Post hoc pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data

<p>Abstract</p> <p>Background</p> <p>Researchers using RNA expression microarrays in experimental designs with more than two treatment groups often identify statistically significant genes with ANOVA approaches. However, the ANOVA test does not discriminate which of the multiple treatment groups differ from one another. Thus, <it>post hoc </it>tests, such as linear contrasts, template correlations, and pairwise comparisons are used. Linear contrasts and template correlations work extremely well, especially when the researcher has <it>a priori </it>information pointing to a particular pattern/template among the different treatment groups. Further, all pairwise comparisons can be used to identify particular, treatment group-dependent patterns of gene expression. However, these approaches are biased by the researcher's assumptions, and some treatment-based patterns may fail to be detected using these approaches. Finally, different patterns may have different probabilities of occurring by chance, importantly influencing researchers' conclusions about a pattern and its constituent genes.</p> <p>Results</p> <p>We developed a four step, <it>post hoc </it>pattern matching (PPM) algorithm to automate single channel gene expression pattern identification/significance. First, 1-Way Analysis of Variance (ANOVA), coupled with <it>post hoc </it>'all pairwise' comparisons are calculated for all genes. Second, for each ANOVA-significant gene, all pairwise contrast results are encoded to create unique pattern ID numbers. The # genes found in each pattern in the data is identified as that pattern's 'actual' frequency. Third, using Monte Carlo simulations, those patterns' frequencies are estimated in random data ('random' gene pattern frequency). Fourth, a Z-score for overrepresentation of the pattern is calculated ('actual' against 'random' gene pattern frequencies). We wrote a Visual Basic program (StatiGen) that automates PPM procedure, constructs an Excel workbook with standardized graphs of overrepresented patterns, and lists of the genes comprising each pattern. The visual basic code, installation files for StatiGen, and sample data are available as supplementary material.</p> <p>Conclusion</p> <p>The PPM procedure is designed to augment current microarray analysis procedures by allowing researchers to incorporate all of the information from post hoc tests to establish unique, overarching gene expression patterns in which there is no overlap in gene membership. In our hands, PPM works well for studies using from three to six treatment groups in which the researcher is interested in treatment-related patterns of gene expression. Hardware/software limitations and extreme number of theoretical expression patterns limit utility for larger numbers of treatment groups. Applied to a published microarray experiment, the StatiGen program successfully flagged patterns that had been manually assigned in prior work, and further identified other gene expression patterns that may be of interest. Thus, over a moderate range of treatment groups, PPM appears to work well. It allows researchers to assign statistical probabilities to patterns of gene expression that fit <it>a priori </it>expectations/hypotheses, it preserves the data's ability to show the researcher interesting, yet unanticipated gene expression patterns, and assigns the majority of ANOVA-significant genes to non-overlapping patterns.</p

Pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data-1

<p><b>Copyright information:</b></p><p>Taken from "pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data"</p><p>http://www.biomedcentral.com/1471-2105/8/240</p><p>BMC Bioinformatics 2007;8():240-240.</p><p>Published online 5 Jul 2007</p><p>PMCID:PMC1934919.</p><p></p>tion information is appended. Data is then filtered according to user input (., absent and unannotated probe sets are removed). Using the Filtered data, StatiGen constructs a Monte Carlo simulation of the data. Both the filtered and Monte Carlo datasets are tested by 1-ANOVA and all pairwise Fisher's PLSD tests. Results from all pairwise comparisons are used to encode pattern IDs (see Methods). Pattern frequency is give by # genes identified in pattern and is statistically compared (Z-test) to that pattern's frequency in a Monte Carlo simulation. Graphic output of significantly overrepresented patterns, along with a list of member genes and annotation information, is included and can be saved as a separate worksheet for further analysis

Pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data-0

<p><b>Copyright information:</b></p><p>Taken from "pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data"</p><p>http://www.biomedcentral.com/1471-2105/8/240</p><p>BMC Bioinformatics 2007;8():240-240.</p><p>Published online 5 Jul 2007</p><p>PMCID:PMC1934919.</p><p></p>tion information is appended. Data is then filtered according to user input (., absent and unannotated probe sets are removed). Using the Filtered data, StatiGen constructs a Monte Carlo simulation of the data. Both the filtered and Monte Carlo datasets are tested by 1-ANOVA and all pairwise Fisher's PLSD tests. Results from all pairwise comparisons are used to encode pattern IDs (see Methods). Pattern frequency is give by # genes identified in pattern and is statistically compared (Z-test) to that pattern's frequency in a Monte Carlo simulation. Graphic output of significantly overrepresented patterns, along with a list of member genes and annotation information, is included and can be saved as a separate worksheet for further analysis