Iterate gene set enrichment analysis across all traits and celltypes

iterate_gsea(
  xmat,
  ymat,
  correction_method = "BH",
  qvalue_thresh = 0.05,
  x_quantiles = 10,
  y_quantiles = 10,
  use_quantiles = 10,
  nCores = 1
)

Arguments

xmat	gene x trait matrix.
ymat	gene x celltype matrix.
correction_method	Multiple-testing correction method to be passed to `stats::p.adjust`.
qvalue_thresh	q.value threshold to use when report significant results summary.
x_quantiles	The number of quantiles to bin `ymat` data into.
y_quantiles	The number of quantiles to bin `ymat` data into.
nCores	Number of cores to use in parallel. Will optimize if `NULL`.

Examples

### DeGAs loadings
data("DEGAS_seurat")
xmat <- DEGAS_seurat@reductions$contributionGene@feature.loadings
xmat <- xmat[, 1:10] # Let's use just 10 components as an example

### Celltype Dataset
data("ctd_BlueLake2018_FrontalCortexOnly")
ymat <- ctd_BlueLake2018_FrontalCortexOnly[[1]]$specificity
res_gsea <- iterate_gsea(xmat = xmat, ymat = ymat)
#> 11410 intersecting genes between GWAS and CTD matrices.
#> Running 80 tests: 10 traits x 8 celltypes.
#> 
#> 0 significant results @ BH<0.05