Identifying cell populations with scRNASeq

被引：164

作者：

Andrews, Tallulah S. ^{[1
]}

Hemberg, Martin ^{[1
]}

机构：

[1] Wellcome Trust Sanger Inst, Hinxton, Cambs, England

来源：

MOLECULAR ASPECTS OF MEDICINE | 2018年 / 59卷

关键词：

RNA-SEQUENCING DATA; GENE-EXPRESSION; TRANSCRIPTIONAL HETEROGENEITY; SEQ; REVEALS; NORMALIZATION; NOISE; DIFFERENTIATION; RECONSTRUCTION; DIVERSITY;

D O I：

10.1016/j.mam.2017.07.002

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

070307 [化学生物学]; 071010 [生物化学与分子生物学];

摘要：

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. (C) 2017 The Authors. Published by Elsevier Ltd.

引用

页码：114 / 122

页数：9

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