Year: 2026
Type: preprint
Abstract: Summary 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. Availability and implementation cit_gsa is implemented in an R package available at https://CRAN.R-project.org/package=citcdf . The data and R scripts are available at https://doi.org/10.5281/zenodo.23264404 for reproducibility.
Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Université Toulouse III - Paul Sabatier, Université de Bordeaux, Inserm, Bordeaux Population Health, Institut de Recherche Vaccinale, Université de Toulouse, SISTM: Statistics In System biology and Translational Medicine, Institut de Mathématiques de Toulouse, Université Paris-Est Créteil, Université Paris-Saclay
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7221820623