Description Usage Arguments Details Value
Method to perform feature set enrichment analysis. Here we use a slightly modified version of the pcgse function.
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object |
a |
view |
a character with the view name, or a numeric vector with the index of the view to use. |
feature.sets |
data structure that holds feature set membership information. Must be a binary membership matrix (rows are feature sets and columns are features). See details below for some pre-built gene set matrices. |
factors |
character vector with the factor names, or numeric vector with the index of the factors for which to perform the enrichment. |
set.statistic |
the set statisic computed from the feature statistics. Must be one of the following: "mean.diff" (default) or "rank.sum". |
statistical.test |
the statistical test used to compute the significance of the feature set statistics under a competitive null hypothesis. Must be one of the following: "parametric" (default), "cor.adj.parametric", "permutation". |
sign |
use only "positive" or "negative" weights. Default is "all". |
min.size |
Minimum size of a feature set (default is 10). |
nperm |
number of permutations. Only relevant if statistical.test is set to "permutation". Default is 1000 |
p.adj.method |
Method to adjust p-values factor-wise for multiple testing. Can be any method in p.adjust.methods(). Default uses Benjamini-Hochberg procedure. |
alpha |
FDR threshold to generate lists of significant pathways. Default is 0.1 |
verbose |
boolean indicating whether to print messages on progress |
The aim of this function is to relate each factor to pre-defined biological pathways by performing a gene set enrichment analysis on the feature weights.
This function is particularly useful when a factor is difficult to characterise based only on the genes with the highest weight.
We provide a few pre-built gene set matrices in the MOFAdata package. See https://github.com/bioFAM/MOFAdata
for details.
The function we implemented is based on the pcgse
function with some modifications.
Please read this paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4543476 for details on the math.
a list with five elements:
pval: |
matrices with nominal p-values. |
pval.adj: |
matrices with FDR-adjusted p-values. |
feature.statistics: |
matrices with the local (feature-wise) statistics. |
set.statistics: |
matrices with the global (gene set-wise) statistics. |
sigPathways |
list with significant pathways per factor. |
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