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bioRxiv · 10.1101/2020.11.16.384479

Venice: A new algorithm for finding marker genes in single-cell transcriptomic data

Vuong, H.·Truong, T.·Phan, T.·Pham, S.
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Abstract

AO_SCPLOWBSTRACTC_SCPLOWMost widely used tools for finding marker genes in single cell data (SeuratT/NegBinom/Poisson, CellRanger, EdgeR, limmatrend) use a conventional definition of differentially expressed genes: genes with different mean expression values. However, in single-cell data, a cell population can be a mixture of many cell types/cell states, hence the mean expression of genes cannot represent the whole population. In addition, these tools assume that gene expression of a population belongs to a specific family of distribution. This assumption is often violated in single-cell data. In this work, we define marker genes of a cell population as genes that can be used to distinguish cells in the population from cells in other populations. Besides log-fold change, we devise a new metric to classify genes into up-regulated, down-regulated, and transitional states. In a benchmark for finding up-regulated and down-regulated genes, our tool outperforms all compared methods, including Seurat, ROTS, scDD, edgeR, MAST, limma, normal t-test, Wilcoxon and Kolmogorov-Smirnov test. Our method is much faster than all compared methods, therefore, enables interactive analysis for large single-cell data sets in BioTuring Browser. Venice algorithm is available within Signac package: https://github.com/bioturing/signac 1).

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Vuong, H., Truong, T., Phan, T., Pham, S.. 2020-11-17. Venice: A new algorithm for finding marker genes in single-cell transcriptomic data. https://doi.org/10.1101/2020.11.16.384479

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