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 signaling activities of >1,000 secreted proteins 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 spatial data, SecAct can infer the signaling pattern and signaling velocity for secreted proteins. For single-cell data, SecAct can infer the intercellular communication network and signaling flow from source cells to receiver cells. For bulk 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. These functionalities and terms are explained more formally in the following tutorials.
To install SecAct R package, we recommend using devtools:
# install.packages("devtools")
devtools::install_github("data2intelligence/SecAct")Or user can install SecAct R package from the source code. Click
here
to download it.
# 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")The R package has been installed successfully on Operating Systems:
- Red Hat Enterprise Linux 8.10 (Ootpa)
- macOS Sequoia 15.3.1
-
If you are not familiar with R, you can use the
Python version of SecAct available
here. -
If you prefer not to install SecAct locally, we provide a Docker image for both the R and Python versions:
# Pull the Docker image.
docker pull psychemistz/secactpy:with-r
- An online server is also available here. Please note that it currently supports only the basic activity inference function and is limited to datasets with fewer than 10 samples.
- C Library: GNU Scientific Library (GSL).
- R version >= 4.2.0.
- R packages: Matrix, ggplot2, reshape2, patchwork, NMF, akima, gganimate, metap, circlize, ComplexHeatmap, ggalluvial, networkD3, survival, survminer.
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)SecAct is applicable to multiple modalities of gene expression profiles, including spatial, single-cell, and bulk transcriptomics data. The following tutorials demonstrate its applications across each data type.
- Signaling patterns and velocities for multi-cellular ST data
- Intercellular communication for single-cell resolution ST data
- Secreted protein signaling activity for distinct cell states
- Cell-cell communication mediated by secreted proteins
- Secreted protein signaling activity change between two phenotypes
- Clinical relevance of secreted proteins in a large patient cohort
For questions, bug reports, or feature requests, please submit an issue. To keep the issue tracker focused and constructive, advertising or promotional content is not permitted.
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]