Usage

To start using SLAPenrich, first load the sparse binary matrix data (EM). See the example LUAD_CaseStudy included in the package:

data(LUAD_CaseStudy)
class(LUAD_CaseStudy)
LUAD_CaseStudy[1:5, 1:5]

Then load the list of pathway gene sets (PATH_COLLECTION). SLAPenrich provides two collections of pathways from two databases: KEGG and PathwayCommons. For example, to load the pathways from KEGG type:

data(SLAPE.MSigDB_KEGG_hugoUpdated)
class(KEGG_PATH)
names(KEGG_PATH)

Finally, load information on the exonic lentgh of the genes (GeneLenghts):

data(SLAPE.all_genes_exonic_content_block_lengths_ensemble)
class(GECOBLenghts)
head(GECOBLenghts)

To run the analysis, type the SLAPE.analyse function:

mySLAPE_analysis = SLAPE.analyse(EM=LUAD_CaseStudy, PATH_COLLECTION=KEGG_PATH, GeneLenghts = GECOBLenghts)
class(mySLAPE_analysis)
names(mySLAPE_analysis)

Plot your core components summary with SLAPE.core_components. This will generate a series of pdf files in the PATH folder. Each pdf file will represent a core component (i.e. a group of related patways):

SLAPE.core_components(PATH = "./", PFP=mySLAPE_analysis, EM=LUAD_CaseStudy, PATH_COLLECTION=KEGG_PATH)

To save your results as a table, run SLAPE.write.table:

SLAPE.write.table(filename = "mySLAPE_analysis.csv", PFP=mySLAPE_analysis, EM=LUAD_CaseStudy, PATH_COLLECTION=KEGG_PATH, GeneLenghts = GECOBLenghts)

Check here for a full documentation of SLAPenrich.