Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
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Using single-cell multiple omics approaches to resolve tumor heterogeneity.The Human Cell Atlas: Technical approaches and challenges.Single cell transcriptome analysis of muscle satellite cells reveals widespread transcriptional heterogeneity.Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance.
P2860
Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
description
2017 nî lūn-bûn
@nan
2017 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2017 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2017年の論文
@ja
2017年論文
@yue
2017年論文
@zh-hant
2017年論文
@zh-hk
2017年論文
@zh-mo
2017年論文
@zh-tw
2017年论文
@wuu
name
Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
@ast
Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
@en
type
label
Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
@ast
Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
@en
prefLabel
Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
@ast
Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
@en
P2093
P2860
P356
P1433
P1476
Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
@en
P2093
Lana Garmire
Sherman M Weissman
Travers Ching
Xinghua Pan
P2860
P356
10.7717/PEERJ.2888
P50
P577
2017-01-19T00:00:00Z