Increase your interpretation power with the new Analysis Match Metadata Evaluator
Now you can easily discover expected and unexpected commonalities among sets of analyses of interest in Analysis Matchusing a new capability that detects statistically significant associations in their metadata. For example: Are the analyses that match yours surprisingly often derived from a particular tissue type, disease state, or treatment? Or do they tend to derive from a particular mouse strain, or from cells with particular cell surface markers? This approach can help easily identify similarities among matching analyses that may have been previously hidden.
IPA scans across more than 90 metadata fields from the set of repository-based analyses that you select in Analysis Match and performs a calculation to detect potential enrichment among their metadata. Figure 1A shows an Analysis Match result filtered for analyses that strongly match (or anti-match) an analysis of gemfibrozil-treated rats. Gemfibrozil is a well-known PPAR agonist. Selecting the matching set (those in the red dotted box in Figure 1A) and then clicking the Evaluate Metadata button generates p-values that are calculated using a right-tailed Fisher’s Exact Test. The results are displayed in a table like the one shown in Figure 1B.
The most significant term among the selected analyses is “PPAR agonists” in Figure 1B in the case.subjecttreatmentfield with p-value = 6.98E-08.Other examples of overrepresented terms are “white adipose cell” and “preadipocyte” in the “case.celltype” field.
Note that the case.subjecttreatment and case.celltypefields are not shown in the Analysis Match table by default, calling attention to the fact that this new feature sifts through and surfaces metadata that may be hidden initially (due to space constraints in the UI).
Figure 1: New feature in Analysis Match to discover commonalties among analyses of interest via shared metadata. Figure 1A shows Analysis Match results for the transcriptomics analysis of the liver of rats treated with the PPAR agonist gemfibrozil (RNA-seq data from PMID 25150839). The table has been filtered to retain only the strongest matching (average matching percentage >43) or anti-matching analyses (average matching percentage < -43). The matching analyses enclosed in the red dotted box were selected and the “Evaluate Metadata” button was chosen. Figure 1B shows the results of the enrichment calculation, where the term “PPAR agonists” was found to be highly enriched (p-value = 6.98E-08) among the matching analyses in the “case.subjecttreatment field”. This level of significance arose because of the 18 analyses that were selected, three of them shared the “PPAR agonists” term, and there are only nine analyses in the entire set of 57,000+ analyses in the Analysis Match repository with that term. Other examples of overrepresented terms are “white adipose cell” and “preadipocyte” in the “case.celltype” field.
The metadata results table can be filtered to focus on certain fields or terms of interest. In Figure 2, the metadata evaluation results are narrowed to show only fields involving the “case” samples (rather than the controls).
Figure 2: Filtering the metadata results table.You can filter the results data to focus on certain types of fields or values, such as fields involving the case rather than the controls.
The analyses that were identified as being treated with “PPAR agonists” were tesaglitazar, fenofibrate, and rosiglitazone.
Note that the computation only considers the metadata in the repository-based analyses. It does not evaluate any metadata that you may have entered for any of your own analyses.
Speed your exploration of diseases and functions on pathways and networks
The Build > Grow > Diseases & Functions feature is a powerful way to add biological context to a pathway or network. However, its calculation of statistical over-representation is computationally expensive and often takes 30-60 seconds. In the past, after performing the first “Grow to Diseases & Functions” operation on a network, IPA would repeat the calculation immediately each time nodes were added or subtracted from the network, forcing you to wait for updated statistical results with each change. Now, IPA provides a button to perform the calculation so that you control when to update the statistics. You can make numerous changes and when ready, only then determine which diseases and function are statistically relevant. Figure 3 below shows the placement of the new Recalculate button.
Figure 3: Recalculate Diseases & Functions over-representation on demand.Now you can make multiple additions or subtractions to the network or pathway before performing the computationally expensive overlap calculation.
Support for import of .csv dataset files
IPA now supports the upload of .csv dataset files. Some upstream software such as 10x Genomics Loupe Cell Browser exports comma-separated data files. IPA now supports their direct import.
Three new Canonical Signaling Pathways
Inhibition of ARE-mediated mRNA degradation pathway
Hepatic Fibrosis Signaling Pathway
Addition of Activity Patterns to six existing Canonical Signaling Pathways
Cell Cycle Control of Chromosomal Replication
Crosstalk between Dendritic Cells and Natural Killer Cells
Endoplasmic Reticulum Stress Pathway
RAN Signaling Pathway
Reelin Signaling in Neurons
Unfolded Protein Response Signaling Pathway
~175,000 new findings (bringing the total to over 7 million findings), including:
~80,000 new Expert findings
~62,000 new Gene Ontology findings (primarily rat-related)
~27,000 protein-protein interaction findings from BioGRID
~6000 protein-protein interaction findings from IntAct
~1400 new disease-to-target findings from ClinicalTrials.gov
~1200 new drug-to-disease findings from ClinicalTrials.gov
~350 newly mappable chemicals
Version number or Date of Third Party Databases
Identifier and Gene Model Source Versions
Release notes as PDF