GSEA: Run Gene Set enrichment Analysis
In GSEA-MSigDB/GSEA_R: Gene Set Enrichment Analysis (GSEA)

Description Usage Arguments Details Value Examples

'GSEA' is the main function to perform gene set enrichment analysis of a dataset

GSEA(input.ds, input.cls, input.chip = "NOCHIP", gene.ann = "", gs.db,
  gs.ann = "", output.directory = getwd(),
  doc.string = "gsea_result", reshuffling.type = "sample.labels",
  nperm = 1000, weighted.score.type = 1, nom.p.val.threshold = -1,
  fwer.p.val.threshold = -1, fdr.q.val.threshold = 0.25, topgs = 20,
  adjust.FDR.q.val = F, gs.size.threshold.min = 15,
  gs.size.threshold.max = 500, reverse.sign = F, preproc.type = 0,
  random.seed = as.integer(Sys.time()), perm.type = 0, fraction = 1,
  replace = F, collapse.dataset = FALSE,
  collapse.mode = "NOCOLLAPSE", save.intermediate.results = F,
  use.fast.enrichment.routine = T, gsea.type = "GSEA",
  rank.metric = "S2N")

`input.ds`	Input gene expression dataset file in GCT format or RNK format if preranked is specified to gsea.type
`input.cls`	Input class vector (phenotype) file in CLS format
`input.chip`	If collapse.dataset = TRUE, read in a CHIP formatted gene mapping file to convert dataset to gene symbols
`gene.ann`	Depreciated parameter. Gene microarray annotation file (Affymetrix Netaffyx *.csv format) (default: none)
`gs.db`	Gene set database in GMT format
`gs.ann`	Depreciated parameter. Gene Set database annotation file (default: none)
`output.directory`	Directory where to store output and results (default: .)
`doc.string`	Documentation string used as a prefix to name result files (default: 'gsea_result')
`reshuffling.type`	Type of permutation reshuffling: 'sample.labels' or 'gene.labels' (default: 'sample.labels')
`nperm`	Number of random permutations (default: 1000)
`weighted.score.type`	Enrichment correlation-based weighting: 0=no weight (KS), 1=standard weigth, 2 = over-weigth (default: 1)
`nom.p.val.threshold`	Significance threshold for nominal p-vals for gene sets (default: -1, no thres)
`fwer.p.val.threshold`	Significance threshold for FWER p-vals for gene sets (default: -1, no thres)
`fdr.q.val.threshold`	Significance threshold for FDR q-vals for gene sets (default: 0.25)
`topgs`	Besides those passing test, number of top scoring gene sets used for detailed reports (default: 20)
`adjust.FDR.q.val`	Adjust the FDR q-vals (default: F)
`gs.size.threshold.min`	Minimum size (in genes) for database gene sets to be considered (default: 15)
`gs.size.threshold.max`	Maximum size (in genes) for database gene sets to be considered (default: 500)
`reverse.sign`	Reverse direction of gene list (pos. enrichment becomes negative, etc.) (default: F)
`preproc.type`	Preprocessing normalization: 0=none, 1=col(z-score)., 2=col(rank) and row(z-score)., 3=col(rank). (default: 0)
`random.seed`	Random number generator seed. (default: use system time)
`perm.type`	Permutation type: 0 = unbalanced, 1 = balanced. For experts only (default: 0)
`fraction`	Subsampling fraction. Set to 1.0 (no resampling). For experts only (default: 1.0)
`replace`	Resampling mode (replacement or not replacement). For experts only (default: F)
`collapse.dataset`	collapse dataset from user specified identifiers to Gene Symbols (default: FALSE)
`collapse.mode`	Method for collapsing the dataset, accepts 'max', 'median', 'mean', 'sum', (default: NOCOLLAPSE)
`save.intermediate.results`	save intermediate results files including ranks and permutations
`use.fast.enrichment.routine`	If true it uses a faster GSEA.EnrichmentScore2 to compute random perm. enrichment
`gsea.type`	Mode to run GSEA. Specify either 'GSEA' for standard mode, or 'preranked' to allow parsing of .RNK file
`rank.metric`	Method for ranking genes. Accepts either signal-to-noise ratio 'S2N' or 'ttest' (default: S2N)

This is a methodology for the analysis of global molecular profiles called Gene Set Enrichment Analysis (GSEA). It determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA operates on all genes from an experiment, rank ordered by the signal to noise ratio and determines whether members of an a priori defined gene set are nonrandomly distributed towards the top or bottom of the list and thus may correspond to an important biological process. To assess significance the program uses an empirical permutation procedure to test deviation from random that preserves correlations between genes. For details see Subramanian et al 2005.

The results of the method are stored in the 'output.directory' specified by the user as part of the input parameters. The results files are: - Two tab-separated global result text files (one for each phenotype). These files are labeled according to the doc string prefix and the phenotype name from the CLS file: <doc.string>.SUMMARY.RESULTS.REPORT.<phenotype>.txt - One set of global plots. They include a.- gene list correlation profile, b.- global observed and null densities, c.- heat map for the entire sorted dataset, and d.- p-values vs. NES plot. These plots are in a single JPEG file named <doc.string>.global.plots.<phenotype>.jpg. When the program is run interactively these plots appear on a window in the R GUI. - A variable number of tab-separated gene result text files according to how many sets pass any of the significance thresholds ('nom.p.val.threshold,' 'fwer.p.val.threshold,' and 'fdr.q.val.threshold') and how many are specified in the 'topgs' parameter. These files are named: <doc.string>.<gene set name>.report.txt. - A variable number of gene set plots (one for each gene set report file). These plots include a.- Gene set running enrichment 'mountain' plot, b.- gene set null distribution and c.- heat map for genes in the gene set. These plots are stored in a single JPEG file named <doc.string>.<gene set name>.jpg. The format (columns) for the global result files is as follows. GS : Gene set name. SIZE : Size of the set in genes. SOURCE : Set definition or source. ES : Enrichment score. NES : Normalized (multiplicative rescaling) normalized enrichment score. NOM p-val : Nominal p-value (from the null distribution of the gene set). FDR q-val: False discovery rate q-values FWER p-val: Family wise error rate p-values. Tag peak. Gene enrichment signal strength. FDR (median): FDR q-values from the median of the null distributions. glob.p.val: P-value using a global statistic (number of sets above the set's NES). The rows are sorted by the NES values (from maximum positive or negative NES to minimum) The format (columns) for the gene set result files is as follows. #: Gene number in the (sorted) gene set GENE : gene name. For example the probe accession number, gene symbol or the gene identifier gin the dataset. SYMBOL : gene symbol from the gene annotation file. DESC : gene description (title) from the gene annotation file. LIST LOC : location of the gene in the sorted gene list. S2N : signal to noise ratio (correlation) of the gene in the gene list. RES : value of the running enrichment score at the gene location. CORE_ENRICHMENT: is this gene is the 'core enrichment' section of the list? Yes or No variable specifying in the gene location is before (positive ES) or after (negative ES) the running enrichment peak. The rows are sorted by the gene location in the gene list. The function call to GSEA returns a two element list containing the two global result reports as data frames ($report1, $report2). results1: Global output report for first phenotype result2: Global putput report for second phenotype

GSEA(input.ds = system.file('extdata', 'Leukemia_hgu95av2.gct', package = 'GSEA', mustWork = TRUE),
input.cls = system.file('extdata', 'Leukemia.cls', package = 'GSEA', mustWork = TRUE),
input.chip = system.file('extdata', 'Human_AFFY_HG_U95_MSigDB_7_0_final.chip',
package = 'GSEA', mustWork = TRUE), gs.db = system.file('extdata',
'h.all.v7.0.symbols.gmt', package = 'GSEA', mustWork = TRUE),
collapse.dataset = TRUE, collapse.mode = 'max')