ACSN description"
In ACSNMineR: Gene Enrichment Analysis from ACSN Maps or GMT Files

library(ACSNMineR)
knitr::opts_chunk$set(fig.dpi = 96)

ACSN Enrichment

Description

ACSNMineR is an R package, freely available.

ACSN stands for Atlas of Cancer Signaling Networks, and shows gene interaction in pathways relevant to cancer.

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

2.1.1 Import gmt files

2.1.2 Perform analysis

2.2. Data vizualization

2.2.1. Heatmaps

2.2.2. Barplots

Usage

Pathway analysis

Import gmt files

Gmt files can be imported thanks to the format_from_gmt function. Let's use saved data from the package:

# Retrieve path of the example gmt
file<-system.file("extdata", "cellcycle_short.gmt", package = "ACSNMineR")
# Then import it
gmt<-ACSNMineR::format_from_gmt(file)

knitr::kable(gmt[,1:10])

ACSN maps are built-in and can easily be accessed through ACSNEnrcihment::ACSN_maps:

# Name of available maps:
names(ACSNMineR::ACSN_maps)

````
`````r
knitr::kable(names(ACSNMineR::ACSN_maps))
````

````r
#And accessing them:
ACSNMineR::ACSN_maps$CellCycle
````
````r
knitr::kable(head(ACSNMineR::ACSN_maps$CellCycle[,1:10]), row.names = FALSE)

The gene set that was used for tests is the following:

ACSNMineR::genes_test

knitr::kable(ACSNMineR::genes_test)

Perform analysis

Gene set enrichment for a single set can be performed by calling:

Example<-ACSNMineR::enrichment(ACSNMineR::genes_test,
    min_module_size = 10, 
    threshold = 0.05,
    maps = list(cellcycle = ACSNMineR::ACSN_maps$CellCycle))

```r knitr::kable(Example,row.names = FALSE)

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:

````r
Example<-ACSNMineR::multisample_enrichment(Genes_by_sample = list(set1 = ACSNMineR::genes_test[-1],
                                                              set2 = ACSNMineR::genes_test[-2]),
    maps = ACSNMineR::ACSN_maps$CellCycle,
    min_module_size = 10,
    cohort_threshold = FALSE)

```r print(Example[[1]])

```r
knitr::kable(Example[[1]],row.names = FALSE)

```r print(Example[[2]])

```r
knitr::kable(Example[[2]],row.names = FALSE)

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.

ACSNMineR::represent_enrichment(enrichment = list(
    SampleA = ACSNMineR::enrichment_test[1:10,], 
    SampleB = ACSNMineR::enrichment_test[3:10,]),
    plot = "heatmap", 
    scale = "reverselog",
    low = "steelblue" , high ="white",
    na.value = "grey")+theme(axis.text = element_text(size = 6,angle = 45),
                             legend.text = element_text(size = 6),
                             legend.title = element_text(size = 8))

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

Barplots

A barplot can be achieved by using the following:

ACSNMineR::represent_enrichment(enrichment = list(
    SampleA = ACSNMineR::enrichment_test[1:10,], 
    SampleB = ACSNMineR::enrichment_test[3:10,]),
    plot = "bar", 
    scale = "reverselog")

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)