pathWave {PathWave} | R Documentation |
Performs an enrichment analysis on optimally arranged grids. Features are generated using a Haar wavelet transform.
pathWave(x, y, optimalM, mTest = TRUE, mTestMethod = "Bonferroni", pvalCutoff = 0.01, genes=NULL, diffReac = 5, nperm = 10000, verbose = TRUE)
x |
Matrix of expression values. Names of row elements correspond to elements in optimalM. Columns are data samples. |
y |
Class factor for x. Should consist of only two classes and length(y) should correspond to ncol(x). |
optimalM |
List of optimal arranged grids as returned by function pwOptGrids(). See details. |
mTest |
Should the results be corrected for multiple testing (default TRUE)? |
mTestMethod |
Method for multiple testing correction (default "Bonferroni") as defined by package multtest. |
pvalCutoff |
Significance level (default 0.01). |
genes |
List of genes for each reaction per pathway as returned by pwKEGGxml(). See details. |
diffReac |
Pathways with how many differentially expressed reaction should be considered (default 5)? |
nperm |
Number of permutations that should be used to estimate the underlying distribution (default 10,000)? |
verbose |
Should the progress in permutations be printed after every 100 permutations? |
PathWave returns a list of class PathWave:
pathwayID |
The ID of enriched pathways. |
p.value |
P-value of the enriched pathway. Correspond to the p-value of the highest ranked feature. |
score |
Size of the feature with which the score was calculated. This is a measurement of the size of the significant pattern. |
reactions |
Reactions from which the significant features were calculated by a Haar wavelet transform. |
reaction.p.value |
The p-values of the reactions. Calculated by a t-test on x. |
reaction.regulation |
The regulation of the reactions: +1 up-regulated, -1 down-regulated, 0 not differentially regulated in class levels(y)[1]. |
feature.p.value |
The p-values of the features. |
feature |
List of the significant features. |
In List features:
Each sub-matrix is listed from which the significant features were derived.
Gunnar Schramm
iChip
See Also pwOptGrids
,pwKEGGxml
#build test optimal matrices: test.opM=list() test.opM[["test1"]]=list(M=matrix(sample(c(paste("test1:R",1:6,sep=""),rep("0",19))),nrow=5)) test.opM[["test2"]]=list(M=matrix(sample(c(paste("test2:R",7:11,sep=""),rep("0",11))),nrow=4)) #generate test class factor for two classes test.y=as.factor(c(rep("test.1",20),rep("test.2",20))) #generate test expression matrix with two classes test.x1=matrix(rnorm(11*length(test.y)),ncol=length(test.y),dimnames=list(paste("test1:R",1:11,sep=""),paste("sample",1:length(test.y),sep=""))) test.x2=matrix(rnorm(11*length(test.y)),ncol=length(test.y),dimnames=list(paste("test2:R",1:11,sep=""),paste("sample",1:length(test.y),sep=""))) #all reactions of the second class are up-regulated in the first pathway test.x1[,test.y==levels(test.y)[2]]=test.x1[,test.y==levels(test.y)[2]] + 2 test.x=rbind(test.x1,test.x2) #build gene list test.gene=list() test1.genes=as.character(1:6) test2.genes=as.character(1:5) names(test1.genes)=paste("test1:R",1:6,sep="") names(test2.genes)=paste("test2:R",7:11,sep="") test.gene[["genes"]]=list(test1=test1.genes,test2=test2.genes) #start pathwave test.result=pathWave(test.x,test.y,optimalM=test.opM,pvalCutoff=0.05,genes=test.gene$genes,diffReac=1)