PT  - JOURNAL ARTICLE
AU  - Wang, Fang
AU  - Liang, Shaoheng
AU  - Kumar, Tapsi
AU  - Navin, Nicholas
AU  - Chen, Ken
TI  - SCMarker: ab initio marker selection for single cell transcriptome profiling
AID  - 10.1101/356634
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 356634
4099  - http://biorxiv.org/content/early/2019/05/02/356634.short
4100  - http://biorxiv.org/content/early/2019/05/02/356634.full
AB  - 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.Author Summary Single cell RNA-sequencing technology simultaneously provides the mRNA transcript levels of thousands of genes in thousands of cells. A frequent requirement of single cell expression analysis is the identification of markers which may explain complex cellular states or tissue composition. We propose a new marker selection strategy (SCMarker) to accurately delineate cell types in single cell RNA-sequencing data by identifying genes that have bi/multi-modally distributed expression levels and are co- or mutually-exclusively expressed with some other genes. Our method can determine the cell-type-discriminative markers without referencing to any known transcriptomic profiles or cell ontologies, and consistently achieves accurate cell-type-discriminative marker identification in a variety of scRNA-seq datasets.