Closed whitleyo closed 5 years ago
Good point, we will include a more comprehensive output in the next couple of days. For now, you could just color the factors by the expression of the individual genes from the significant pathways:
plotFactorScatter(MOFAobject, factors = 1:2, color_by = "GENE_NAME")
where GENE_NAME has to be a feature that is part of the training data.
Now we include the test statistics in the runEnrichment
output and we provide a new function to inspect which genes are driving the signal for each pathway. See plotEnrichmentDetailed
.
Hi,
I've looked through the man pages and vignettes for the package, and it doesn't appear as if there's any functionality to determine the direction of association of a pathway with a factor. With runEnrichmentAnalysis, correct me if I'm wrong, but you get the result of a two sided test (p-value and adjusted p-value). For a biologist interested in characterizing what the sources of variation are in their data, the direction of association of pathways matter (e.g. how does location in the space of 2 factors determine how stem-like my samples are?). I think it might be a good idea to include the test statistic (e.g. mean difference statistic) in the output.
Thanks