SecAct: Inference of Secreted Protein Signaling Activity

Overview

SecAct is a computational framework for inferring intercellular signaling activity mediated by secreted proteins from transcriptomic data. The package implements a ridge regression model with permutation-based statistical testing to quantify the activity of over 1,000 secreted proteins across diverse biological contexts.

SecAct supports multiple data modalities:

Bulk RNA-seq: Population-level activity inference
Single-cell RNA-seq: Cell type-specific signaling analysis
Spatial transcriptomics: Spatially-resolved communication patterns

Methodology

Mathematical Framework

SecAct employs regularized linear regression to infer secreted protein activity:

$$\hat{\beta} = (X^TX + \lambda I)^{-1} X^T Y$$

where:

$Y$ ∈ ℝ^(n×m): Gene expression matrix (n genes × m samples)
$X$ ∈ ℝ^(n×p): Signature matrix (n genes × p secreted proteins)
$\hat{\beta}$ ∈ ℝ^(p×m): Inferred activity matrix
$\lambda$: Ridge regularization parameter (default: 5×10⁵)

Statistical Significance

Activity significance is assessed via permutation testing:

Generate null distribution by permuting sample labels
Compute z-scores: $z = (\hat{\beta} - \mu_{null}) / \sigma_{null}$
Calculate empirical p-values with correction for finite permutations

Signature Database

The SecAct signature matrix comprises:

1,170 curated secreted proteins
7,919 downstream target genes
Derived from published literature and pathway databases

Installation

From R-universe (Recommended)

install.packages("SecAct", repos = c(
  "https://zaoqu-liu.r-universe.dev",
  "https://cloud.r-project.org"
))

From GitHub

# Install devtools if needed
if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")

devtools::install_github("Zaoqu-Liu/SecAct")

System Requirements

SecAct includes an optional C implementation using the GNU Scientific Library (GSL) for enhanced 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

Data Download

SecAct signature database (~76MB) is hosted on GitHub Release and will be automatically downloaded on first use. You can also manually download:

library(SecAct)

# Check data status
SecAct.check.data()

# Download data (if not already present)
SecAct.download.data()

Quick Start

library(SecAct)

# Load example expression 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, lambda = 5e5, nrand = 1000)

# Access results
head(result$zscore)  # Activity z-scores
head(result$pvalue)  # Statistical significance

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

Key Functions

Function	Description
`SecAct.inference.r()`	Pure R implementation (cross-platform)
`SecAct.inference.gsl()`	GSL-accelerated implementation
`SecAct.activity.inference()`	Main inference function (bulk data)
`SecAct.activity.inference.ST()`	Spatial transcriptomics inference
`SecAct.activity.inference.scRNAseq()`	Single-cell RNA-seq inference
`SecAct.CCC.scST()`	Cell-cell communication (spatial)
`SecAct.CCC.scRNAseq()`	Cell-cell communication (single-cell)
`SecAct.signaling.pattern()`	NMF-based signaling pattern discovery
`SecAct.coxph.regression()`	Survival association analysis

Documentation

Comprehensive tutorials are available at: https://zaoqu-liu.github.io/SecAct/

Tutorial	Description
Quick Start	Getting started in 5 minutes
Algorithm	Mathematical principles and implementation
Visualization	Comprehensive plotting guide
Spatial: Patterns	Signaling patterns in spatial data
Spatial: Communication	Cell-cell communication analysis
Single-cell: CCC	scRNA-seq communication inference
Bulk: Differential	Treatment vs control analysis
Bulk: Survival	Clinical relevance assessment

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. Manuscript in preparation.

Authors

Beibei Ru — Original author (beibei.ru@nih.gov)
Zaoqu Liu — Maintainer (liuzaoqu@163.com)

License

This project is licensed under the GPL-3.0 License — see the LICENSE file for details.

Related Resources

Documentation: https://zaoqu-liu.github.io/SecAct/
R-universe: https://zaoqu-liu.r-universe.dev/SecAct
Issues: https://github.com/Zaoqu-Liu/SecAct/issues