ScPred: Accurate Supervised Method for Cell-type Classification from Single-cell RNA-seq Data

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2019 Dec 13

PMID 31829268

Citations 174

Authors

Jose Alquicira-Hernandez

Anuja Sathe

Hanlee P Ji

Quan Nguyen

Joseph E Powell

Affiliations

Soon will be listed here.

Abstract

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 scPred, 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 scPred to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cells and show that scPred is able to classify individual cells with high accuracy. The generalized method is available at https://github.com/powellgenomicslab/scPred/.

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