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ACSNEnrichment is an R package, freely available.
This package is designed for an easy analysis of gene maps (either user imported from gmt files or ACSN maps). Its aim is to allow a statistical analysis of statistically enriched or depleted pathways from a user imported gene list, as well as a graphic representation of results.
This readme contains:
The gene set that was used for tests is the following:
genes_test<-c("ATM","ATR","CHEK2","CREBBP","TFDP1","E2F1","EP300","HDAC1","KAT2B","GTF2H1","GTF2H2","GTF2H2B")
Gene set enrichment for a single set can be performed by calling:
enrichment(genes_test,
min_module_size = 10,
threshold = 0.05,
maps = list(cellcycle = ACSNEnrichment::ACSN_maps$CellCycle))
Where:
- genes_test is a character vector to test
- min_module_size is the minimal size of a module to be taken into account
- threshold is the maximal p-value that will be displayed in the results (all modules with p-values higher than threshold will be removed)
- maps is a list of maps -here we take the cell cycle map from ACSN- imported through the format_from_gmt() function of the package
Output looks like:
| module | module_size | nb_genes_in_module | genes_in_module | universe_size | nb_genes_in_universe | p.value | p.value.corrected | test |
|---|---|---|---|---|---|---|---|---|
| E2F1 | 19 | 12 | ATM ATR CHEK2 CREBBP TFDP1 E2F1 | 104 | 12 | 4.5e-06 | 6.68e-05 | greater |
| EP300 HDAC1 KAT2B GTF2H1 GTF2H2 GTF2H2B |
Gene set enrichment for multiple sets/cohorts can be performed by calling:
multisample_enrichment(Genes_by_sample = list(set1 = genes_test[-1],set2=genes_test[-2]),
maps = ACSNEnrichment::ACSN_maps$CellCycle,
min_module_size = 15)
Where:
- Genes_by_sample is a list of character vectors to test
- min_module_size is the minimal size of a module to be taken into account
- maps is a list of maps -here we take the cell cycle map from ACSN - imported through the format_from_gmt() function of the package
Results from the enrichment analysis function can be transformed to images thanks to the represent enrichment function. Two different plot are available: heatmap and barplot.
Heatmaps for single sample or multiple sample representing p-values can be easily generated thanks to the represent_enrichment function.
represent_enrichment(enrichment = list(SampleA = enrichment_test,
SampleB = enrichment_test[1:3,]),
plot = "heatmap", scale = "log")
Where:
- enrichment is the result from the enrichment or multisample_enrichment function
- scale can be set to either identity or log and will affect the gradient of colors
- low: the color for the low (significant) p-values
- high: color for the high (less significant) p-values
- na.value is the color in which tiles which have "NA" should appear
The result of this is:
A barplot can be achieved by using the following:
represent_enrichment(enrichment = enrichment_test,
scale = "reverselog",
sample_name = "test",
plot = "bar")
Where:
- enrichment is the result from the enrichment or multisample_enrichment function
- scale can be set to either identity or log and will affect the gradient of colors
- nrow is the number of rows that should be used to plot all barplots (default is 1)
