Identifying cell populations with scRNASeq. (February 2018)

• Record Type: Journal Article
• Authors: Andrews, Tallulah S.; Hemberg, Martin
• Is Part Of: Molecular aspects of medicine. Volume 59(2017)
• Page Start: 114
• Page End: 122
• Publication Date: 2018-02
• Subjects: Pathology, Molecular -- Periodicals; Medicine -- Periodicals; Biochemistry -- Periodicals; Medicine -- Periodicals; Molecular Biology -- Periodicals; Pathologie moléculaire -- Périodiques; Médecine -- Périodiques; Electronic journals; 612.015
• DOI: 10.1016/j.mam.2017.07.002
• Languages: English
• ISSNs: 0098-2997

Abstract

Abstract: Single-cell RNASeq (scRNASeq) has emerged as a powerful method for quantifying the transcriptome of individual cells. However, the data from scRNASeq experiments is often both noisy and high dimensional, making the computational analysis non-trivial. Here we provide an overview of different experimental protocols and the most popular methods for facilitating the computational analysis. We focus on approaches for identifying biologically important genes, projecting data into lower dimensions and clustering data into putative cell-populations. Finally we discuss approaches to validation and biological interpretation of the identified cell-types or cell-states.