Assumption adequacy averaging as a concept for developing more robust methods for differential gene expression analysis

DOI10.1016/j.csda.2008.05.010zbMath1453.62180OpenAlexW2082106305WikidataQ41836113 ScholiaQ41836113MaRDI QIDQ961314

Shesh N. Rai, Stan Pounds

Publication date: 30 March 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc2678745

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20)

Related Items (3)

Combining assumptions and graphical network into gene expression data analysis ⋮ Estimation of empirical null using a mixture of normals and its use in local false discovery rate ⋮ Editorial: Statistical genetics \& statistical genomics: where biology, epistemology, statistics, and computation collide

Cites Work

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• Improved statistical tests for differential gene expression by shrinking variance components estimates
• An analysis of variance test for normality (complete samples)
• Power and sample size estimation in high dimensional biology
• Statistical Significance Threshold Criteria For Analysis of Microarray Gene Expression Data
• Use of Ranks in One-Criterion Variance Analysis

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