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Selection of differentially expressed genes in microarray data analysis

The Pharmacogenomics Journal volume 7, pages 212–220 (2007)Cite this article

Abstract

One common objective in microarray experiments is to identify a subset of genes that express differentially among different experimental conditions, for example, between drug treatment and no drug treatment. Often, the goal is to determine the underlying relationship between poor versus good gene signatures for identifying biological functions or predicting specific therapeutic outcomes. Because of the complexity in studying hundreds or thousands of genes in an experiment, selection of a subset of genes to enhance relationships among the underlying biological structures or to improve prediction accuracy of clinical outcomes has been an important issue in microarray data analysis. Selection of differentially expressed genes is a two-step process. The first step is to select an appropriate test statistic and compute the P-value. The genes are ranked according to their P-values as evidence of differential expression. The second step is to assign a significance level, that is, to determine a cutoff threshold from the P-values in accordance with the study objective. In this paper, we consider four commonly used statistics, t-, S- (SAM), U-(Mann–Whitney) and M-statistics to compute the P-values for gene ranking. We consider the family-wise error and false discovery rate false-positive error-controlled procedures to select a limited number of genes, and a receiver-operating characteristic (ROC) approach to select a larger number of genes for assigning the significance level. The ROC approach is particularly useful in genomic/genetic profiling studies. The well-known colon cancer data containing 22 normal and 40 tumor tissues are used to illustrate different gene ranking and significance level assignment methods for applications to genomic/genetic profiling studies. The P-values computed from the t-, U- and M-statistics are very similar. We discuss the common practice that uses the P-value, false-positive error probability, as the primary criterion, and then uses the fold-change as a surrogate measure of biological significance for gene selection. The P-value and the fold-change can be pictorially shown simultaneously in a volcano plot. We also address several issues on gene selection.

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Author notes

  1. J J Chen and S-J Wang: These authors contributed equally to this work.

Authors and Affiliations

  1. Division of Biometry and Risk Assessment, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA

    J J Chen & C-J Lin

  2. Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA

    S-J Wang

  3. Institute of Statistical Science, Academia Sinica, Taipei, Taiwan

    C-A Tsai

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  1. J J Chen
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  2. S-J Wang
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  3. C-A Tsai
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  4. C-J Lin
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Correspondence to J J Chen.

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The views presented in this paper are those of the authors and do not necessarily represent those of the US Food and Drug Administration.

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Chen, J., Wang, SJ., Tsai, CA. et al. Selection of differentially expressed genes in microarray data analysis. Pharmacogenomics J 7, 212–220 (2007). https://doi.org/10.1038/sj.tpj.6500412

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