The differential expression data can yield insights on potential diseases enriched in the sample data. Such conclusions can be drawn by observing the number of differentially expressed genes or proteins in your data. One such computational approach is described below.
iPathwayGuide provides a comprehensive analysis of differential gene/ protein expression data that includes disease analysis.
For each disease, the number of differentially expressed (DE) genes annotated to it is compared to the number of genes expected just by chance. iPathwayGuide uses an over-representation approach to compute statistical significance of observing more than the given number of DE genes. The p-value is computed using the hypergeometric distribution that can be corrected using False Discovery Rate or Bonferroni method.
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