Bobby Ranjan1, Florian Schmidt1, Wenjie Sun1, Jinyu Park1, Mohammad Amin Honardoost1,2, Joanna Tan1, Nirmala Arul Rayan1, Shyam Prabhakar3. 1. Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, 60 Biopolis Street, Singapore, 138672, Singapore. 2. Department of Medicine, School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, Singapore. 3. Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, 60 Biopolis Street, Singapore, 138672, Singapore. prabhakars@gis.a-star.edu.sg.
Abstract
BACKGROUND: Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner 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 approaches 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. RESULTS: We present SCCONSENSUS, an [Formula: see text] framework for generating a consensus clustering by (1) integrating results from both unsupervised and supervised approaches and (2) refining the consensus clusters using differentially expressed genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. CONCLUSIONS: SCCONSENSUS combines the merits of unsupervised and supervised approaches to partition cells with better cluster separation and homogeneity, thereby increasing our confidence in detecting distinct cell types. SCCONSENSUS is implemented in [Formula: see text] and is freely available on GitHub at https://github.com/prabhakarlab/scConsensus .
BACKGROUND: Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner 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 approaches 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. RESULTS: We present SCCONSENSUS, an [Formula: see text] framework for generating a consensus clustering by (1) integrating results from both unsupervised and supervised approaches and (2) refining the consensus clusters using differentially expressed genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. CONCLUSIONS: SCCONSENSUS combines the merits of unsupervised and supervised approaches to partition cells with better cluster separation and homogeneity, thereby increasing our confidence in detecting distinct cell types. SCCONSENSUS is implemented in [Formula: see text] and is freely available on GitHub at https://github.com/prabhakarlab/scConsensus .
Entities:
Keywords:
Cell type annotation; Clustering; Consensus method; ScRNA-seq
Authors: Michael Thompson; Maika Matsumoto; Tianqi Ma; Anne Senabouth; Nathan J Palpant; Joseph E Powell; Quan Nguyen Journal: Front Genet Date: 2021-07-19 Impact factor: 4.599