README.Rmd

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# SecAct: Secreted Protein Activity Inference <img src="man/figures/sticker.png" align="right" alt="" width="120" />

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SecAct is an R package designed for inferring the intercellular signaling activity of secreted proteins from gene expression profiles. Users can input multiple modalities of expression data, including spatial, single-cell, or bulk transcriptomics data. The outputs are the inferred <b>signaling activities of 1,170 secreted proteins</b> for each spatial spot, individual cell, or sample, depending on the input data type. Based on the inferred activities, SecAct provides multiple downstream application modules. For <b>spatial</b> data, SecAct can infer the signaling pattern and signaling velocity for secreted proteins. For <b>single-cell</b> data, SecAct can infer the intercellular communication network and signaling flow from source cells to receiver cells. For <b>bulk</b> data, SecAct can infer secreted protein risk scores for a large cohort linked to clinical data, and can infer secreted protein activities that are differentially regulated between two phenotypes. Check the tutorials below for details on how to use the package.

<p align="center"><img src="man/figures/workflow.png" width="100%"/></p>


## Installation

To install `SecAct` R package, we recommend using `devtools`:

``` r
# install.packages("devtools")
devtools::install_github("data2intelligence/SecAct")
```

Or user can install `SecAct` R package from the source code. Click <a href="https://api.github.com/repos/data2intelligence/SecAct/tarball/HEAD" target="_blank">here</a> to download it.

``` r
# install SecAct dependencies
remotes::install_deps("Path_to_the_source_code", force = TRUE)

# install SecAct R package
install.packages("Path_to_the_source_code", repos = NULL, type="source")
``` 

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#### 🔴 Optional accelerators

After installing the `SecAct` R package, you can complete all analyses in the tutorial sections. Note that the activity inference functions (`SecAct.activity.inference` and its `*.ST` and `*.scRNAseq` variants) rely on ridge regression and permutation in R language, and may run relatively slowly. If you would like to speed up these functions, please visit <a href="https://data2intelligence.github.io/SecAct/articles/accelerator.html" target="_blank">here</a> to install the accelerator R package additionally.

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#### 🔷 Alternative installation options

1. If you are not familiar with R, you can use the Python version of SecAct available <a href="https://github.com/data2intelligence/SecActpy" target="_blank">here</a>.

2. If you prefer not to install SecAct locally, a Docker image is available for both the R and Python versions:
``` 
# Pull the Docker image.
docker pull psychemistz/secactpy:with-r
``` 
3. An online server is also available <a href="https://appshare.cancer.gov/SecAct/" target="_blank">here</a>. Please note that it currently supports the basic activity inference function only and is limited to datasets with fewer than 10 samples.

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## Dependencies

* R version >= 4.2.0.
* R packages: Matrix, ggplot2, reshape2, patchwork, NMF, akima, gganimate, metap, circlize, ComplexHeatmap, ggalluvial, networkD3, survival, survminer.


## Example

``` r
library(SecAct)

dataPath <- file.path(system.file(package = "SecAct"), "extdata/")
expr.diff <- read.table(paste0(dataPath, "Ly86-Fc_vs_Vehicle_logFC.txt"))

# infer activity; ~2 mins
res <- SecAct.activity.inference(inputProfile=expr.diff, is.differential=TRUE) 

head(res$zscore)

```


## Tutorial
SecAct is applicable to multiple modalities of gene expression profiles, including spatial, single-cell, and bulk transcriptomics data.

#### Spatial transcriptomcis (ST) data
* [Signaling patterns and velocities for multi-cellular ST data](https://data2intelligence.github.io/SecAct/articles/stPattern.html)  
* [Intercellular communication for single-cell resolution ST data](https://data2intelligence.github.io/SecAct/articles/stCCC.html)

#### Single-cell RNA sequencing data
* [Secreted protein signaling activity for distinct cell states](https://data2intelligence.github.io/SecAct/articles/scState.html)
* [Cell-cell communication mediated by secreted proteins](https://data2intelligence.github.io/SecAct/articles/scCCC.html)  

#### Bulk RNA sequencing data
* [Secreted protein signaling activity change between two phenotypes](https://data2intelligence.github.io/SecAct/articles/bulkChange.html)
* [Clinical relevance of secreted proteins in a large patient cohort](https://data2intelligence.github.io/SecAct/articles/bulkCohort.html)

#### Miscellaneous
* [Activity inference accelerators and batch mode](https://data2intelligence.github.io/SecAct/articles/accelerator.html)


## Contact
For questions, bug reports, or feature requests, please submit an [issue](https://github.com/data2intelligence/SecAct/issues). To keep the issue tracker focused and constructive, advertising or promotional content is not permitted.

## Citation
Beibei Ru,  Lanqi Gong, Emily Yang, Seongyong Park, George Zaki, Kenneth Aldape, Lalage Wakefield, Peng Jiang. Inference of secreted protein activities in intercellular communication. **Nature Methods**, 2026. [[Full Text](https://github.com/data2intelligence/SecAct){target="_blank"}]