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-typediscriminative 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 mutuallyexclusively 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.
Original language | English (US) |
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Article number | e1007445 |
Journal | PLoS computational biology |
Volume | 15 |
Issue number | 10 |
DOIs | |
State | Published - 2019 |
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Modeling and Simulation
- Ecology
- Molecular Biology
- Genetics
- Cellular and Molecular Neuroscience
- Computational Theory and Mathematics
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