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Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

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

Bobby Ranjan¹, Florian Schmidt¹, Wenjie Sun¹

¹Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, 60 Biopolis Street, Singapore, 138672, Singapore.

BMC Bioinformatics

|April 13, 2021

Summary

SCCONSENSUS integrates unsupervised and supervised methods for improved single-cell data clustering. This consensus approach enhances cell type identification accuracy and confidence by refining clusters with gene expression data.

Keywords:

Cell type annotation Clustering Consensus method ScRNA-seq

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Last Updated: Nov 9, 2025

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Published on: October 25, 2018

Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Clustering is vital for single-cell data analysis, with unsupervised and supervised methods offering complementary strengths.
Unsupervised clustering relies on gene expression patterns, while supervised methods use labeled references for cell type identification.
Combining both approaches can yield more accurate clustering and precise cell type annotation.

Purpose of the Study:

To develop a novel framework, SCCONSENSUS, for generating consensus clustering in single-cell data.
To leverage the advantages of both unsupervised and supervised clustering paradigms for improved analysis.
To enhance cell type annotation accuracy through a refined consensus clustering approach.

Main Methods:

SCCONSENSUS integrates results from unsupervised and supervised clustering methods.
The framework refines consensus clusters using differentially expressed genes.
The approach is implemented as an R package.

Main Results:

SCCONSENSUS demonstrates improved cluster separation and homogeneity compared to individual methods.
The framework's effectiveness is validated on multiple single-cell RNA sequencing datasets.
Accurate cell type identification is achieved with increased confidence.

Conclusions:

SCCONSENSUS effectively combines unsupervised and supervised methods for robust single-cell data analysis.
The tool enhances confidence in detecting distinct cell types through improved clustering.
SCCONSENSUS is freely available on GitHub, facilitating its adoption in the research community.