states would cells move +toward?* + +This quickstart walks through the minimal workflow: load a dataset, +build a neighborhood graph, run one perturbation, and plot the resulting +vector field. All key parameters are annotated; everything else is left +at its default. For algorithmic depth, multiple modules, and downstream +Markov-chain analyses, see the **Basics Simulation** vignette. + +> **Prerequisite:** The dataset used here already has co-expression +> modules computed with `hdWGCNA`. If you need to run hdWGCNA on your +> own data first, start with the [hdWGCNA +> tutorial](https://smorabit.github.io/hdWGCNA/articles/basic_tutorial.html). + +------------------------------------------------------------------------ + +## Section 1: Load Libraries and Data + +``` r + +library(Seurat) +library(tidyverse) +library(cowplot) +library(patchwork) +library(hdWGCNA) +library(compact) + +theme_set(theme_cowplot()) +set.seed(12345) +``` + +**Dataset generation: simulation and preprocessing steps (click to +expand)** + +The dataset was generated from scratch using **splatter** to simulate a +linear single-cell trajectory, then processed through Seurat and +hdWGCNA. These steps are provided here for full reproducibility; they do +not need to be re-run if you are loading the pre-processed object below. + +**Step 1: Simulate a linear trajectory with splatter** + +``` r + +library(splatter) +library(scater) + +params <- newSplatParams() +params <- setParam(params, "nGenes", 1500) # 1,500 genes +params <- setParam(params, "batchCells", 500) # 500 cells +params <- setParam(params, "lib.loc", 8) +params <- setParam(params, "path.nonlinearProb", 0.1) + +sim_path <- splatSimulatePaths( + params, + group.prob = c(0.2, 0.2, 0.2, 0.2, 0.2), # 5 equally-sized groups + de.prob = 0.75, + de.facLoc = 0.2, + path.from = c(0, 1, 2, 3, 4), # linear chain: 1 → 2 → 3 → 4 → 5 + seed = 12345, + verbose = FALSE +) + +# remove genes with no zero counts (fully non-sparse genes confound ZINB modeling) +X <- counts(sim_path) +exclude_genes <- names(which(rowMins(X) > 0)) +X <- X[setdiff(rownames(X), exclude_genes), ] + +seurat_obj <- CreateSeuratObject( + counts = X, + meta.data = as.data.frame(colData(sim_path)) +) +``` + +**Step 2: Standard Seurat preprocessing** + +``` r + +seurat_obj <- NormalizeData(seurat_obj) +seurat_obj <- FindVariableFeatures(seurat_obj) +seurat_obj <- ScaleData(seurat_obj) +seurat_obj <- RunPCA(seurat_obj) +seurat_obj <- FindNeighbors(seurat_obj, reduction = 'pca', annoy.metric = 'cosine') +seurat_obj <- RunUMAP(seurat_obj, dims = 1:10) +``` + +**Step 3: Co-expression network analysis with hdWGCNA** + +``` r + +library(hdWGCNA) +enableWGCNAThreads(nThreads = 8) + +# set up hdWGCNA using all genes +seurat_obj <- SetupForWGCNA( + seurat_obj, + features = rownames(seurat_obj), + wgcna_name = "linear" +) + +# construct metacells within each group for robust co-expression estimation +seurat_obj <- MetacellsByGroups( + seurat_obj = seurat_obj, + group.by = c("Group"), + reduction = 'pca', + k = 10, + max_shared = 5, + ident.group = 'Group', + target_metacells = 100, + min_cells = 5 +) +seurat_obj <- NormalizeMetacells(seurat_obj) + +# fit soft-power threshold and construct the co-expression network +seurat_obj <- SetDatExpr(seurat_obj) +seurat_obj <- TestSoftPowers(seurat_obj) +seurat_obj <- ConstructNetwork( + seurat_obj, + soft_power = 4, + tom_name = 'sim_linear', + overwrite_tom = TRUE +) + +# compute module eigengenes and hub gene connectivity scores +seurat_obj <- ModuleEigengenes(seurat_obj) +seurat_obj <- ModuleConnectivity(seurat_obj) + +saveRDS(seurat_obj, file = 'data/simulation_linear.rds') +``` + +Load the simulated linear-trajectory dataset. This object has been +processed through Seurat (normalization, PCA, UMAP) and hdWGCNA +(co-expression network, module identification). The co-expression +network (TOM) is stored as a separate file; update the path below if +your working directory differs. + +``` r + +# TODO: update this block once this Seurat object is included in the package +seurat_obj <- readRDS('/home/groups/singlecell/smorabito/analysis/COMPACT/data/simulation_linear.rds') + +# Update the TOM file path to match your local directory +net <- GetNetworkData(seurat_obj) +net$TOMFiles <- '/home/groups/singlecell/smorabito/analysis/COMPACT/TOM/sim_linear_TOM.rda' +seurat_obj <- SetNetworkData(seurat_obj, net) +``` + +Visualize the dataset on the PCA embedding, colored by pseudotime group. +The five groups (Group1–Group5) represent ordered positions along a +single linear differentiation path simulated with **splatter**. + +``` r + +DimPlot( + seurat_obj, + reduction = 'pca', + group.by = 'Group', + label = TRUE, + pt.size = 1.5 +) + NoLegend() + coord_equal() +``` + +![](quickstart_files/figure-html/dimplot-1.png) + +To see which region of the embedding each co-expression module is active +in, plot the module eigengenes (hMEs): + +``` r + +plot_list <- ModuleFeaturePlot( + seurat_obj, + reduction = 'pca', + features = 'MEs', + order = TRUE +) +#> [1] "turquoise" +#> [1] "brown" +#> [1] "blue" +wrap_plots(plot_list, ncol = 3) +``` + +![](quickstart_files/figure-html/module-featureplot-1.png) + +Note which module shows the strongest gradient from Group1 to Group5 +that is the module we will perturb. + +------------------------------------------------------------------------ + +## Section 2: Build the KNN Graph + +compact needs a cell-cell neighborhood graph to compute transition +probabilities: transition probabilities are only evaluated between +neighboring cells. Run `FindNeighbors` once; all subsequent +`ModulePerturbation` calls reuse the same graph. + +``` r + +seurat_obj <- FindNeighbors( + seurat_obj, + reduction = 'pca', + dims = 1:10, + k.param = 20, + annoy.metric = 'cosine' +) +``` + +`FindNeighbors` stores two graphs in the Seurat object: `RNA_nn` (KNN) +and `RNA_snn` (shared nearest neighbors). We use the SNN graph +(`RNA_snn`) for `ModulePerturbation` in the next section. + +> **Tip:** If you ran `FindNeighbors` during standard Seurat +> preprocessing, you can skip this step and pass the existing graph +> directly to `ModulePerturbation`. Check which graphs are available +> with `names(seurat_obj@graphs)`. + +------------------------------------------------------------------------ + +## Section 3: Run the Perturbation + +`ModulePerturbation` is the core function of compact. Internally it: + +1. **`ApplyPerturbation`** modifies expression of the module’s top hub + genes in the direction specified by `perturb_dir` +2. **`ApplyPropagation`** diffuses that signal through the gene + co-expression network in log-normalized space +3. **`PerturbationTransitions`** computes cell-cell transition + probabilities on the KNN graph + +Here we knock down the **blue** module (the module with the highest +pseudotime gradient) using `perturb_dir = -1`. The result is stored as a +new assay and a new transition probability graph inside the Seurat +object. + +``` r + +seurat_obj <- ModulePerturbation( + seurat_obj, + mod = 'blue', # co-expression module to perturb + perturb_dir = -1, # negative = knock-down (ZINB mode) + perturbation_name = 'blue_down', + graph = 'RNA_snn', # neighborhood graph for transition probabilities + n_hubs = 10, # top 10 hub genes receive the primary perturbation + delta_scale = 0.2, # propagation dampening keep ≤ 0.2 to avoid saturation + n_iters = 3 # number of propagation iterations +) +#> [1] "Applying primary in-silico perturbation to hub genes..." +#> | | | 0% | |===== | 10% | |========== | 20% | |=============== | 30% | |==================== | 40% | |========================= | 50% | |============================== | 60% | |=================================== | 70% | |======================================== | 80% | |============================================= | 90% | |==================================================| 100% +#> [1] "Applying log-space signal propagation throughout co-expression network..." +#> [1] "Computing cell-cell transition probabilities based on the perturbation..." +``` + +**Expected runtime:** 1–2 minutes on a laptop with a 500-cell dataset. + +**Key parameters:** + +| Parameter | Value | What it controls | +|----|----|----| +| `perturb_dir` | `-1` | Direction: negative = knock-down, positive = knock-in, `0` = knock-out | +| `n_hubs` | `10` | Number of hub genes to perturb directly; signal diffuses from these to the rest | +| `delta_scale` | `0.2` | Propagation dampening; lower values reduce risk of signal saturation | +| `n_iters` | `3` | Propagation steps through the network | + +> **Troubleshooting graph name mismatch:** If you see an error like +> *“Graph ‘RNA_snn’ not found”*, the graph name in your object may +> differ. Check with `names(seurat_obj@graphs)` and update the `graph=` +> argument to match. + +------------------------------------------------------------------------ + +## Section 4: Visualize the Vector Field + +`PlotTransitionVectors` projects the cell-cell transition probabilities +onto the 2D embedding as a vector field analogous to an RNA velocity +plot. Each arrow summarizes the weighted average direction of +transitions within that grid region. + +``` r + +PlotTransitionVectors( + seurat_obj, + perturbation_name = 'blue_down', + reduction = 'pca', + color.by = 'Group', # color cells by pseudotime group + plot_mode = 'cells', + arrow_scale = 1.5, + arrow_size = 0.5 +) + ggtitle('blue knock-down') + coord_equal() +``` + +![](quickstart_files/figure-html/plot-vectors-1.png) + +**What to look for:** In a linear trajectory, knocking down the module +with the highest pseudotime gradient should produce arrows that point +*against* the differentiation direction that is, toward earlier groups +(Group1 and Group2) rather than later ones (Group4 and Group5). This +confirms that the module drives late-stage differentiation, and removing +it would redirect cells toward an earlier state. + +> **Tip:** `arrow_scale` controls only the visual arrow length it does +> not affect the underlying transition probabilities. Adjust it freely +> until the arrows are legible. If arrows appear too small, increase it; +> if they overlap, decrease it. + +------------------------------------------------------------------------ + +## Section 5: Next Steps + +This quickstart covered the minimal compact workflow. The following +vignettes build on it: + +- **Basics Simulation** (`simulation_tutorial.Rmd`): full pipeline on a + branching trajectory both perturbation directions, all modules, vector + field coherence scoring, and all four Markov-chain analyses + (`PredictPerturbationTime`, `PredictAttractors`, `PredictFates`, + `PredictCommitment`). + +- **Basics Real Dataset** (`basic_tutorial.Rmd`): compact applied to a + real Alzheimer’s disease microglia atlas `AlertSystemScore`, + `ComputeDistance`, and `FindShapKeyDriver` for interpretable driver + gene prioritization. + +- **Advanced Use Cases** (`TF_tutorial.Rmd`): `TFPerturbation` for + transcription factor–based perturbations using a regulatory network, + and `CustomPerturbation` for arbitrary gene sets. + +``` r + +sessionInfo() +#> R version 4.5.3 (2026-03-11) +#> Platform: x86_64-conda-linux-gnu +#> Running under: CentOS Linux 7 (Core) +#> +#> Matrix products: default +#> BLAS/LAPACK: /home/groups/singlecell/smorabito/.conda/envs/compact_fresh/lib/libopenblasp-r0.3.32.so; LAPACK version 3.12.0 +#> +#> locale: +#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C +#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 +#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 +#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C +#> [9] LC_ADDRESS=C LC_TELEPHONE=C +#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C +#> +#> time zone: Europe/Madrid +#> tzcode source: system (glibc) +#> +#> attached base packages: +#> [1] stats4 stats graphics grDevices utils datasets methods +#> [8] base +#> +#> other attached packages: +#> [1] future_1.70.0 compact_0.0.3 +#> [3] hdWGCNA_0.4.11 enrichR_3.4 +#> [5] SummarizedExperiment_1.40.0 Biobase_2.70.0 +#> [7] MatrixGenerics_1.22.0 matrixStats_1.5.0 +#> [9] GenomicRanges_1.62.1 Seqinfo_1.0.0 +#> [11] IRanges_2.44.0 S4Vectors_0.48.0 +#> [13] BiocGenerics_0.56.0 generics_0.1.4 +#> [15] GeneOverlap_1.46.0 UCell_2.14.0 +#> [17] tidygraph_1.3.0 ggraph_2.2.2 +#> [19] igraph_2.3.1 WGCNA_1.74 +#> [21] fastcluster_1.3.0 dynamicTreeCut_1.63-1 +#> [23] ggrepel_0.9.8 harmony_2.0.2 +#> [25] Rcpp_1.1.1-1.1 patchwork_1.3.2 +#> [27] cowplot_1.2.0 lubridate_1.9.5 +#> [29] forcats_1.0.1 stringr_1.6.0 +#> [31] dplyr_1.2.1 purrr_1.2.2 +#> [33] readr_2.2.0 tidyr_1.3.2 +#> [35] tibble_3.3.1 ggplot2_4.0.3 +#> [37] tidyverse_2.0.0 Seurat_5.5.0 +#> [39] SeuratObject_5.4.0 sp_2.2-1 +#> +#> loaded via a namespace (and not attached): +#> [1] fs_2.1.0 spatstat.sparse_3.1-0 +#> [3] bitops_1.0-9 httr_1.4.8 +#> [5] RColorBrewer_1.1-3 doParallel_1.0.17 +#> [7] tools_4.5.3 sctransform_0.4.3 +#> [9] backports_1.5.1 R6_2.6.1 +#> [11] lazyeval_0.2.3 uwot_0.2.4 +#> [13] withr_3.0.2 gridExtra_2.3 +#> [15] preprocessCore_1.72.0 progressr_0.19.0 +#> [17] cli_3.6.6 textshaping_1.0.5 +#> [19] spatstat.explore_3.8-0 fastDummies_1.7.6 +#> [21] labeling_0.4.3 sass_0.4.10 +#> [23] S7_0.2.2 spatstat.data_3.1-9 +#> [25] proxy_0.4-29 ggridges_0.5.7 +#> [27] pbapply_1.7-4 pkgdown_2.2.0 +#> [29] systemfonts_1.3.2 foreign_0.8-91 +#> [31] pscl_1.5.9 parallelly_1.47.0 +#> [33] WriteXLS_6.8.0 VGAM_1.1-14 +#> [35] rstudioapi_0.18.0 impute_1.84.0 +#> [37] gtools_3.9.5 ica_1.0-3 +#> [39] spatstat.random_3.4-5 car_3.1-5 +#> [41] Matrix_1.7-5 abind_1.4-8 +#> [43] lifecycle_1.0.5 yaml_2.3.12 +#> [45] carData_3.0-6 gplots_3.3.0 +#> [47] SparseArray_1.10.8 Rtsne_0.17 +#> [49] grid_4.5.3 promises_1.5.0 +#> [51] miniUI_0.1.2 lattice_0.22-9 +#> [53] pillar_1.11.1 knitr_1.51 +#> [55] rjson_0.2.23 xgboost_3.2.0.1 +#> [57] future.apply_1.20.2 codetools_0.2-20 +#> [59] glue_1.8.1 spatstat.univar_3.1-7 +#> [61] data.table_1.18.2.1 vctrs_0.7.3 +#> [63] png_0.1-9 spam_2.11-3 +#> [65] gtable_0.3.6 cachem_1.1.0 +#> [67] xfun_0.57 S4Arrays_1.10.1 +#> [69] mime_0.13 survival_3.8-6 +#> [71] SingleCellExperiment_1.32.0 iterators_1.0.14 +#> [73] fitdistrplus_1.2-6 ROCR_1.0-12 +#> [75] nlme_3.1-169 SHAPforxgboost_0.2.0 +#> [77] RcppAnnoy_0.0.23 bslib_0.10.0 +#> [79] irlba_2.3.7 KernSmooth_2.23-26 +#> [81] otel_0.2.0 rpart_4.1.27 +#> [83] colorspace_2.1-2 Hmisc_5.2-5 +#> [85] nnet_7.3-20 tidyselect_1.2.1 +#> [87] compiler_4.5.3 curl_7.1.0 +#> [89] htmlTable_2.5.0 BiocNeighbors_2.4.0 +#> [91] desc_1.4.3 DelayedArray_0.36.0 +#> [93] plotly_4.12.0 checkmate_2.3.4 +#> [95] scales_1.4.0 caTools_1.18.3 +#> [97] lmtest_0.9-40 digest_0.6.39 +#> [99] goftest_1.2-3 spatstat.utils_3.2-2 +#> [101] rmarkdown_2.31 XVector_0.50.0 +#> [103] htmltools_0.5.9 pkgconfig_2.0.3 +#> [105] base64enc_0.1-6 fastmap_1.2.0 +#> [107] rlang_1.2.0 htmlwidgets_1.6.4 +#> [109] BBmisc_1.13.1 shiny_1.13.0 +#> [111] farver_2.1.2 jquerylib_0.1.4 +#> [113] zoo_1.8-15 jsonlite_2.0.0 +#> [115] BiocParallel_1.44.0 magrittr_2.0.5 +#> [117] Formula_1.2-5 dotCall64_1.2 +#> [119] viridis_0.6.5 reticulate_1.46.0 +#> [121] stringi_1.8.7 MASS_7.3-65 +#> [123] plyr_1.8.9 parallel_4.5.3 +#> [125] listenv_0.10.1 deldir_2.0-4 +#> [127] graphlayouts_1.2.3 splines_4.5.3 +#> [129] tensor_1.5.1 hms_1.1.4 +#> [131] rdist_0.0.5 ggpubr_0.6.3 +#> [133] spatstat.geom_3.7-3 ggsignif_0.6.4 +#> [135] RcppHNSW_0.6.0 reshape2_1.4.5 +#> [137] evaluate_1.0.5 tester_0.3.0 +#> [139] tzdb_0.5.0 foreach_1.5.2 +#> [141] tweenr_2.0.3 httpuv_1.6.17 +#> [143] RANN_2.6.2 polyclip_1.10-7 +#> [145] scattermore_1.2 ggforce_0.5.0 +#> [147] broom_1.0.12 xtable_1.8-8 +#> [149] RSpectra_0.16-2 rstatix_0.7.3 +#> [151] later_1.4.8 viridisLite_0.4.3 +#> [153] ragg_1.5.2 memoise_2.0.1 +#> [155] cluster_2.1.8.2 timechange_0.4.0 +#> [157] globals_0.19.1 +``` diff --git a/docs/articles/quickstart_files/figure-html/dimplot-1.png b/docs/articles/quickstart_files/figure-html/dimplot-1.png new file mode 100644 index 0000000000000000000000000000000000000000..9594088609aa12687f5f270661926b1ede89d277 GIT binary patch literal 120565 zcmeFXWmJ@H_y;(o2m&G@f`CecbV(y6LpRb5!oVQiWq`EIP(um~Al)q`0@5%DNUP+~ zF?8*NzW?3*uxHPnvuAg|?3^?GJokOwSN^Vu&{TgxOh`ir0)dFZO7hwu5N-$vgwsxd z5B#ELfZYJR;ajP^kO$s@$EjR%Fz|N!wUVJb2t*ft^Tdi<-m?XP9)ZB}&vkv$H|Kl< 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    -

    Site built with pkgdown 2.1.1.

    +

    Site built with pkgdown 2.2.0.

    diff --git a/docs/authors.md b/docs/authors.md new file mode 100644 index 0000000..f5372c0 --- /dev/null +++ b/docs/authors.md @@ -0,0 +1,24 @@ +# Authors and Citation + +## Authors + +- **[Sam Morabito](https://smorabit.github.io)**. Author, maintainer. + [](https://orcid.org/0000-0002-7768-4856) + +- **[Zechuan Shi](https://github.com/rootze)**. Author. + [](https://orcid.org/0000-0002-2844-5816) + +- **[Swarup Lab](https://swaruplab.bio.uci.edu/)**. Funder. + +## Citation + +Morabito S, Shi Z (2026). *compact: compact*. R package version 0.0.3, +. + + @Manual{, + title = {compact: compact}, + author = {Sam Morabito and Zechuan Shi}, + year = {2026}, + note = {R package version 0.0.3}, + url = {https://smorabit.github.io/COMPACT/}, + } diff --git a/docs/deps/bootstrap-5.3.1/bootstrap.min.css b/docs/deps/bootstrap-5.3.1/bootstrap.min.css index 44cc9d1..ef653fa 100644 --- a/docs/deps/bootstrap-5.3.1/bootstrap.min.css +++ b/docs/deps/bootstrap-5.3.1/bootstrap.min.css @@ -2,4 +2,4 @@ * Bootstrap v5.3.1 (https://getbootstrap.com/) * Copyright 2011-2023 The Bootstrap Authors * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) - */:root,[data-bs-theme="light"]{--bs-blue: #2c3e50;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #e83e8c;--bs-red: #e74c3c;--bs-orange: #fd7e14;--bs-yellow: #f39c12;--bs-green: #18bc9c;--bs-teal: #20c997;--bs-cyan: #3498db;--bs-black: #000;--bs-white: #fff;--bs-gray: #95a5a6;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: 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var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-link-hover-bg: rgba(255,255,255,0.15);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #2c3e50;--bs-dropdown-link-disabled-color: #b4bcc2;--bs-dropdown-header-color: #b4bcc2}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width) * -1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n + 3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width) * -1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 2rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: #95a5a6;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(44,62,80,0.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: var(--bs-border-width);--bs-nav-tabs-border-color: #ecf0f1;--bs-nav-tabs-border-radius: var(--bs-border-radius);--bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) #ecf0f1;--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * var(--bs-nav-tabs-border-width));border-top-left-radius:0;border-top-right-radius:0}.nav-pills{--bs-nav-pills-border-radius: var(--bs-border-radius);--bs-nav-pills-link-active-color: #fff;--bs-nav-pills-link-active-bg: #2c3e50}.nav-pills .nav-link{border-radius:var(--bs-nav-pills-border-radius)}.nav-pills .nav-link.active,.nav-pills .show>.nav-link{color:var(--bs-nav-pills-link-active-color);background-color:var(--bs-nav-pills-link-active-bg)}.nav-underline{--bs-nav-underline-gap: 1rem;--bs-nav-underline-border-width: .125rem;--bs-nav-underline-link-active-color: var(--bs-emphasis-color);gap:var(--bs-nav-underline-gap)}.nav-underline .nav-link{padding-right:0;padding-left:0;border-bottom:var(--bs-nav-underline-border-width) solid transparent}.nav-underline .nav-link:hover,.nav-underline .nav-link:focus{border-bottom-color:currentcolor}.nav-underline .nav-link.active,.nav-underline .show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: 1rem;--bs-navbar-color: #fff;--bs-navbar-hover-color: #18bc9c;--bs-navbar-disabled-color: rgba(255,255,255,0.3);--bs-navbar-active-color: #18bc9c;--bs-navbar-brand-padding-y: .3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: #fff;--bs-navbar-brand-hover-color: #fff;--bs-navbar-nav-link-padding-x: .5rem;--bs-navbar-toggler-padding-y: .25rem;--bs-navbar-toggler-padding-x: .75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(255,255,255,0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: .25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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var(--bs-navbar-hover-color);--bs-nav-link-disabled-color: var(--bs-navbar-disabled-color);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;list-style:none}.navbar-nav .nav-link.active,.navbar-nav .nav-link.show{color:var(--bs-navbar-active-color)}.navbar-nav .dropdown-menu{position:static}.navbar-text{padding-top:.5rem;padding-bottom:.5rem;color:var(--bs-navbar-color)}.navbar-text a,.navbar-text a:hover,.navbar-text a:focus{color:var(--bs-navbar-active-color)}.navbar-collapse{flex-basis:100%;-webkit-flex-basis:100%;flex-grow:1;-webkit-flex-grow:1;align-items:center;-webkit-align-items:center}.navbar-toggler{padding:var(--bs-navbar-toggler-padding-y) var(--bs-navbar-toggler-padding-x);font-size:var(--bs-navbar-toggler-font-size);line-height:1;color:var(--bs-navbar-color);background-color:transparent;border:var(--bs-border-width) solid 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.navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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!important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: .75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid 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ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23121920'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #969fa8;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media 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reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: .75rem;--bs-breadcrumb-padding-y: .375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: .25rem;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: rgba(0,0,0,0);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #0f7864;--bs-pagination-hover-border-color: rgba(0,0,0,0);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0f7864;--bs-pagination-active-border-color: rgba(0,0,0,0);--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: #3be6c4;--bs-pagination-disabled-border-color: rgba(0,0,0,0);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0 * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: #2c3e50;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: .4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: .25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:transparent var(--bs-btn-close-bg) center/1em auto 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var(--bs-border-color-translucent);--bs-toast-border-radius: var(--bs-border-radius);--bs-toast-box-shadow: var(--bs-box-shadow);--bs-toast-header-color: var(--bs-secondary-color);--bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color: var(--bs-border-color-translucent);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-.5 * var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: .5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid 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.modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media (max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media (max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media (max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media (max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: .5rem;--bs-tooltip-padding-y: .25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:.875rem;--bs-tooltip-color: var(--bs-body-bg);--bs-tooltip-bg: var(--bs-emphasis-color);--bs-tooltip-border-radius: var(--bs-border-radius);--bs-tooltip-opacity: .9;--bs-tooltip-arrow-width: .8rem;--bs-tooltip-arrow-height: .4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:var(--bs-font-sans-serif);font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:transparent;border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="top"] .tooltip-arrow{bottom:calc(-1 * var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="top"] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) 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var(--bs-border-width);--bs-popover-border-color: var(--bs-border-color-translucent);--bs-popover-border-radius: var(--bs-border-radius-lg);--bs-popover-inner-border-radius: calc(var(--bs-border-radius-lg) - var(--bs-border-width));--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: .5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: var(--bs-secondary-bg);--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: var(--bs-body-color);--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: .5rem;--bs-popover-arrow-border: 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auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width) * -1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n + 3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width) * -1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 2rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: #95a5a6;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) 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var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * 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var(--bs-navbar-hover-color);--bs-nav-link-disabled-color: var(--bs-navbar-disabled-color);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;list-style:none}.navbar-nav .nav-link.active,.navbar-nav .nav-link.show{color:var(--bs-navbar-active-color)}.navbar-nav .dropdown-menu{position:static}.navbar-text{padding-top:.5rem;padding-bottom:.5rem;color:var(--bs-navbar-color)}.navbar-text a,.navbar-text a:hover,.navbar-text a:focus{color:var(--bs-navbar-active-color)}.navbar-collapse{flex-basis:100%;-webkit-flex-basis:100%;flex-grow:1;-webkit-flex-grow:1;align-items:center;-webkit-align-items:center}.navbar-toggler{padding:var(--bs-navbar-toggler-padding-y) var(--bs-navbar-toggler-padding-x);font-size:var(--bs-navbar-toggler-font-size);line-height:1;color:var(--bs-navbar-color);background-color:transparent;border:var(--bs-border-width) solid 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}:where(.navbar[data-bs-theme="dark"] .navbar-toggler-icon){--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme="dark"] :where(.navbar:not([data-bs-theme="light"]) .navbar-toggler-icon){--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.navbar[data-bs-theme="light"]{--bs-navbar-color: #fff;--bs-navbar-hover-color: #18bc9c;--bs-navbar-disabled-color: rgba(255,255,255,0.3);--bs-navbar-active-color: #18bc9c;--bs-navbar-brand-color: #fff;--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(255,255,255,0.15)}.navbar[data-bs-theme="light"] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: .5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: .75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid 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var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius)}.card-header-tabs{margin-right:calc(-.5 * var(--bs-card-cap-padding-x));margin-bottom:calc(-1 * 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url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23121920'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #969fa8;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media (prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: .75rem;--bs-breadcrumb-padding-y: .375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: .25rem;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: rgba(0,0,0,0);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #0f7864;--bs-pagination-hover-border-color: rgba(0,0,0,0);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0f7864;--bs-pagination-active-border-color: rgba(0,0,0,0);--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: #3be6c4;--bs-pagination-disabled-border-color: rgba(0,0,0,0);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0 * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: #2c3e50;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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