Work

Empirical Bayesian selection of hypothesis testing procedures for analysis of digital gene expression data

Year: 2013

Type: article

Abstract: Background Differential expression analysis of digital gene expression data involves performing a large number of hypothesis tests that compare the expression count data of each gene or transcript across two or more biological conditions. The assumptions of any specific hypothesis-testing method will probably not be valid for each of a very large number of genes. Thus, computational evaluation of assumptions should be incorporated into the analysis to select an appropriate hypothesis-testing method for each gene.

Source: BMC Bioinformatics

DOI: 10.1186/1471-2105-14-s17-a21

Authors: Stan Pounds, Cuilan Gao

Institutions: St. Jude Children's Research Hospital, University of Tennessee at Chattanooga

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,990

OpenAlex ID: W2066059478