SecAct: Secreted Protein Activity Inference

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 1248 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.

Installation

To install SecAct, we recommend using devtools:

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

Or user can install SecAct from the source code. Click here to download it.

# install SecAct in the R environment.
install.packages("Path_to_the_source_code", repos = NULL, type="source")

The package has been installed successfully on Operating Systems:

• Red Hat Enterprise Linux 8.10 (Ootpa)
• macOS Sequoia 15.3.1

How to install GNU Scientific Library (GSL)?

If your operating system does not already have GSL installed, please follow one of the installation methods below, depending on your operating system.

# Linux (Ubuntu/Debian):
sudo apt-get install libgsl-dev

# Linux (Fedora/RHEL-based):
sudo dnf install gsl-devel

# macOS (Homebrew):
brew install gsl

Dependencies

• C Library: GNU Scientific Library (GSL).
• R version >= 4.2.0.
• R packages: Matrix, ggplot2, reshape2, patchwork, NMF, gganimate, metap, circlize, ComplexHeatmap, ggalluvial, networkD3, survival, survminer.

Example

library(SecAct)

dataPath <- file.path(system.file(package = "SecAct"), "extdata/")
expr.diff <- read.table(paste0(dataPath, "Ly86-Fc_vs_Vehicle_logFC.txt"))
res <- SecAct.activity.inference(inputProfile=expr.diff, is.differential=TRUE) # ~2 mins

head(res$zscore)

Tutorial

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.

Spatial transcriptomcis (ST) data

• Signaling patterns and velocities for multi-cellular ST data
  
• Intercellular communication for single-cell resolution ST data

Single-cell RNA sequencing data

• Cell-cell communication mediated by secreted proteins
  
• Secreted protein signaling activity for distinct cell states

Bulk RNA sequencing data

• Clinical relevance of secreted proteins in a large patient cohort
• Secreted protein signaling activity change between two phenotypes

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. [Link]