analyze: Gene Set Collection Analysis or NetWork Analysis

In CityUHK-CompBio/HTSanalyzeR2: HTSanalyzeR2: An R package for functional annotation, network analysis and time series analysis of high-throughput data

Description

This is a generic function.

Usage

## S4 method for signature 'GSCA'
analyze(
  object,
  para = list(pValueCutoff = 0.05, pAdjustMethod = "BH", nPermutations = 1000,
    minGeneSetSize = 15, exponent = 1),
  verbose = TRUE,
  doGSOA = FALSE,
  doGSEA = TRUE,
  GSEA.by = "HTSanalyzeR2"
)

## S4 method for signature 'NWA'
analyze(object, fdr = 0.001, species, plotBumModel = FALSE, verbose = TRUE)

Arguments

object	A 'GSCA' or 'NWA'object.
para	A list of parameters for GSEA and hypergeometric tests. This argument is only for class GSCA.
verbose	A single logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE).
doGSOA	A single logical value specifying to perform gene set overrepresentation analysis(hypergeometric test) (when doGSOA=TRUE) or not (when doGSOA=FALSE).
doGSEA	A single logical value specifying to perform gene set enrichment analysis (when doGSEA=TRUE) or not (when doGSEA=FALSE).
GSEA.by	A single character value to choose which algorithm to do GSEA. Valid value could either be "HTSanalyzeR2"(default) or "fgsea". If performed by "fgsea", the result explanation please refer to fgsea.
fdr	A single numeric value specifying the false discovery for the scoring of nodes (see BioNet::scoreNodes and Dittrich et al., 2008 for details)
species	A single character value specifying the species for which the data should be read.
plotBumModel	Boolean value, whether to plot a histogram and qqplot of the p-values with the fitted model.
pValueCutoff	A single numeric value specifying the cutoff for adjusted p-values considered significant.
pAdjustMethod	A single character value specifying the p-value adjustment method to be used (see 'p.adjust' for details), default is "BH".
nPermutations	A single integer or numeric value specifying the number of permutations for deriving p-values in GSEA.
minGeneSetSize	A single integer or numeric value specifying the minimum number of elements shared by a gene set and the input total genes. Gene sets with fewer than this number are removed from both hypergeometric analysis and GSEA.
exponent	A single integer or numeric value used in weighting phenotypes in GSEA.

Value

In the end, this function will return an updated object of class GSCA or NWA. All the analyzed results could be found in slot result.

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For GSOA result of class GSCA:

Universe Size: total number of genes as background in GSOA analysis;

Gene Set Size: number of genes in the gene set;

Total Hits: number of genes in hits (interested genes);

Expected Hits: expected number of hits genes in the gene set;

Observed Hits: observed/actual number of hits genes in the gene set;

Pvalue: pvalue of the gene set in GSOA analysis;

Adjusted.Pvalue: adjusted pvalue of the gene set in GSOA analysis using user-defined p-value adjustment method;

Overlap.Gene: overlapped genes between hits and the gene set.

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For GSEA result of class GSCA:

Observed.score: Enrichment score of the gene set in GSEA analysis;

Pvalue: pvalue of the gene set in GSEA analysis;

Adjusted.Pvalue: adjusted pvalue of the gene set in GSEA analysis using user-defined p-value adjustment method;

Leading.Edge: the subset of the gene set that contribute most to the enrichment score.

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For NWA result of class NWA:

subnw: an igraph object of the enriched subnetwork;

labels: gene labels of the nodes in the enriched subnetwork.

Methods (by class)

• GSCA: The function will perform gene set collection analysis on a GSCA object and update information about these results to slot summary of class GSCA.

• NWA: The function will store the subnetwork module identified by BioNet (if species is given, labels of nodes will also be mapped from Entrez IDs to gene symbols), and update information about these results to slot summary of class NWA.

References

Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, Scott L. Pomeroy, Todd R. Golub, Eric S. Lander, and Jill P. Mesirov Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles PNAS 2005 102 (43) 15545-15550; published ahead of print September 30, 2005, doi:10.1073/pnas.0506580102

Beisser D, Klau GW, Dandekar T, Muller T, Dittrich MT. BioNet: an R-Package for the functional analysis of biological networks. Bioinformatics. 2010 Apr 15;26(8):1129-30.

Dittrich MT, Klau GW, Rosenwald A., Dandekar T and Muller T. Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics 2008 24(13):i223-i231.

Examples

# ====================================================
# Gene Set Collection Analysis Part
library(org.Hs.eg.db)
library(GO.db)
library(KEGGREST)
## load data for enrichment analyse
data(d7)
phenotype <- as.vector(d7$neg.lfc)
names(phenotype) <- d7$id

## select hits if you also want to do GSOA, otherwise ignore it
hits <-  names(phenotype[which(abs(phenotype) > 2)])

## set up a list of gene set collections
GO_MF <- GOGeneSets(species="Hs", ontologies=c("MF"))
PW_KEGG <- KeggGeneSets(species="Hs")
ListGSC <- list(GO_MF=GO_MF, PW_KEGG=PW_KEGG)

## create an object of class 'GSCA'
gsca <- GSCA(listOfGeneSetCollections = ListGSC, geneList = phenotype, hits = hits)

## do preprocessing
gsca1 <- preprocess(gsca, species="Hs", initialIDs="SYMBOL", keepMultipleMappings=TRUE,
                    duplicateRemoverMethod="max", orderAbsValue=FALSE)

## support parallel calculation using doParallel package
if (requireNamespace("doParallel", quietly=TRUE)) {
doParallel::registerDoParallel(cores=2)
} else {
}

## do hypergeometric tests and GSEA
gsca2 <- analyze(gsca1, para=list(pValueCutoff=0.01, pAdjustMethod ="BH",
                                  nPermutations=100, minGeneSetSize=10, exponent=1),
                                  doGSOA = TRUE, doGSEA = TRUE)

## summarize gsca2 and get results
summarize(gsca2)
head(getResult(gsca2)$GSEA.results$GO_MF)
head(getResult(gsca2)$HyperGeo.results$PW_KEGG)

# ===========================================================
## Not run: 
# Network Analysis
library(org.Hs.eg.db)
library(GO.db)
## load data for network analyse
data(d7)
pvalues <- d7$neg.p.value
names(pvalues) <- d7$id

## input phenotypes if you want to color nodes by it
phenotypes <- as.vector(d7$neg.lfc)
names(phenotypes) <- d7$id

## create an object of class 'NWA' with phenotypes
nwa <- NWA(pvalues=pvalues, phenotypes=phenotypes)

## do preprocessing
nwa1 <- preprocess(nwa, species="Hs", initialIDs="SYMBOL", keepMultipleMappings=TRUE,
                   duplicateRemoverMethod="max")

## create an interactome for nwa1 by downloading from BioGRID database
nwa2 <- interactome(nwa1, species="Hs", reportDir="HTSanalyzerReport", genetic=FALSE)

## analyze
nwa3 <- analyze(nwa2, fdr=0.0001, species="Hs")
## summarize nwa3
summarize(nwa3)

## End(Not run)

CityUHK-CompBio/HTSanalyzeR2 documentation built on Dec. 3, 2020, 2:35 a.m.