Dev

.github/workflows/pkgdown.yaml

@@ -37,7 +37,7 @@ jobs:
           needs: website
 
       - name: Build site
-        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE, run_dont_run = FALSE)
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE, run_dont_run = TRUE)
         shell: Rscript {0}
 
       - name: Deploy to GitHub pages 🚀

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: immunarch
 Type: Package
 Title: Multi-Modal Immune Repertoire Analytics for Immunotherapy and Vaccine Design in R
-Version: 0.10.2
+Version: 0.10.3
 Authors@R: c(
     person("Vadim I.", "Nazarov", , "support@immunomind.com", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-3659-2709")),

R/v0_vis.R

@@ -169,90 +169,36 @@ theme_cleveland2 <- function(rotate = TRUE) {
 #'
 #' @description
 #'
-#' `r lifecycle::badge('deprecated')`
+#' `r lifecycle::badge('experimental')`
+#'
+#' **`vis()` is a lightweight, quick-look plotting helper.**
+#' It's designed to help you **visualise results fast** with sensible defaults.
+#' It automatically detects the input type and chooses an appropriate visualisation
+#' (e.g., output from `airr_stats_genes()` is recognised as gene usage values and plotted
+#' without extra arguments).
 #'
-#' Output from every function in immunarch can be visualised with a
-#' single function - `vis`. The `vis` automatically detects
-#' the type of the data and draws a proper visualisation. For example, output
-#' from the `repOverlap` function will be identified as repertoire overlap values
-#' and respective visualisation will be chosen without any additional arguments.
-#' See "Details" for the list of available visualisations.
+#' **vis() is not intended for publication-quality figures.**
+#' For serious, highly customised, or publication-ready plots, I recommend
+#' building your graphics directly with **ggplot2**.
 #'
-#' @param .data Pass the output from any immunarch analysis tool to `vis()`.
+#' @param .data The output from any immunarch analysis function. The function automatically resolves to a correct visualisation.
 #' @param ... Any other arguments, see the "Details" section for specific visualisation functions.
 #'
 #' @details
-#' List of available visualisations for different kinds of data.
-#'
-#' Basic analysis:
-#'
-#' - Exploratory analysis results (from [repExplore]) - see [vis.immunr_exp_vol];
-#'
-#' - Clonality statistics (from [repClonality]) - see [vis.immunr_homeo].
-#'
-#' Overlaps and public clonotypes:
-#'
-#' - Overlaps (from [repOverlap]) using heatmaps, circos plots, polar area plots - see [vis.immunr_ov_matrix];
-#'
-#' -  Overlap clustering (from [repOverlapAnalysis]) - see [vis.immunr_hclust];
-#'
-#' - Repertoire incremental overlaps (from [repOverlap]) - see [vis.immunr_inc_overlap];
-#'
-#' - Public repertoire abundance (from [pubRep]) - vis [vis.immunr_public_repertoire].
-#'
-#' Gene usage:
-#'
-#' - Gene usage statistics (from [geneUsage]) using bar plots, box plots - see [vis.immunr_gene_usage];
-#'
-#' - Gene usage distances (from [geneUsageAnalysis]) using heatmaps, circos plots, polar area plots - see [vis.immunr_ov_matrix];
-#'
-#' - Gene usage clustering (from [geneUsageAnalysis]) - see [vis.immunr_hclust].
-#'
-#' Diversity estimation:
-#'
-#' - Diversity estimations (from [repDiversity]) - see [vis.immunr_chao1].
-#'
-#' BCR analysis:
-#'
-#' - Clonal tree (from [repClonalFamily]) - see [vis.clonal_family] and [vis.clonal_family_tree].
-#'
-#' Advanced analysis:
-#'
-#' - Repertoire dynamics (from [trackClonotypes]) - see [vis.immunr_dynamics];
-#'
-#' - Sequence logo plots of amino acid distributions (from [kmer_profile]) - see [vis_seqlogo];
-#'
-#' - Kmers distributions (from [getKmers]) - see [vis.immunr_kmer_table];
-#'
-#' - Mutation networks (from mutationNetwork) - Work In Progress on vis.immunr_mutation_network;
-#'
-#' - CDR3 amino acid properties, e.g., biophysical (from cdrProp) - Work In Progress on vis.immunr_cdr_prop.
-#'
-#' Additionaly, we provide a wrapper functions for visualisations of common data types:
-#'
-#' - Any data frames or matrices using heatmaps - see [vis_heatmap] and [vis_heatmap2];
-#'
-#' - Any data frames or matrices using circos plots - see [vis_circos].
+#' List of available visualisations for different kinds of data - will be available soon.
 #'
 #' @return
-#' A ggplot2, pheatmap or circlize object.
+#' A ggplot2 object.
 #'
 #' @seealso [fixVis] for precise manipulation of plots.
 #'
 #' @examples
 #' \dontrun{
-#' # Load the test data
-#' data(immdata)
-#'
-#' # Compute and visualise:
-#' ov <- repOverlap(immdata$data)
-#' vis(ov)
-#'
-#' gu <- geneUsage(immdata$data)
-#' vis(gu)
-#'
-#' dv <- repDiversity(immdata$data)
-#' vis(dv)
+#' immdata <- get_test_idata() |> agg_repertoires("Therapy")
+#' airr_stats_genes(immdata, gene_col = "v_call") |> vis()
+#' airr_public_jaccard(immdata) |> vis()
+#' airr_diversity_pielou(immdata) |> vis()
+#' airr_diversity_chao1(immdata) |> vis()
 #' }
 #' @export
 vis <- function(.data, ...) {

R/v1_aaa_commons_vis.R

@@ -1,24 +1,3 @@
-#
-# 1) Look at this for examples: https://chatgpt.com/c/68eacc4b-785c-832a-88f5-b94086ad7f26
-#
-# 2) Register in the registry. Registry should check if all visualisations are assigned to existing (!) classes.
-# vis_airr_diversity_chao1 <- ...
-# It is probably impossible to do...
-# Should use https://stat.ethz.ch/R-manual/R-devel/RHOME/library/base/html/S3method.html
-#
-# 3) I need registry to control the class system in one place. One for assigning classes following a specific schema, the other one is for parsing classes.
-# I probably need to write a short manual on the logic of this. And on the structure of the code. And on the phiolosophy / architectural design decisions. Same for immundata.
-#
-# 4) Key question: can I use different names? https://stackoverflow.com/questions/61482561/whats-the-preferred-means-for-defining-an-s3-method-in-an-r-package-without-int/61483612#61483612
-# https://vctrs.r-lib.org/reference/s3_register.html
-#
-# 5) [!!!] I still need some name for Roxygen to @inheritParams. There is no way around that.
-# opt.1 - dynamically register S3 stuff, one big vis() or some weird names for docs
-# opt.2 - pre-defined classes, vis() is constructed via @inheritParams
-# Or...
-# - https://github.com/r-lib/roxygen2/issues/1159
-# - https://github.com/rstudio/renv/blob/4a8bcb4605f085fbea5f29a76ad9a291ac2bd363/R/roxygen.R#L2-L26
-
 #' @keywords internal
 make_fixed_col_plot <- function(x_col, y_col, title, xlab, ylab) {
   checkmate::assert_string(x_col)

R/v1_migration_updates.R

@@ -1,6 +1,6 @@
 #' @keywords internal
 immunarch_v1_update_oct_2025 <- function() {
-  cli::cli_h1("{cli::col_green('immunarch')} {cli::col_yellow('0.10.0')} -- Critical Pre-release Notice")
+  cli::cli_h1("{cli::col_green('immunarch')} {cli::col_yellow('0.10.3')} -- Critical Pre-release Notice")
 
   cli::cli_alert_warning("Update #1 [Oct 2025] -- Major changes are coming in {cli::col_green('immunarch')} {cli::col_yellow('1.0.0')}!")
   cli::cli_text(cli::col_yellow(cli::spark_line(runif(110, 0, 1))))

R/v1_public_airr.R

@@ -21,7 +21,9 @@
 #' # Change this only if you know what you're doing (e.g., multi-user machines, shared CI/servers).
 #' db_exec("SET threads TO 1")
 #' # Load data
+#' \dontrun{
 #' immdata <- get_test_idata() |> agg_repertoires("Therapy")
+#' }
 #'
 #' @name airr_public
 #' @concept Public indices