Parent pathways contain very few unique selected genes outside the intersection; highlighting them would be misleading.
Child pathways would be misleading to discuss if they have a smaller density of selected genes than the MSP.
For overlapping pathways, it is hard to intuitively assess if it is a Hitchhiker
or if it brings sufficient new information. Only those with enough additional
information should be discussed.
Due to the structure of the ontology, many pathways in a pathway enrichment
analysis (PEA) are Hitchhikers, which complicates the analysis and requires pathways
to be manually selected or grouped.
PathwayDenester performs statistical tests to check if a pathway is distinctive enough to be
highlighted alongside the MSP.
The genes selected from a pathway are randomized, then, starting from the most significant pathway,
every intersecting pathway is tested. The procedure is then repeated for every pathway kept by previous iterations.
The output is an assessment of all pathways (keep/exclude) based on the statistical evaluation.
Input PEA data using familiar point-and-click boxes. The
whole analysis runs server-side, so no
need to have Python or any kind of package installed.
By clicking on the “Preform Analysis” button, receive the
results in a table (that can be downloaded
as a tsv file) and see the input pathways
represented as a graph (which can be downloaded as a JSON file). Nodes are connected to the
most significant pathway they intersect and are
colored grey if they are to be excluded.