GFMap {GFMaps}R Documentation

Visualization technique for interpretation of high-throughput genetic or proteomic experiments.

Description

This technique organizes genes on virtual microarray real-estate according to the prominent biological functionality in the experiment as revealed Gene Set Enrichment analysis. Consistent with enrichment analysis, biological functionality spans Biological Process,Molecular Function and cellular component from Gene Ontology ((GO-BP, GO-MF, GO-CC) as well as KEGG Pathways.

Usage

GFMap(input.ds, class.labels, option, reshuffling.type = "sample.labels", 
nperm = 100, weighted.score.type = 0, topgs = 10, reverse.sign = FALSE, 
perm.type = 0, fraction = 1, replace = FALSE, which.GeneRanking = "FC", 
is.log = FALSE, FUN = mean, bootstrap = TRUE, which.pvalue = "FDR q-val", 
pvalueCutoff = NULL, use.fast.enrichment.routine = TRUE, importance = "size",
relevance = "score", output.directory = "")

Arguments

input.ds Dataset as an object of class ExpressionSet.
class.labels factor level assignment (0 & 1) of datatype numeric
option Biological principle for functional organization e.g. GO annotation for Biological Process (BP), Molecular function (MF), Cellular component (CC) or Pathways (KEGG).
reshuffling.type Type of permutation reshuffling: "sample.labels" or "gene.labels" (default: "sample.labels")
nperm Number of random permutations (default: 100)
weighted.score.type Enrichment correlation-based weighting: 0=no weight (KS), 1=standard weigth, 2 = over-weigth (default: 1)
topgs Besides those passing test, number of top scoring gene sets used for detailed reports (default: 10)
reverse.sign Reverse direction of gene list (pos. enrichment becomes negative, etc.) (default: FALSE)
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: FALSE)
which.GeneRanking 2 metrics for Gene Ranking are available: "FC"- fold change ,"s2n"=Signal 2 noise
is.log A logical variable indicating whether the data has been logged.
FUN The summary statistics function used to calcuate fold change, the default is set as mean, the user can also use median
bootstrap Bootstraping for calculation of normalized enrichmnet score,Default=TRUE
which.pvalue 4 p-values: "FDR q-val"-False discovery rate q-values ,"NOM p-val"-Nominal p-value,"FWER p-val"- Family wise error rate p-values ,"FDR (median)"-FDR q-values from the median of the null distributions.
pvalueCutoff A numeric values between zero and one used as a p-value cutoff for p-values (default is NULL)
use.fast.enrichment.routine if true it uses a faster version to compute random perm. enrichment "GSEA.EnrichmentScore2" (default is NULL)
importance Signifcant categories are categorized on the basis of either 'size' or 'ES' (default is 'size')
relevance Assertiveness is communicated on the basis of differential expression "score" (eg FC or s2n) or on the basis of statistical significance p-value 'pvalue' (default is 'size')
output.directory Directory where to store output and results (default: .)

Details

This technique organizes genes on virtual microarray real-estate according to the prominent biological functionality in the experiment as revealed by Gene Set Enrichment analysis so as to improve Signal-to-noise ratio and hence, enhance the quality of analysis. The GFMaps is the conceptual analog of a pioneering technique in clinical studies - fMRI . In fMRI, areas of the neuro-anatomy light-up in response to stimulus, revealing the connection between anatomy and cognitive function. Similarly in a GFMaps, parts of the genome light-up to indicate connection with observed phenotypical differential.

Value

The results of the method are stored in the "output.directory" specified by the user as part of the input parameters. A list containing the 2 dataframes & one image(.JPEG or png)

Summary if bootstrap=TRUE then it contains Gene Set name, Gene set definition, actual gene count for each term tested, Enrichment Score, Normalized Enrichment score (NES) and P value for each category term tested. The rows of the summary table are sorted by the NES values (from maximum positive or negative NES to minimum). Else (bootstrap=FALSE) it will not contain NES, pvalue and rows of the summary table are sorted by Enrichment Score
GeneMatrix Tt displays the list of genes present in the ExpressionSet along with its details that includes its score (fold change or Signal to noise ratio), P value, Gene Set and Gene Set definition. Each gene is represented by its Entrez Gene ID. Genes that do not map to any Gene Set shows NAs in the corresponding columns
Geneomic Function Map(GFMap) An image (pdf) that integrate microarray data with biological annotations in terms of Gene Ontology and pathway information and organize genes in such a way that most assertive genes (i.e high score(FC or S2n) or less pvalue) in biological component are placed at the center of their field, with the less assertive members placed peripherally around this core in successive layers of diminishing assertiveness.

Note

This technique can only handle one factor two level microarray experiment

Author(s)

Dr. Sanjay Bhatikar sanjay.bhatikar@biobase.in, Kanika Arora kanika.arora@biobase.in

References

See Also

R-GSEA http://www.broad.mit.edu/cancer/software/gsea/wiki/index.php/R-GSEA_Readme

Examples

require(ALL)
data(ALL, package = "ALL")
Bcell <- grep("^B", as.character(ALL$BT))
bcrAblOrNegIdx <- which(match(as.character(ALL$mol),c("NEG", "ALL1/AF4"),nomatch=0)!=0)
bcrAblOrNeg <- ALL[, intersect(Bcell, bcrAblOrNegIdx)]
class.labels<-(as.numeric((bcrAblOrNeg$mol.biol!="NEG")))
res<-GFMap(input.ds=bcrAblOrNeg,class.labels=class.labels,nperm=20,option="KEGG",topgs = 20,
which.GeneRanking="s2n")

[Package GFMaps version 1.1 Index]