scGSCA: gene-set differential co-expression analysis for single-cell RNA sequencing data
- 1University of Miami
Abstract
Understanding disease-associated pathway coordination requires distinguishing phenotype-related co-expression changes from differences across cellular contexts while accounting for cells sampled from the same subject. Bulk gene-set co-expression methods do not directly address the sparse counts, single-cell distributional noise, subject-level dependence, and cell-type heterogeneity of single-cell RNA sequencing (scRNA-seq) data. We developed single-cell gene-set co-expression analysis (scGSCA), a framework for pathway-level differential co-expression analysis in scRNA-seq data. scGSCA fits gene-wise zero-inflated negative binomial mixed models with subject-level random effects and computes gene-set co-expression statistics from quantile residual correlations. It provides an overall differential gene-set co-expression analysis and an interaction test to assess whether the co-expression pattern between two cell types varies by sample phenotype. We applied scGSCA to tumor-normal gastric, breast-subtype, and lung-skin comparisons with matched bulk and scRNA-seq data. The overall test identified signals not captured in bulk analysis, including Axon guidance in gastric cancer and estrogen-response, IL6-JAK-STAT3, and TNF/NF-{kappa}B pathways in breast cancer. The interaction test further identified co-expression contrasts across cell types. scGSCA extends gene-set co-expression analysis to single-cell data while retaining subject-level structure and cell-type-specific context. scGSCA is freely available as an open-source R package.
Related
- Model-based inference of gene expression noise from single-cell RNA-sequencing data
- Limitations of differential expression for cell-type marker discovery in single-cell RNA sequencing atlases
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- Differential Gene Expression and Functional Enrichment Analysis of Breast Tumor and Normal Tissue RNA-seq Data: A Reanalysis of GEO Dataset GSE210787
- Differential Gene Expression in the Tropical House Cricket and Its Iridovirus in Healthy versus Diseased Specimens
BibTeX
@misc{jin2026scgsca,
title = {{scGSCA: gene-set differential co-expression analysis for single-cell RNA sequencing data}},
author = {Z. Jin and R. Irlmeier and J. Ping and Y. Wang and Y. Guo and F. Ye},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.05.756579},
url = {https://www.biorxiv.org/content/10.64898/2026.10.05.756579v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - scGSCA: gene-set differential co-expression analysis for single-cell RNA sequencing data
AU - Z. Jin
AU - R. Irlmeier
AU - J. Ping
AU - Y. Wang
AU - Y. Guo
AU - F. Ye
PY - 2026
DA - 2026/10/05
PB - bioRxiv
DO - 10.64898/2026.10.05.756579
UR - https://www.biorxiv.org/content/10.64898/2026.10.05.756579v1
AB - Understanding disease-associated pathway coordination requires distinguishing phenotype-related co-expression changes from differences across cellular contexts while accounting for cells sampled from the same subject. Bulk gene-set co-expression methods do not directly address the sparse counts, single-cell distributional noise, subject-level dependence, and cell-type heterogeneity of single-cell RNA sequencing (scRNA-seq) data. We developed single-cell gene-set co-expression analysis (scGSCA), a framework for pathway-level differential co-expression analysis in scRNA-seq data. scGSCA fits gene-wise zero-inflated negative binomial mixed models with subject-level random effects and computes gene-set co-expression statistics from quantile residual correlations. It provides an overall differential gene-set co-expression analysis and an interaction test to assess whether the co-expression pattern between two cell types varies by sample phenotype. We applied scGSCA to tumor-normal gastric, breast-subtype, and lung-skin comparisons with matched bulk and scRNA-seq data. The overall test identified signals not captured in bulk analysis, including Axon guidance in gastric cancer and estrogen-response, IL6-JAK-STAT3, and TNF/NF-{kappa}B pathways in breast cancer. The interaction test further identified co-expression contrasts across cell types. scGSCA extends gene-set co-expression analysis to single-cell data while retaining subject-level structure and cell-type-specific context. scGSCA is freely available as an open-source R package.
ER -