SecAct

SecAct (Secreted protein Activity inference) is an R package for inferring intercellular signaling activity of over 1,000 secreted proteins from gene expression profiles. It supports spatial transcriptomics, single-cell RNA-seq, and bulk RNA-seq data.

🌟 Key Features

Multi-platform: Works on macOS, Linux, and Windows
Multi-data: Supports bulk, single-cell, and spatial transcriptomics
Fast: C/GSL implementation with pure R fallback
Comprehensive: 1,170 secreted proteins, 7,919 downstream genes
Statistical: Permutation-based significance testing

📦 Installation

Prerequisites

SecAct uses GSL (GNU Scientific Library) for optimal performance. GSL is optional - a pure R fallback is available.

# macOS
brew install gsl

# Ubuntu/Debian
sudo apt-get install libgsl-dev

# RHEL/CentOS/Fedora
sudo dnf install gsl-devel

Install SecAct

# Install from GitHub
devtools::install_github("Zaoqu-Liu/SecAct")

# Windows users (if GSL not available):
devtools::install_github("Zaoqu-Liu/SecAct", configure.args = "--without-gsl")

🚀 Quick Start

library(SecAct)

# Load example data
data_path <- system.file("extdata/GSE100093.IFNG.expr.gz", package = "SecAct")
expr <- read.table(gzfile(data_path), sep = "\t", header = TRUE, row.names = 1)

# Infer secreted protein activity
result <- SecAct.activity.inference(expr)

# Visualize results
SecAct.heatmap.plot(result$zscore[1:20, ])

📚 Tutorials

Tutorial	Description
Quick Start	Get started in 5 minutes
Algorithm	Mathematical principles
Visualization	Plotting guide
Spatial: Patterns	ST signaling patterns
Spatial: Communication	ST cell-cell communication
Single-cell: CCC	scRNA-seq communication
Single-cell: State	Cell state activity
Bulk: Change	Treatment vs control
Bulk: Cohort	Clinical relevance

🔬 Algorithm Overview

SecAct employs ridge regression with permutation testing:

β = (X'X + λI)⁻¹ X'Y

Where: - Y: Gene expression matrix - X: Secreted protein signature matrix (1,170 proteins) - β: Inferred activity matrix - λ: Regularization parameter (default: 5×10⁵)

Statistical significance is assessed via permutation testing with z-score transformation.

📊 Workflow

┌─────────────────────────────────────────────────────────────┐
│                    SecAct Workflow                          │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Expression Data ──► Activity Inference ──► Downstream      │
│  (Bulk/SC/Spatial)   (Ridge Regression)    Analysis         │
│                                                             │
│                           │                                 │
│                           ▼                                 │
│              ┌────────────────────────┐                     │
│              │  • Cell Communication  │                     │
│              │  • Signaling Patterns  │                     │
│              │  • Survival Analysis   │                     │
│              │  • Velocity Analysis   │                     │
│              └────────────────────────┘                     │
│                                                             │
└─────────────────────────────────────────────────────────────┘

📖 Citation

If you use SecAct in your research, please cite:

Beibei Ru, Lanqi Gong, Emily Yang, Seongyong Park, George Zaki, Kenneth Aldape, Lalage Wakefield, Peng Jiang. Inference of secreted protein activities in intercellular communication.