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64 Gonye et al.does not span the entire genome or is specific to a particular tissue/disease. Inmost of the cases, the microarray gene list is first processed in the FeasnetBuilder to obtain a microarray Feasnet. When analyzing the experimental genelists (e.g., differentially expressed genes from a microaray experiment), thismicroarray Feasnet should be utilized as the “reference Feasnet” in step 16 (seeSubheading 3.1.2.). However, the choice of reference set does not end withusing the reference Feasnet from the microarray gene list. For example, in comparisonof an early upregulated gene set to the set of all upregulated genes thesignificantly enriched TREs point to those that are characteristic of the earlyupregulated genes relative to all the upregulated genes. If the input gene list isthat of entire differentially expressed genes in an experiment, Feasnet from eachgene cluster in the input list (typically, with specific-expression profile or function)can be compared with that of the input list itself. Such a “cluster-to-list”comparison can reveal TREs that are differentially specific to each gene cluster.TRNA using PAINT is based on multiple results arising from such comparisons,for TRE enrichment to derive specific regulatory network hypotheses.The Feasnet analysis and visualization step can be repeated multiple times byconsidering different gene cluster combinations such as those based on differentclustering of expression pattern, biological function from gene ontology, orpathway data.4.6. Multiple Testing Correction Using an FDR EstimateIn PAINT, the raw p-values in each overrepresentation analysis are correctedfor multiple testing using a FDR estimate (29). As a first option, the results fromthe FDR-based, adjusted p-values should be used in identifying the significantlyoverrepresented TREs. However, in some cases, this particular correctionis either inappropriate (e.g., if raw p-values do not follow a β-uniform distribution)or overconservative (owing to correlations among TREs). It is likely thatfiltering the FDR-based multiple testing corrected p-values yields little or noresults. Hence, the Feasnet analysis and visualization step in PAINT includesfilters for both the adjusted and raw p-values. In cases wherein the former yieldsno results, one can utilize the latter to follow a discovery approach to deriveTRE hypotheses for further experimental validation. Whereas this alternativemay result in a set of hypotheses that can potentially contain 100% false-positivesin the extreme case (from the multiple testing perspective), in practice, thisamounts to prioritizing the validation experiments based on individuallyenriched TREs. Considering that the primary role of any computational analysisis in generating candidates for further experimental validation, in caseswhereby multiple testing correction yields little or no results the alternative rawp-value based approach is the next best option.