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Indexed by:期刊论文

Date of Publication:2015-01-01

Journal:Cancer Informatics

Included Journals:Scopus

Volume:14

Page Number:57-67

ISSN No.:11769351

Abstract:Deep sequencing has recently emerged as a powerful alternative to microarrays for the high-throughput profiling of gene expression. In order to account for the discrete nature of RNA sequencing data, new statistical methods and computational tools have been developed for the analysis of differential expression to identify genes that are relevant to a disease such as cancer. In this paper, it is thus timely to provide an overview of these analysis methods and tools. For readers with statistical background, we also review the parameter estimation algorithms and hypothesis testing strategies used in these methods. ? the authors, publisher and licensee Libertas Academica Limited.