scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data

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Description

<jats:title>Abstract</jats:title><jats:p>Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an individual cell based on its transcriptional profile. Here, we present<jats:italic>scPred</jats:italic>, a new generalizable method that is able to provide highly accurate classification of single cells, using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning probability-based prediction method. We apply<jats:italic>scPred</jats:italic>to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cells and show that<jats:italic>scPred</jats:italic>is able to classify individual cells with high accuracy. The generalized method is available at<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/powellgenomicslab/scPred/">https://github.com/powellgenomicslab/scPred/</jats:ext-link>.</jats:p>

Journal

  • Genome Biology

    Genome Biology 20 (1), 2019-12

    Springer Science and Business Media LLC

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