Unsupervised Investigation of Information Captured in Pathway Activity Score in scRNA-Seq Analysis

Abstract

With the introduction of single cell RNA sequencing, research on cell, tissue and disease heterogeneity has a new boost. Transforming gene levels to explainable pathways via single-sample enrichment algorithms is a leading analysis step in understanding cell heterogeneity. In this study, eight different single-sample methods were investigated and accompanied by gene level outcomes as reference. For all, their ability to cell separation and clustering accuracy was tested. For this purpose, six scRNA-Seq datasets with labelled cells and their various numbers were collected. PLAGE method shows the best cell separation with statistically significant differences to gene level and to six other tested methods. The clustering accuracy analysis also indicates that PLAGE is the leading technique in single-sample enrichment methods in scRNA-Seq. Here the worst performance was observed to JASMINE algorithm which, in contrary to PLAGE, was designed to analyse the scRNA-Seq data. Moreover, Louvain clustering shows the best results regarding cell division regardless of the tested single-sample method. Finally, the results of clustering given by PLAGE reveal T cell subtypes initially not labelled, showing the great potential of this algorithm in heterogeneity investigation.