Adjustments and measures of differential expression for microarray data.

A Tsodikov; A Szabo; D Jones

doi:10.1093/bioinformatics/18.2.251

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PubMed · 11847073

Adjustments and measures of differential expression for microarray data.

A Tsodikov·A Szabo·D Jones

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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.

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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 Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.

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A Tsodikov, A Szabo, D Jones. 2002. Adjustments and measures of differential expression for microarray data.. https://doi.org/10.1093/bioinformatics%2F18.2.251

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