champ.GSEA: Do GSEA for DMP, DMR and other methylation data related...

In YuanTian1991/ChAMP: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC

Do GSEA for DMP, DMR and other methylation data related results.

Description

This function would do GSEA on the results of champ functions like DMP and DMR. However users may also add individual CpGs and genes in it. There are three method are incoporated into champ.GSEA function here. One is old Fisher Exact Test method, which will used information downloaded from MSigDB and do fisher exact test to calculated the enrichment status for each pathways. And another method is "gometh" method, which will use missMethyl package to correct the inequality between number of genes and number of CpGs, then do GSEA. The third and newest method is Empirical Bayes (ebayes) method, which does not need DMP or DMR information, but would directly calculate global test across all CpGs then do GSEA. User may assign parameter "method" as "ebayes", "gometh" or "fisher" to choose which method they want to use.

Usage

    champ.GSEA(beta=myNorm,
               DMP=myDMP[[1]],
               DMR=myDMR,
               CpGlist=NULL,
               Genelist=NULL,
               pheno=myLoad$pd$Sample_Group,
               method="fisher",
               arraytype="450K",
               Rplot=TRUE,
               adjPval=0.05,
               cores=1)

Arguments

beta	A matrix of values representing the methylation scores for each sample (M or B). Better to be imputed and normalized data. (default = myNorm)
DMP	Results from champ.DMP() function. (default = myDMP)
DMR	Results from champ.DMR() function. (default = myDMR)
CpGlist	Apart from previous parameters, if you have any other CpGs list want to do GSEA, you can input them here as a list. (default = NULL)
Genelist	Apart from previous parameters, if you have any other Gene list want to do GSEA. you can inpute them here as a list. (default = NULL)
pheno	If use ebayes method, user needs to provide phenotype information to conduct global test. (default = myLoad$pd$Sample_Group)
method	Which method would be used to do GSEA?"gometh","fisher", or"ebayes". "ebayes" is our new unbias GSEA method, you could refer to champ.ebGSEA() function to know more. (default = "fisher")
arraytype	Which kind of array your data set is? (default = "450K")
Rplot	If gometh method was chosen, should Probability Weight plot will be plotted. More information please check gometh package. (default = TRUE)
adjPval	Adjusted p value cutoff for all calculated GSEA result. (default = 0.05)
cores	Number of parallel threads/cores used in ebayes method. (default = 1)

Value

For fisher Method:

Genelist	List of pathway we get by enriching genes onto annotation database.
nOVLAP	Number of genes overlapped in your significant gene list and annotated pathways.
OR	Odds Ratio calculated for each enrichment.
P-value	Significance calculated from fisher exact test.
adjPval	Adjusted P value from "BH" method.
Genes	Name of genes enriched in each pathway.

For gometh method, the returned value are:

category	GO pathway's index.
over_represented_pvalue	The p value for genes' over representing in this pathway.
under_represented_pvalue	The p value for genes' under representing in this pathway.(Not likely to be used)
numDEInCat	Numbers of Different Methylation Genes in this pathway.
numInCat	Numbers of all genes related to this pathway.
term	The short explaination for this pathway.
ontology over_represented_adjPvalue	The ajusted over representing p value with "BH" method. User may used this one to select qualitied Pathways.

For ebayes method:

There are three list: GSEA contains all pathway's GSEA result in one list, and only significant pathways GSEA in another. EnrichedGene: contains enriched genes in each pathways. gtResult: global test result for each gene.

Below are columns for list GSEA.

nREP	Number of genes enriched in this pathway.
AUC	Area under curve from wilcox test.
P(WT)	P value detected for each pathway from Wilcox Test.
P(KPMT)	P value from Known Population Median Test
adjP	Adjusted P value for each pathway, using BH method.

Author(s)

Yuan Tian, Danyue Dong

Examples

    ## Not run: 
        myLoad <- champ.load(directory=system.file("extdata",package="ChAMPdata"))
        myNorm <- champ.norm()
        myDMP <- champ.DMP()
        myDMR <- champ.DMR()
        myGSEA <- champ.GSEA()
        
## End(Not run)

YuanTian1991/ChAMP documentation built on Feb. 21, 2023, 1:13 p.m.