IndepthPathway: an integrated tool for in-depth pathway enrichment analysis based on bulk and single cell sequencing data

Single-cell sequencing (SCS) enables exploring the pathways and processes of cells and cell populations, however, there is a paucity of pathway enrichment methods designed to tolerate the high noise and low gene coverage of this technology. When gene expression data are noisy and signals are sparse, testing pathway enrichment based on the genes measured may not yield statistically significant results which is particularly problematic when detecting the pathways enriched in less abundant cells that are vulnerable to disturbances. In this project, we developed a Weighted Concept Signature Enrichment Analysis (WCSEA) algorithm specialized for pathway enrichment analysis from single cell transcriptomics (scRNA-seq). WCSEA took a broader approach for assessing the functional relations of pathway gene sets to differentially expressed genes, and leverage the cumulative signature of molecular concepts characteristic of the highly differentially expressed genes, which we termed as the universal concept signature, to tolerate the high noise and low coverage of this technology. We then incorporated WCSEA into a R package called “IndepthPathway” for biologists to broadly leverage this method for pathway analysis based on bulk and single cell sequencing data. Through simulating the technical variability and dropouts in gene expression characteristic of scRNA-seq, WCSEA yielded overall low deviations in pathway enrichment results. Leveraging its unique strength, IndepthPathway will enhance the application of bulk and single cell sequencing technologies to explore the cellular pathway mechanisms at more precise resolution.