knitr::opts_chunk$set( # code or die echo = TRUE, # minimize verbosity warning = FALSE, message = FALSE, # dpi = 150, # for hires images comment = "#>") set.seed(0xFEED)
Under Construction
The facile feature set enrichment analysis leans on the sparrow framework
for running GSEA methods, as well as providing gene set collections via
sparrow::GeneSetDb
objects.
This vignette assumes that you've got a GeneSetDb
object in hand named gdb
.
library(FacileData) library(FacileAnalysis) samples <- exampleFacileDataSet() |> filter_samples(indication == "BLCA")
For now, I imagine (gene) set enerichment analyses will be performed over the result of other analyses. This means that we can perform (G)SEA on:
fdge
) using:or some combination of the two.
```r gdb <- sparrow::exampleGeneSetDb() dge.gsea <- samples |> flm_def(covariate = "sample_type", numer = "tumor", denom = "normal", batch = "sex") |> fdge() |> ffsea(gdb, method = c("cameraPR", "ora"), rank_by = "t") ```
The "highly loaded" genes from the first prinicpal component of a
principal components analysis (fpca
). Deafult GSEA methods are set for
each analysis result type, too, if you don't want to specify them.
r
pc1.gsea <- samples |>
fpca() |>
ffsea(gdb, pc = 1)
In the immediate term, the GSEA functionality will lean heavily on the sparrow package, including the bits that enable interactivity over the results (ie. the sparrow.shiny package).
Comparing (Gene) Set Enrichment Results
To compare GSEA results, Thomas' idea of overlaying enrichment maps from the two results on top of each other in some dynamic way may be the first thing we try.
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