Computational approaches for interpreting scRNA-seq data.
about
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications.Delineating biological and technical variance in single cell expression dataSingle-cell transcriptomics to explore the immune system in health and disease.Lessons from single-cell transcriptome analysis of oxygen-sensing cells.Global and targeted approaches to single-cell transcriptome characterization.Tracking the origin, development, and differentiation of hematopoietic stem cells.An interpretable framework for clustering single-cell RNA-Seq datasets.Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database
P2860
Q38618194-8EC627AF-274C-401A-B164-5BBC5B6B42DBQ38675040-63D01B6A-D1F5-46A9-978A-F85A93B055B4Q42641259-98F068F2-387B-4E07-9270-65F01F1A9FC6Q42777629-D3E6E043-6C9C-4889-AF84-73CBFC334591Q42778129-D03A6A82-DAF7-4D0A-A500-6776DC9FF460Q48502210-01E09900-472D-4444-A303-95CD96AE9360Q52664099-1E104B35-2B7E-40DB-8281-E802A228FF07Q56394930-042BEAED-E284-4D4D-8CDD-527D1523AF55
P2860
Computational approaches for interpreting scRNA-seq data.
description
2017 nî lūn-bûn
@nan
2017年の論文
@ja
2017年学术文章
@wuu
2017年学术文章
@zh-cn
2017年学术文章
@zh-hans
2017年学术文章
@zh-my
2017年学术文章
@zh-sg
2017年學術文章
@yue
2017年學術文章
@zh
2017年學術文章
@zh-hant
name
Computational approaches for interpreting scRNA-seq data.
@ast
Computational approaches for interpreting scRNA-seq data.
@en
type
label
Computational approaches for interpreting scRNA-seq data.
@ast
Computational approaches for interpreting scRNA-seq data.
@en
prefLabel
Computational approaches for interpreting scRNA-seq data.
@ast
Computational approaches for interpreting scRNA-seq data.
@en
P2860
P50
P356
P1433
P1476
Computational approaches for interpreting scRNA-seq data.
@en
P2093
Raghd Rostom
P2860
P304
P356
10.1002/1873-3468.12684
P407
P577
2017-05-19T00:00:00Z
2017-08-01T00:00:00Z