What’s new in the IPA Fall Release (September 2022)

New addition to Canonical Pathway bubble charts

In this release, you can now show individual Canonical Pathway names along either axis.

Figure 1 shows a bubble chart that plots the predicted effects of an Nrf2-activating compound on Canonical Pathway activity in mouse kidney. The pathways are sorted by significance and colored according to predicted activation. The sizes of the bubbles increase with the number of genes that overlap each pathway.

The most significant pathways are on the right side of the x-axis, and, in this dataset, are mostly activated, also having the largest number of genes that overlap the dataset (as indicated by bubble size).

Figure 1. Nrf2-activator Canonical Pathway scores plotted as pathway name vs. significance. The colors indicate the z-score (see legend at top right), and the size of the bubble increases with the number of overlapping genes. The gene expression data are from kidney of mice that were fed the Nrf2 activating chemical CDDO-me (Shelton, L.M. et al. (2015) Kidney Int. 88:1261. PMC4676083).

There are other ways to plot your data so that pathway names appear on an axis. For example, you can create a chart that emphasizes the categorization of the pathways (see Figure 2).

Figure 2. Canonical Pathway scores from single-cell data plotted as pathway name versus pathway category. This view helps you see how the pathways and their scores cluster into categories. The analyzed data are from the natural killer (NK) cell cluster from developing human liver. The data were re-processed using the CLC Workbench single-cell module from data published in Popescu, D.-M., et al (2019) Decoding human fetal liver haematopoiesis Nature 574: 365. The analysis in IPA is derived from the gene expression for the single-cell cluster of natural killer cells (compared to the rest of the clusters).

Improved display of analysis names in comparison heat maps

You can now toggle the height of the column headers in heatmaps to show the full analysis names, as shown in Figure 3. The taller headers are present in exported image files as well.

Figure 3. Expand column headers in comparison heatmaps to see the entire analysis names. This expansion applies both to Comparison Analysis heatmaps and Analysis Match heatmaps.

Content updates

Explore new areas with three new and eight updated pathways

New pathways

S100 Family Signaling Pathway
Neutrophil Extracellular Trap Signaling Pathway
Macrophage Alternative Activation Signaling Pathway

Updated to include an activity pattern

Cancer Drug Resistance by Drug Efflux
G Protein Signaling Mediated by Tubby
DNA damage-induced 14-3-3σ Signaling
Human Embryonic Stem Cell Pluripotency
IL-4 Signaling
IL-10 Signaling
Role of JAK family kinases in IL-6-type Cytokine Signaling
Role of JAK2 in Hormone-like Cytokine Signaling

>1.5 million new findings (bringing the total in IPA to over 11.8 million)

~60,000 expert findings
~995,000 from TargetScan mouse
~45,000 protein–protein interaction findings from BioGrid
~5000 Gene Ontology findings
~2000 target-to-disease findings from ClinicalTrials.gov
~2100 drug-to-disease findings from ClinicalTrials.gov
~2300 protein–protein interaction findings from IntAct
~1000 findings from the Online Mendelian Inheritance in Man (OMIM)
~600 gene-to-disease or phenotype associations from the Mouse Genome Database (MGD or "Jax”)
~18,500 from Human Metabolome Database (HMDB)
~550,000 findings from The Human Protein Atlas (THPA)

121,750 expression datasets will be available mid-October (3,457 added)

Read more here about the latest improvements to QIAGEN IPA.

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