cit_gsa: gene set analysis for scRNA-seq with covariate adjustment
- 1University of Bordeaux, Inserm Bordeaux Population Health, Inria SISTM, Vaccine Research Institute, Institut de Mathématiques de Toulouse
Abstract
Single-cell RNA-seq enables gene expression analysis at single-cell resolution. Differential Expression Analysis (DEA) performed at the gene set level for better power and interpretation, identifies gene sets associated with a factor of interest. Existing single-cell DEA methods rely on two-step approaches, a gene-level analysis followed by an enrichment step, are often limited to two-group comparisons and do not support covariate adjustment. We introduce cit_gsa, a novel one-step self-contained gene set DEA method for Single-cell RNA-seq that handles more than two conditions and allows covariate adjustment. A benchmark of cit_gsa against state-of-the-art methods shows that our method has better performance in complex scenarios, and real-data analysis confirms that covariate adjustment improves biological interpretation.
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BibTeX
@misc{fallet2026citgsa,
title = {{cit_gsa: gene set analysis for scRNA-seq with covariate adjustment}},
author = {S. Fallet and V. Godot and P. Neuvial and B. P. Hejblum},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.13.758831},
url = {https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - cit_gsa: gene set analysis for scRNA-seq with covariate adjustment
AU - S. Fallet
AU - V. Godot
AU - P. Neuvial
AU - B. P. Hejblum
PY - 2026
DA - 2026/10/13
PB - bioRxiv
DO - 10.64898/2026.10.13.758831
UR - https://www.biorxiv.org/content/10.64898/2026.10.13.758831v1
AB - Single-cell RNA-seq enables gene expression analysis at single-cell resolution. Differential Expression Analysis (DEA) performed at the gene set level for better power and interpretation, identifies gene sets associated with a factor of interest. Existing single-cell DEA methods rely on two-step approaches, a gene-level analysis followed by an enrichment step, are often limited to two-group comparisons and do not support covariate adjustment. We introduce cit_gsa, a novel one-step self-contained gene set DEA method for Single-cell RNA-seq that handles more than two conditions and allows covariate adjustment. A benchmark of cit_gsa against state-of-the-art methods shows that our method has better performance in complex scenarios, and real-data analysis confirms that covariate adjustment improves biological interpretation.
ER -