ArticleComputational and structural biotechnology journal2026
FUNCellA: A Tool for Single-Sample Enrichment Analysis and Relative Pathway Activity Estimation in Single-Cell RNA Sequencing Data.
Article in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Single-cell RNA sequencing (scRNA-Seq) enables advanced analysis of cellular heterogeneity; however, due to data sparsity and noise, analyzing single genes can be challenging. While pathway activity scoring offers a robust alternative to standard single-gene analysis, most available enrichment methods were developed for bulk RNA-Seq or microarray data, so they may fail in addressing the variability and dropout typical of single-cell experiments. Existing approaches primarily focus on comparing pathway activity across predefined groups. Thus, there is a gap in solutions for clustering single-pathway activity vectors, which helps detect subpopulations. Consequently, this limits the functional interpretation and the discovery of meaningful data heterogeneity. We introduce FUNCellA, a framework for estimating relative pathway activity scores using unsupervised tools such as
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