scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

Abstract

Clustering is a crucial step in the analysis of single-cell data. Clusters identified using unsupervised clustering are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering strategies have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. We present O_SCPLOWSCC_SCPLOWCO_SCPLOWONSENSUSC_SCPLOW, an R framework for generating a consensus clustering by (i) integrating the results from both unsupervised and supervised approaches and (ii) refining the consensus clusters using differentially expressed (DE) genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. O_SCPLOWSCC_SCPLOWCO_SCPLOWONSENSUSC_SCPLOW is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.