SCMarker: Ab initio marker selection for single cell transcriptome profiling

Single-cell RNA-sequencing data generated by a variety of technologies, such as Drop-seq and SMART-seq, can reveal simultaneously the mRNA transcript levels of thousands of genes in thousands of cells. It is often important to identify informative genes or cell-type-discriminative markers to reduce...

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Bibliographic Details
Published in:PLoS computational biology 2019-10, Vol.15 (10), p.e1007445
Main Authors: Wang, Fang, Liang, Shaoheng, Kumar, Tapsi, Navin, Nicholas, Chen, Ken
Format: Article
Language:English
Subjects:
Algorithms
B cells
Base Sequence - genetics
Bioinformatics
Biology
Biology and Life Sciences
Biomarkers
Cancer
Cluster Analysis
Clustering
Computational Biology - methods
Datasets
Gene expression
Gene Expression Profiling - methods
Gene sequencing
Genes
Genetic research
Genomes
Head & neck cancer
Humans
Medicine and Health Sciences
Messenger RNA
Metastasis
Principal components analysis
Research and analysis methods
RNA
RNA - genetics
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
Software
Source code
Technology
Transcription
Transcriptome - genetics
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Summary:Single-cell RNA-sequencing data generated by a variety of technologies, such as Drop-seq and SMART-seq, can reveal simultaneously the mRNA transcript levels of thousands of genes in thousands of cells. It is often important to identify informative genes or cell-type-discriminative markers to reduce dimensionality and achieve informative cell typing results. We present an ab initio method that performs unsupervised marker selection by identifying genes that have subpopulation-discriminative expression levels and are co- or mutually-exclusively expressed with other genes. Consistent improvements in cell-type classification and biologically meaningful marker selection are achieved by applying SCMarker on various datasets in multiple tissue types, followed by a variety of clustering algorithms. The source code of SCMarker is publicly available at https://github.com/KChen-lab/SCMarker.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1007445