Supporting data for "IKAP - Identifying K mAjor cell Population groups in single-cell RNA-seq analysis"

In single-cell RNA-seq analysis, clustering cells into groups and differentiating cell groups by differentially expressed (DE) genes are two separate steps for investigating cell identity. However, the ability to differentiate between cell groups could be affected by clustering. This interdependency often creates a bottleneck in the analysis pipeline, requiring researchers to repeat these two steps multiple times by setting different clustering parameters to identify a set of cell groups that are more differentiated and biologically relevant.
To accelerate this process, we have developed IKAP an algorithm to identify major cell groups and improve differentiating cell groups by systematically tuning parameters for clustering. We demonstrate that, with default parameters, IKAP successfully identifies major cell types such as T cells, B cells, NK cells, and monocytes in two peripheral blood mononuclear cell (PBMC) datasets and recovers major cell types in a previously published mouse cortex dataset. These major cell groups identified by IKAP present more distinguishing DE genes compared with cell groups generated by different combinations of clustering parameters. We further show that cell subtypes can be identified by recursively applying IKAP within identified major cell types thereby delineating cell identities in a multi-layered ontology.
By tuning the clustering parameters to identify major cell groups, IKAP greatly improves the automation of single-cell RNA-seq analysis to produce distinguishing DE genes and refine cell ontology using single-cell RNA-seq data.