Medical College of Wisconsin
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Adjustments and measures of differential expression for microarray data. Bioinformatics 2002 Feb;18(2):251-60 PMID: 11847073

Pubmed ID

11847073

Abstract

MOTIVATION: Existing analyses of microarray data often incorporate an obscure data normalization procedure applied prior to data analysis. For example, ratios of microarray channels intensities are normalized to have common mean over the set of genes. We made an attempt to understand the meaning of such procedures from the modeling point of view, and to formulate the model assumptions that underlie them. Given a considerable diversity of data adjustment procedures, the question of their performance, comparison and ranking for various microarray experiments was of interest.

RESULTS: A two-step statistical procedure is proposed: data transformation (adjustment for slide-specific effect) followed by a statistical test applied to transformed data. Various methods of analysis for differential expression are compared using simulations and real data on colon cancer cell lines. We found that robust categorical adjustments outperform the ones based on a precisely defined stochastic model, including some commonly used procedures.

Author List

Tsodikov A, Szabo A, Jones D

Author

Aniko Szabo PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin




Scopus

2-s2.0-0036191188   45 Citations

MESH terms used to index this publication - Major topics in bold

Colonic Neoplasms
Computational Biology
Computer Simulation
DNA, Neoplasm
Data Interpretation, Statistical
Gene Expression Profiling
Humans
Models, Genetic
Models, Statistical
Oligonucleotide Array Sequence Analysis
Software
Stochastic Processes
Tumor Cells, Cultured
jenkins-FCD Prod-299 9ef562391eceb2b8f95265c767fbba1ce5a52fd6