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SCMarker: Ab initio marker selection for single cell transcriptome profiling.

ABSTRACT: 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.

SUBMITTER: Wang F

PROVIDER: S-EPMC6837541 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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SCMarker: Ab initio marker selection for single cell transcriptome profiling.

Wang Fang F Liang Shaoheng S Kumar Tapsi T Navin Nicholas N Chen Ken K

PLoS computational biology 20191028 10

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 expre ...[more]

PMID: 31658262

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