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ACSNMineR

Description

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:

1. This description

2. Usage section

2.1. Pathway analysis

2.2. Data vizualization

2.2.1. Heatmaps

2.2.2. Barplots

Usage

Pathway analysis

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

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

Data visualization

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

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[1:10,],

SampleB = enrichment_test[3:10,]),
plot = "heatmap", 
scale = "log",
low = "steelblue" , high ="white",
na.value = "grey")

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:

Barplots

A barplot can be achieved by using the following:

represent_enrichment(enrichment = list(SampleA = enrichment_test[1:10,],

'SampleB = enrichment_test[3:10,]),
plot = "bar", 
scale = "log",
nrow = 1)

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)