Release date: 2016-12-01
IPA can now improve your success of mapping identifiers in your datasets by evaluating more than one column of gene or chemical IDs. Assign up to five columns of IDs, and IPA will scan from left to right across the columns of identifiers and stop (for that row) when it successfully maps an ID.
Mapping across multiple columns of IDs is especially valuable in the case of metabolite (chemical) IDs. Figure 1 shows a dataset during the upload process with four columns of metabolite IDs, which resulted in more rows being mapped than when using any one identifier column alone.
IPA now calculates a Benjamini-Hochberg (B-H) corrected p-value for Upstream Regulators and for Causal Networks, increasing the statistical stringency of these results in Core Analyses. The B-H p-value corrects for multiple testing-- the fact that the more statistical tests you run, the greater the chance that you will observe a false positive result. Figure 2 shows the Upstream Regulator tab in a Core Analysis with the new B-H column. Note that these new p-values won’t be present for any analysis that you have run prior to this release. Please re-run previous analyses to calculate the values.
B-H p-values have been available in IPA for Canonical Pathways and for Diseases and Functions for several years, however, the values were not easily accessible for the latter. An optional B-H column is now available in the Diseases & Functions tab as shown below:
The Diseases & Functions TreeMap can be visualized using the B-H corrected p-value. The rectangles can be colored by and/or sized by the -log of the B-H p-value, as shown below in Figure 4.
The B-H statistics are also available in Comparison Analysis for your analyses that are run (or re-run) after this release, and are calculated for all Analysis Match analyses as well.
The Help menu in IPA now has a quick link to a set of video tutorials to help you get started with how to use IPA. The topics range from how to format and upload your data, how to analyze your data, and how the p-values in IPA are calculated:
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