DEsingle for detecting three types of differential expression in single-cell RNA-seq data-Reference-Cited by-同舟云学术

DEsingle for detecting three types of differential expression in single-cell RNA-seq data

Published:2018-04-24 Issue:18 Volume:34 Page:3223-3224
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Miao Zhun¹^ORCID,Deng Ke²,Wang Xiaowo¹,Zhang Xuegong¹³

Affiliation:

1. MOE Key Laboratory of Bioinformatics, Division of Bioinformatics and Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, China

2. Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China

3. School of Life Sciences, Tsinghua University, Beijing, China

Funder

National Natural Science Foundation of China

Chan Zuckerberg Initiative

Silicon Valley Community Foundation

Human Cell Atlas 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/34/18/3223/25731617/bty332.pdf

Reference13 articles.

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

3. Increasing cDNA yields from single-cell quantities of mRNA in standard laboratory reverse transcriptase reactions using acoustic microstreaming;Boon;J. Vis. Exp. Jove,2011

4. Accounting for technical noise in single-cell RNA-seq experiments;Brennecke;Nat. Methods,2013

5. Discrete distributional differential expression (D3E)–a tool for gene expression analysis of single-cell RNA-seq data;Delmans;BMC Bioinformatics,2016

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