Two-phase differential expression analysis for single cell RNA-seq-Reference-Cited by-同舟云学术

Two-phase differential expression analysis for single cell RNA-seq

Published:2018-04-24 Issue:19 Volume:34 Page:3340-3348
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Wu Zhijin¹²³,Zhang Yi¹,Stitzel Michael L⁴⁵⁶,Wu Hao⁷

Affiliation:

1. Department of Biostatistics, Brown University, Providence, RI, USA

2. Center for Statistical Sciences, Brown University, Providence, RI, USA

3. Center for Computational Molecular Biology, Brown University, Providence, RI, USA

4. The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA

5. Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA

6. Department of Genetics & Genome Sciences, University of Connecticut, Farmington, CT, USA

7. Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA

Funder

NIH

NIGMS

NSF

Peer Reviewed Medical Research Program

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/article-pdf/34/19/3340/25839598/bty329.pdf

Reference44 articles.

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2. Design and computational analysis of single-cell RNA-sequencing experiments;Bacher;Genome Biol,2016

3. Controlling the false discovery rate: a practical and powerful approach to multiple testing;Benjamini;J. R. Stat. Soc. Ser. B (Methodological),1995

4. Single-cell chromatin accessibility reveals principles of regulatory variation;Buenrostro;Nature,2015

5. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells;Buettner;Nat. Biotechnol,2015

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