Classifying next-generation sequencing data using a zero-inflated Poisson model-Reference-Cited by-同舟云学术

Classifying next-generation sequencing data using a zero-inflated Poisson model

Published:2017-11-27 Issue:8 Volume:34 Page:1329-1335
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Zhou Yan¹,Wan Xiang²,Zhang Baoxue³,Tong Tiejun⁴

Affiliation:

1. College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, China

2. Department of Computer Science, and Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong

3. School of Statistics, Capital University of Economics and Business, Beijing, China

4. Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong

Funder

National Natural Science Foundation of China

Hong Kong Baptist University

Health and Medical Research Fund

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/34/8/1329/25119952/btx768.pdf

Reference32 articles.

1. Differential expression analysis for sequence count data;Anders;Genome Biol,2010

2. Slicing and dicing for small RNAs;Birchler;Science,2008

3. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments;Bullard;BMC Bioinformatics,2010

4. Maximum likelihood estimation from incomplete data via the EM Algorithm;Dempster;J. R. Stat. Soc. Ser. B,1977

5. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis;Dillies;Brief. Bioinf,2013

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