A survey of statistical software for analysing RNA-seq data

被引：0

作者：

Gao D. ^{[1
,5
]}

Kim J. ^{[2
]}

Kim H. ^{[4
]}

Phang T.L. ^{[3
]}

Selby H. ^{[2
]}

Tan A.C. ^{[2
,5
]}

Tong T. ^{[6
]}

机构：

[1] Department of Pediatrics, University of Colorado School of Medicine, Aurora

[2] Division of Medical Oncology, University of Colorado School of Medicine, Aurora

[3] Division of Critical Care and Pulmonary Medicine, University of Colorado School of Medicine, Aurora

[4] Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora

[5] Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora

[6] Department of Applied Mathematics, University of Colorado, Boulder

来源：

Human Genomics | / 5卷 / 1期

关键词：

normalisation; RNA-sequencing analysis; sequencing data; statistical software;

D O I：

10.1186/1479-7364-5-1-56

中图分类号：

学科分类号：

摘要：

High-throughput RNA sequencing is rapidly emerging as a favourite method for gene expression studies. We review three software packages - edgeR, DEGseq and baySeq - from Bioconductor http://bioconductor.org for analysing RNA-sequencing data. We focus on three aspects: normalisation, statistical models and the testing employed on these methods. We also discuss the advantages and limitations of these software packages.

引用

页码：56 / 60

页数：4

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