| Title: | Detect tissue heterogeneity in expression profiles with gene sets |
|---|---|
| Description: | BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance. |
| Authors: | Jitao David Zhang [cre, aut], Laura Badi [aut], Gregor Sturm [aut], Roland Ambs [aut], Iakov Davydov [aut] |
| Maintainer: | Jitao David Zhang <[email protected]> |
| License: | GPL (>=3) + file LICENSE |
| Version: | 1.33.0 |
| Built: | 2024-07-20 04:57:36 UTC |
| Source: | https://github.com/bioc/BioQC |
Subsetting GmtList object into another GmtList object
## S3 method for class 'GmtList' x[i, drop = FALSE]## S3 method for class 'GmtList' x[i, drop = FALSE]
x |
A GmtList object |
i |
Index to subset |
drop |
In case only one element remains, should a list representing the single geneset returned? Default: FALSE |
myGmtList <- GmtList(list(gs1=letters[1:3], gs2=letters[3:4], gs3=letters[4:5])) myGmtList[1:2] myGmtList[1] ## default behaviour: not dropping myGmtList[1,drop=TRUE] ## force droppingmyGmtList <- GmtList(list(gs1=letters[1:3], gs2=letters[3:4], gs3=letters[4:5])) myGmtList[1:2] myGmtList[1] ## default behaviour: not dropping myGmtList[1,drop=TRUE] ## force dropping
Subsetting GmtList object to fetch one gene-set
## S3 method for class 'GmtList' x[[i]]## S3 method for class 'GmtList' x[[i]]
x |
A GmtList object |
i |
The index to subset |
myGmtList <- GmtList(list(gs1=letters[1:3], gs2=letters[3:4], gs3=letters[4:5])) myGmtList[[1]]myGmtList <- GmtList(list(gs1=letters[1:3], gs2=letters[3:4], gs3=letters[4:5])) myGmtList[[1]]
Absolute base-10 logarithm of p-values
absLog10p(x)absLog10p(x)
x |
Numeric vector or matrix The function returns the absolute values of base-10 logarithm of p-values. |
The logarithm transformation of p-values is commonly used to visualize results from statistical tests. Although it may cause misunderstanding and therefore its use is disapproved by some experts, it helps to visualize and interpret results of statistical tests intuitively.
The function transforms p-values with base-10 logarithm, and returns its absolute value. The choice of base 10 is driven by the simplicity of interpreting the results.
Numeric vector or matrix.
Jitao David Zhang <[email protected]>
testp <- runif(1000, 0, 1) testp.al <- absLog10p(testp) print(head(testp)) print(head(testp.al))testp <- runif(1000, 0, 1) testp.al <- absLog10p(testp) print(head(testp)) print(head(testp.al))
Append a GmtList object to another one
appendGmtList(gmtList, newGmtList, ...)appendGmtList(gmtList, newGmtList, ...)
gmtList |
A |
newGmtList |
Another |
... |
Further |
A new GmtList list, with all elements in the input appended in the given order
test_gmt_file<- system.file("extdata/test.gmt", package="BioQC") testGmtList1 <- readGmt(test_gmt_file, namespace="test1") testGmtList2 <- readGmt(test_gmt_file, namespace="test2") testGmtList3 <- readGmt(test_gmt_file, namespace="test3") testGmtAppended <- appendGmtList(testGmtList1, testGmtList2, testGmtList3)test_gmt_file<- system.file("extdata/test.gmt", package="BioQC") testGmtList1 <- readGmt(test_gmt_file, namespace="test1") testGmtList2 <- readGmt(test_gmt_file, namespace="test2") testGmtList3 <- readGmt(test_gmt_file, namespace="test3") testGmtAppended <- appendGmtList(testGmtList1, testGmtList2, testGmtList3)
Convert a list of gene symbols into a gmtlist
as.GmtList(list, description = NULL, uniqGenes = TRUE, namespace = NULL)as.GmtList(list, description = NULL, uniqGenes = TRUE, namespace = NULL)
list |
A named list with character vectors of genes. Names will become names of gene sets; character vectors will become genes |
description |
Character, description of gene-sets. The value will be expanded to the same length of the list. |
uniqGenes |
Logical, whether redundant genes should be made unique? |
namespace |
Character or |
testVec <- list(GeneSet1=c("AKT1", "AKT2"), GeneSet2=c("MAPK1", "MAPK3"), GeneSet3=NULL) testVecGmtlist <- as.GmtList(testVec)testVec <- list(GeneSet1=c("AKT1", "AKT2"), GeneSet2=c("MAPK1", "MAPK3"), GeneSet3=NULL) testVecGmtlist <- as.GmtList(testVec)
An S4 class to hold a list of indices, with the possibility to specify the offset of the indices. IndexList and SignedIndexList extend this class
offsetAn integer specifying the value of first element. Default 1
keepNALogical, whether NA is kept during construction
keepDupLogical, whether duplicated values are kept during construction
Shannon entropy
entropy(vector)entropy(vector)
vector |
A vector of numbers, or characters. Discrete probability of each item is calculated and the Shannon entropy is returned. |
Shannon entropy
Shannon entropy can be used as measures of gene expression specificity, as well as measures of tissue diversity and specialization. See references below.
We use 2 as base for the entropy calculation, because in this
base the unit of entropy is bit.
Jitao David Zhang <[email protected]>
Martinez and Reyes-Valdes (2008) Defining diversity, specialization, and gene specificity in transcriptomes through information theory. PNAS 105(28):9709–9714
myVec0 <- 1:9 entropy(myVec0) ## log2(9) myVec1 <- rep(1, 9) entropy(myVec1) entropy(LETTERS) entropy(rep(LETTERS, 5))myVec0 <- 1:9 entropy(myVec0) ## log2(9) myVec1 <- rep(1, 9) entropy(myVec1) entropy(LETTERS) entropy(rep(LETTERS, 5))
Entropy-based sample diversity
entropyDiversity(mat, norm = FALSE)entropyDiversity(mat, norm = FALSE)
mat |
A matrix (usually an expression matrix), with genes (features) in rows and samples in columns. |
norm |
Logical, whether the diversity should be normalized by |
A vector as long as the column number of the input matrix
Martinez and Reyes-Valdes (2008) Defining diversity, specialization, and gene specificity in transcriptomes through information theory. PNAS 105(28):9709–9714
entropy and sampleSpecialization
myMat <- rbind(c(3,4,5),c(6,6,6), c(0,2,4)) entropyDiversity(myMat) entropyDiversity(myMat, norm=TRUE) myRandomMat <- matrix(runif(1000), ncol=20) entropyDiversity(myRandomMat) entropyDiversity(myRandomMat, norm=TRUE)myMat <- rbind(c(3,4,5),c(6,6,6), c(0,2,4)) entropyDiversity(myMat) entropyDiversity(myMat, norm=TRUE) myRandomMat <- matrix(runif(1000), ncol=20) entropyDiversity(myRandomMat) entropyDiversity(myRandomMat, norm=TRUE)
Entropy-based gene-expression specificity
entropySpecificity(mat, norm = FALSE)entropySpecificity(mat, norm = FALSE)
mat |
A matrix (usually an expression matrix), with genes (features) in rows and samples in columns. |
norm |
Logical, whether the specificity should be normalized by |
A vector of the length of the row number of the input matrix, namely the specificity score of genes.
Martinez and Reyes-Valdes (2008) Defining diversity, specialization, and gene specificity in transcriptomes through information theory. PNAS 105(28):9709–9714
myMat <- rbind(c(3,4,5),c(6,6,6), c(0,2,4)) entropySpecificity(myMat) entropySpecificity(myMat, norm=TRUE) myRandomMat <- matrix(runif(1000), ncol=20) entropySpecificity(myRandomMat) entropySpecificity(myRandomMat, norm=TRUE)myMat <- rbind(c(3,4,5),c(6,6,6), c(0,2,4)) entropySpecificity(myMat) entropySpecificity(myMat, norm=TRUE) myRandomMat <- matrix(runif(1000), ncol=20) entropySpecificity(myRandomMat) entropySpecificity(myRandomMat, norm=TRUE)
Filter a GmtList by size
filterBySize(x, min, max)filterBySize(x, min, max)
x |
A |
min |
Numeric, gene-sets with fewer genes than |
max |
Numeric, gene-sets with more genes than |
A GmtList object with sizes (count of genes) between min and max (inclusive).
Filter rows of p-value matrix under the significance threshold
filterPmat(x, threshold)filterPmat(x, threshold)
x |
A matrix of p-values. It must be raw p-values and should not be transformed (e.g. logarithmic). |
threshold |
A numeric value, the minimal p-value used to filter
rows. If missing, given the values of |
Matrix of p-values. If no line is left, a empty matrix of the same dimension as input will be returned.
Jitao David Zhang <[email protected]>
set.seed(1235) testMatrix <- matrix(runif(100,0,1), nrow=10) ## filtering (testMatrix.filter <- filterPmat(testMatrix, threshold=0.05)) ## more strict filtering (testMatrix.strictfilter <- filterPmat(testMatrix, threshold=0.01)) ## no filtering (testMatrix.nofilter <- filterPmat(testMatrix))set.seed(1235) testMatrix <- matrix(runif(100,0,1), nrow=10) ## filtering (testMatrix.filter <- filterPmat(testMatrix, threshold=0.05)) ## more strict filtering (testMatrix.strictfilter <- filterPmat(testMatrix, threshold=0.01)) ## no filtering (testMatrix.nofilter <- filterPmat(testMatrix))
Getting leading-edge indices from a vector
getLeadingEdgeIndexFromVector( x, index, comparison = c("greater", "less"), reference = c("background", "geneset") ) getLeadingEdgeIndexFromMatrix( x, index, comparison = c("greater", "less"), reference = c("background", "geneset") )getLeadingEdgeIndexFromVector( x, index, comparison = c("greater", "less"), reference = c("background", "geneset") ) getLeadingEdgeIndexFromMatrix( x, index, comparison = c("greater", "less"), reference = c("background", "geneset") )
x |
A numeric vector ( |
index |
An integer vector, indicating the indices of genes in a gene-set. |
comparison |
Character string, are values greater than or less than the reference value considered as leading-edge? This depends on the type of value requested by the user in |
reference |
Character string, which reference is used? If |
An integer vector, indicating the indices of leading-edge genes.
getLeadingEdgeIndexFromMatrix: x is a matrix.
myProfile <- c(rnorm(5, 3), rnorm(15, -3), rnorm(100, 0)) getLeadingEdgeIndexFromVector(myProfile, 1:20) getLeadingEdgeIndexFromVector(myProfile, 1:20, comparison="less") getLeadingEdgeIndexFromVector(myProfile, 1:20, comparison="less", reference="geneset") myProfile2 <- c(rnorm(15, 3), rnorm(5, -3), rnorm(100, 0)) myProfileMat <- cbind(myProfile, myProfile2) getLeadingEdgeIndexFromMatrix(myProfileMat, 1:20) getLeadingEdgeIndexFromMatrix(myProfileMat, 1:20, comparison="less") getLeadingEdgeIndexFromMatrix(myProfileMat, 1:20, comparison="less", reference="geneset")myProfile <- c(rnorm(5, 3), rnorm(15, -3), rnorm(100, 0)) getLeadingEdgeIndexFromVector(myProfile, 1:20) getLeadingEdgeIndexFromVector(myProfile, 1:20, comparison="less") getLeadingEdgeIndexFromVector(myProfile, 1:20, comparison="less", reference="geneset") myProfile2 <- c(rnorm(15, 3), rnorm(5, -3), rnorm(100, 0)) myProfileMat <- cbind(myProfile, myProfile2) getLeadingEdgeIndexFromMatrix(myProfileMat, 1:20) getLeadingEdgeIndexFromMatrix(myProfileMat, 1:20, comparison="less") getLeadingEdgeIndexFromMatrix(myProfileMat, 1:20, comparison="less", reference="geneset")
Calculate the Gini index of a numeric vector.
gini(x)gini(x)
x |
A numeric vector. |
The Gini index (Gini coefficient) is a measure of statistical dispersion. A Gini coefficient of zero expresses perfect equality where all values are the same. A Gini coefficient of one expresses maximal inequality among values.
A numeric value between 0 and 1.
Jitao David Zhang <[email protected]>
Gini. C. (1912) Variability and Mutability, C. Cuppini, Bologna 156 pages.
testValues <- runif(100) gini(testValues)testValues <- runif(100) gini(testValues)
Convert a list to a GmtList object
GmtList(list)GmtList(list)
list |
A list of genesets; each geneset is a list of at least three fields: 'name', 'desc', and 'genes'. 'name' and 'desc' contains one character string ('desc' can be NULL while 'name' cannot), and 'genes' can be either NULL or a character vector. In addition, 'namespace' is accepted to represent the namespace. For convenience, the function also accepts a list of character vectors, each containing a geneset. In this case, the function works as a wrapper of |
If a list of gene symbols need to be converted into a GmtList, use 'as.GmtList' instead
testList <- list(list(name="GS_A", desc=NULL, genes=LETTERS[1:3]), list(name="GS_B", desc="gene set B", genes=LETTERS[1:5]), list(name="GS_C", desc="gene set C", genes=NULL)) testGmt <- GmtList(testList) # as wrapper of as.GmtList testGeneList <- list(GS_A=LETTERS[1:3], GS_B=LETTERS[1:5], GS_C=NULL) testGeneGmt <- GmtList(testGeneList)testList <- list(list(name="GS_A", desc=NULL, genes=LETTERS[1:3]), list(name="GS_B", desc="gene set B", genes=LETTERS[1:5]), list(name="GS_C", desc="gene set C", genes=NULL)) testGmt <- GmtList(testList) # as wrapper of as.GmtList testGeneList <- list(GS_A=LETTERS[1:3], GS_B=LETTERS[1:5], GS_C=NULL) testGeneGmt <- GmtList(testGeneList)
An S4 class to hold geneset in the GMT file in a list, each item in the list is in in turn a list containing following items: name, desc, and genes.
Convert gmtlist into a list of signed genesets
gmtlist2signedGenesets( gmtlist, posPattern = "_UP$", negPattern = "_DN$", nomatch = c("ignore", "pos", "neg") )gmtlist2signedGenesets( gmtlist, posPattern = "_UP$", negPattern = "_DN$", nomatch = c("ignore", "pos", "neg") )
gmtlist |
A gmtlist object, probably read-in by |
posPattern |
Regular expression pattern of positive gene sets. It is trimmed from the original name to get the stem name of the gene set. See examples below. |
negPattern |
Regular expression pattern of negative gene sets. It is trimmed from the original name to get the stem name of the gene set. See examples below. |
nomatch |
Options to deal with gene sets that match neither positive nor negative patterns. ignore: they will be ignored (but not discarded, see details below); pos: they will be counted as positive signs; neg: they will be counted as negative signs |
An S4-object of SignedGenesets, which is a list of signed_geneset, each being a two-item list; the first item is 'pos', containing a character vector of positive genes; and the second item is 'neg', containing a character vector of negative genes.
Gene set names are detected whether they are positive or negative. If neither positive nor negative, nomatch will determine how will they be interpreted. In case of pos (or neg), such genesets will be treated as positive (or negative) gene sets.In case nomatch is set to ignore, the gene set will appear in the returned values with both positive and negative sets set to NULL.
testInputList <- list(list(name="GeneSetA_UP",genes=LETTERS[1:3]), list(name="GeneSetA_DN", genes=LETTERS[4:6]), list(name="GeneSetB", genes=LETTERS[2:4]), list(name="GeneSetC_DN", genes=LETTERS[1:3]), list(name="GeneSetD_UP", genes=LETTERS[1:3])) testOutputList.ignore <- gmtlist2signedGenesets(testInputList, nomatch="ignore") testOutputList.pos <- gmtlist2signedGenesets(testInputList, nomatch="pos") testOutputList.neg <- gmtlist2signedGenesets(testInputList, nomatch="neg")testInputList <- list(list(name="GeneSetA_UP",genes=LETTERS[1:3]), list(name="GeneSetA_DN", genes=LETTERS[4:6]), list(name="GeneSetB", genes=LETTERS[2:4]), list(name="GeneSetC_DN", genes=LETTERS[1:3]), list(name="GeneSetD_UP", genes=LETTERS[1:3])) testOutputList.ignore <- gmtlist2signedGenesets(testInputList, nomatch="ignore") testOutputList.pos <- gmtlist2signedGenesets(testInputList, nomatch="pos") testOutputList.neg <- gmtlist2signedGenesets(testInputList, nomatch="neg")
Gene-set descriptions
gsDesc(x)gsDesc(x)
x |
A |
Descriptions as a vector of character strings of the same length as x
Gene-set gene counts
gsSize is the synonym of gsGeneCount
gsGeneCount(x, uniqGenes = TRUE) gsSize(x, uniqGenes = TRUE)gsGeneCount(x, uniqGenes = TRUE) gsSize(x, uniqGenes = TRUE)
x |
A |
uniqGenes |
Logical, whether only unique genes are counted |
Gene counts (aka gene-set sizes) as a vector of integer of the same length as x
Gene-set member genes
gsGenes(x)gsGenes(x)
x |
A |
A list of genes as character strings of the same length as x
Gene-set names
gsName(x)gsName(x)
x |
A |
Names as a vector of character strings of the same length as x
Gene-set namespaces
gsNamespace(x)gsNamespace(x)
x |
A |
Namespaces as a vector of character strings of the same length as x
gsNamespace<- is the synonym of setGsNamespace
gsNamespace(x) <- valuegsNamespace(x) <- value
x |
A |
value |
|
Whether namespace is set
hasNamespace(x)hasNamespace(x)
x |
A |
Logical, whether all gene-sets have the field namespace set
Convert a list to an IndexList object
IndexList(object, ..., keepNA = FALSE, keepDup = FALSE, offset = 1L) ## S4 method for signature 'numeric' IndexList(object, ..., keepNA = FALSE, keepDup = FALSE, offset = 1L) ## S4 method for signature 'logical' IndexList(object, ..., keepNA = FALSE, keepDup = FALSE, offset = 1L) ## S4 method for signature 'list' IndexList(object, keepNA = FALSE, keepDup = FALSE, offset = 1L)IndexList(object, ..., keepNA = FALSE, keepDup = FALSE, offset = 1L) ## S4 method for signature 'numeric' IndexList(object, ..., keepNA = FALSE, keepDup = FALSE, offset = 1L) ## S4 method for signature 'logical' IndexList(object, ..., keepNA = FALSE, keepDup = FALSE, offset = 1L) ## S4 method for signature 'list' IndexList(object, keepNA = FALSE, keepDup = FALSE, offset = 1L)
object |
Either a list of unique integer indices, NULL and logical vectors (of same lengths), or a numerical vector or a logical vector. NA is discarded. |
... |
If |
keepNA |
Logical, whether NA indices should be kept or not. Default: FALSE (removed) |
keepDup |
Logical, whether duplicated indices should be kept or not. Default: FALSE (removed) |
offset |
Integer, the starting index. Default: 1 (as in the convention of R) |
The function returns a list of vectors
testList <- list(GS_A=c(1,2,3,4,3), GS_B=c(2,3,4,5), GS_C=NULL, GS_D=c(1,3,5,NA), GS_E=c(2,4)) testIndexList <- IndexList(testList) IndexList(c(FALSE, TRUE, TRUE), c(FALSE, FALSE, TRUE), c(TRUE, FALSE, FALSE), offset=0) IndexList(list(A=1:3, B=4:5, C=7:9)) IndexList(list(A=1:3, B=4:5, C=7:9), offset=0)testList <- list(GS_A=c(1,2,3,4,3), GS_B=c(2,3,4,5), GS_C=NULL, GS_D=c(1,3,5,NA), GS_E=c(2,4)) testIndexList <- IndexList(testList) IndexList(c(FALSE, TRUE, TRUE), c(FALSE, FALSE, TRUE), c(TRUE, FALSE, FALSE), offset=0) IndexList(list(A=1:3, B=4:5, C=7:9)) IndexList(list(A=1:3, B=4:5, C=7:9), offset=0)
An S4 class to hold a list of integers as indices, with the possibility to specify the offset of the indices
offsetAn integer specifying the value of first element. Default 1
keepNALogical, whether NA is kept during construction
keepDupLogical, whether duplicated values are kept during construction
Function to validate a BaseIndexList object
isValidBaseIndexList(object)isValidBaseIndexList(object)
object |
A BaseIndexList object
Use |
Function to validate a GmtList object
isValidGmtList(object)isValidGmtList(object)
object |
A GmtList object
Use |
Function to validate an IndexList object
isValidIndexList(object)isValidIndexList(object)
object |
an IndexList object
Use |
Function to validate a SignedGenesets object
isValidSignedGenesets(object)isValidSignedGenesets(object)
object |
A SignedGenesets object
Use |
Function to validate a SignedIndexList object
isValidSignedIndexList(object)isValidSignedIndexList(object)
object |
a SignedIndexList object
Use |
Match genes in a list-like object to a vector of genesymbols
matchGenes(list, object, ...) ## S4 method for signature 'GmtList,character' matchGenes(list, object) ## S4 method for signature 'GmtList,matrix' matchGenes(list, object) ## S4 method for signature 'GmtList,eSet' matchGenes(list, object, col = "GeneSymbol") ## S4 method for signature 'character,character' matchGenes(list, object) ## S4 method for signature 'character,matrix' matchGenes(list, object) ## S4 method for signature 'character,eSet' matchGenes(list, object) ## S4 method for signature 'character,DGEList' matchGenes(list, object, col = "GeneSymbol") ## S4 method for signature 'GmtList,DGEList' matchGenes(list, object, col = "GeneSymbol") ## S4 method for signature 'SignedGenesets,character' matchGenes(list, object) ## S4 method for signature 'SignedGenesets,matrix' matchGenes(list, object) ## S4 method for signature 'SignedGenesets,eSet' matchGenes(list, object, col = "GeneSymbol") ## S4 method for signature 'SignedGenesets,DGEList' matchGenes(list, object, col = "GeneSymbol")matchGenes(list, object, ...) ## S4 method for signature 'GmtList,character' matchGenes(list, object) ## S4 method for signature 'GmtList,matrix' matchGenes(list, object) ## S4 method for signature 'GmtList,eSet' matchGenes(list, object, col = "GeneSymbol") ## S4 method for signature 'character,character' matchGenes(list, object) ## S4 method for signature 'character,matrix' matchGenes(list, object) ## S4 method for signature 'character,eSet' matchGenes(list, object) ## S4 method for signature 'character,DGEList' matchGenes(list, object, col = "GeneSymbol") ## S4 method for signature 'GmtList,DGEList' matchGenes(list, object, col = "GeneSymbol") ## S4 method for signature 'SignedGenesets,character' matchGenes(list, object) ## S4 method for signature 'SignedGenesets,matrix' matchGenes(list, object) ## S4 method for signature 'SignedGenesets,eSet' matchGenes(list, object, col = "GeneSymbol") ## S4 method for signature 'SignedGenesets,DGEList' matchGenes(list, object, col = "GeneSymbol")
list |
A GmtList, list, character or SignedGenesets object |
object |
Gene symbols to be matched; they can come from a vector of character strings, or
a column in the fData of an |
... |
additional arguments like |
col |
Column name of |
An IndexList object, which is essentially a list of the same length as input (length of 1 in case characters are used as input), with matching indices.
## test GmtList, character testGenes <- sprintf("gene%d", 1:10) testGeneSets <- GmtList(list(gs1=c("gene1", "gene2"), gs2=c("gene9", "gene10"), gs3=c("gene100"))) matchGenes(testGeneSets, testGenes) ## test GmtList, matrix testGenes <- sprintf("gene%d", 1:10) testGeneSets <- GmtList(list(gs1=c("gene1", "gene2"), gs2=c("gene9", "gene10"), gs3=c("gene100"))) testGeneExprs <- matrix(rnorm(100), nrow=10, dimnames=list(testGenes, sprintf("sample%d", 1:10))) matchGenes(testGeneSets, testGeneExprs) ## test GmtList, eSet testGenes <- sprintf("gene%d", 1:10) testGeneSets <- GmtList(list(gs1=c("gene1", "gene2"), gs2=c("gene9", "gene10"), gs3=c("gene100"))) testGeneExprs <- matrix(rnorm(100), nrow=10, dimnames=list(testGenes, sprintf("sample%d", 1:10))) testFeat <- data.frame(GeneSymbol=rownames(testGeneExprs), row.names=testGenes) testPheno <- data.frame(SampleId=colnames(testGeneExprs), row.names=colnames(testGeneExprs)) testEset <- ExpressionSet(assayData=testGeneExprs, featureData=AnnotatedDataFrame(testFeat), phenoData=AnnotatedDataFrame(testPheno)) matchGenes(testGeneSets, testGeneExprs) ## force using row names matchGenes(testGeneSets, testEset, col=NULL) ## test GmtList, DGEList if(requireNamespace("edgeR")) { mat <- matrix(rnbinom(100, mu=5, size=2), ncol=10) rownames(mat) <- sprintf("gene%d", 1:nrow(mat)) y <- edgeR::DGEList(counts=mat, group=rep(1:2, each=5)) ## if genes are not set, row names of the count matrix will be used for lookup myGeneSet <- GmtList(list(gs1=rownames(mat)[1:2], gs2=rownames(mat)[9:10], gs3="gene100")) matchGenes(myGeneSet, y) matchGenes(c("gene1", "gene2"), y) ## alternatively, use 'col' parameter to specify the column in 'genes' y2 <- edgeR::DGEList(counts=mat, group=rep(1:2, each=5), genes=data.frame(GeneIdentifier=rownames(mat), row.names=rownames(mat))) matchGenes(myGeneSet, y2, col="GeneIdentifier") } ## test character, character matchGenes(c("gene1", "gene2"), testGenes) ## test character, matrix matchGenes(c("gene1", "gene2"), testGeneExprs) ## test character, eset matchGenes(c("gene1", "gene2"), testEset)## test GmtList, character testGenes <- sprintf("gene%d", 1:10) testGeneSets <- GmtList(list(gs1=c("gene1", "gene2"), gs2=c("gene9", "gene10"), gs3=c("gene100"))) matchGenes(testGeneSets, testGenes) ## test GmtList, matrix testGenes <- sprintf("gene%d", 1:10) testGeneSets <- GmtList(list(gs1=c("gene1", "gene2"), gs2=c("gene9", "gene10"), gs3=c("gene100"))) testGeneExprs <- matrix(rnorm(100), nrow=10, dimnames=list(testGenes, sprintf("sample%d", 1:10))) matchGenes(testGeneSets, testGeneExprs) ## test GmtList, eSet testGenes <- sprintf("gene%d", 1:10) testGeneSets <- GmtList(list(gs1=c("gene1", "gene2"), gs2=c("gene9", "gene10"), gs3=c("gene100"))) testGeneExprs <- matrix(rnorm(100), nrow=10, dimnames=list(testGenes, sprintf("sample%d", 1:10))) testFeat <- data.frame(GeneSymbol=rownames(testGeneExprs), row.names=testGenes) testPheno <- data.frame(SampleId=colnames(testGeneExprs), row.names=colnames(testGeneExprs)) testEset <- ExpressionSet(assayData=testGeneExprs, featureData=AnnotatedDataFrame(testFeat), phenoData=AnnotatedDataFrame(testPheno)) matchGenes(testGeneSets, testGeneExprs) ## force using row names matchGenes(testGeneSets, testEset, col=NULL) ## test GmtList, DGEList if(requireNamespace("edgeR")) { mat <- matrix(rnbinom(100, mu=5, size=2), ncol=10) rownames(mat) <- sprintf("gene%d", 1:nrow(mat)) y <- edgeR::DGEList(counts=mat, group=rep(1:2, each=5)) ## if genes are not set, row names of the count matrix will be used for lookup myGeneSet <- GmtList(list(gs1=rownames(mat)[1:2], gs2=rownames(mat)[9:10], gs3="gene100")) matchGenes(myGeneSet, y) matchGenes(c("gene1", "gene2"), y) ## alternatively, use 'col' parameter to specify the column in 'genes' y2 <- edgeR::DGEList(counts=mat, group=rep(1:2, each=5), genes=data.frame(GeneIdentifier=rownames(mat), row.names=rownames(mat))) matchGenes(myGeneSet, y2, col="GeneIdentifier") } ## test character, character matchGenes(c("gene1", "gene2"), testGenes) ## test character, matrix matchGenes(c("gene1", "gene2"), testGeneExprs) ## test character, eset matchGenes(c("gene1", "gene2"), testEset)
Get offset from an IndexList object
offset(object) ## S4 method for signature 'BaseIndexList' offset(object)offset(object) ## S4 method for signature 'BaseIndexList' offset(object)
object |
An IndexList object |
myIndexList <- IndexList(list(1:5, 2:7, 3:8), offset=1L) offset(myIndexList)myIndexList <- IndexList(list(1:5, 2:7, 3:8), offset=1L) offset(myIndexList)
IndexList or a SignedIndexList objectSet the offset of an IndexList or a SignedIndexList object
`offset<-`(object, value) ## S4 replacement method for signature 'IndexList,numeric' offset(object) <- value ## S4 replacement method for signature 'SignedIndexList,numeric' offset(object) <- value`offset<-`(object, value) ## S4 replacement method for signature 'IndexList,numeric' offset(object) <- value ## S4 replacement method for signature 'SignedIndexList,numeric' offset(object) <- value
object |
An |
value |
The value, that the offset of |
myIndexList <- IndexList(list(1:5, 2:7, 3:8), offset=1L) offset(myIndexList) offset(myIndexList) <- 3 offset(myIndexList)myIndexList <- IndexList(list(1:5, 2:7, 3:8), offset=1L) offset(myIndexList) offset(myIndexList) <- 3 offset(myIndexList)
Prettify default signature names
prettySigNames(names, includeNamespace = TRUE)prettySigNames(names, includeNamespace = TRUE)
names |
Character strings, signature names |
includeNamespace |
Logical, whether the namespace of the signatures should be included |
Character strings, pretty signature names
sig <- readCurrentSignatures() prettyNames <- prettySigNames(names(sig))sig <- readCurrentSignatures() prettyNames <- prettySigNames(names(sig))
Load current BioQC signatures
readCurrentSignatures(uniqGenes = TRUE, namespace = NULL)readCurrentSignatures(uniqGenes = TRUE, namespace = NULL)
uniqGenes |
Logical, whether duplicated genes should be removed, passed
to |
namespace |
Character, namespace of the gene-set, or codeNULL, passed
to |
A GmtList
readCurrentSignatures()readCurrentSignatures()
Read in gene-sets from a GMT file
readGmt(..., uniqGenes = TRUE, namespace = NULL)readGmt(..., uniqGenes = TRUE, namespace = NULL)
... |
Named or unnamed characater string vector, giving file names of one or more GMT format files. |
uniqGenes |
Logical, whether duplicated genes should be removed |
namespace |
Character, namespace of the gene-set. It can be used to specify namespace or sources of the gene-sets. If |
A GmtList object, which is a S4-class wrapper of a list. Each
element in the object is a list of (at least) three items:
gene-set name (field name), character string, accessible with gsName
gene-set description (field desc), character string, accessible with gsDesc
genes (field genes), a vector of character strings, , accessible with gsGenes
namespace (field namespace), accessible with gsNamespace
Currently, when namespace is set as NULL, no namespace is used. This may change in the future, since we may use file base name as the default namespace.
gmt_file <- system.file("extdata/exp.tissuemark.affy.roche.symbols.gmt", package="BioQC") gmt_list <- readGmt(gmt_file) gmt_nonUniqGenes_list <- readGmt(gmt_file, uniqGenes=FALSE) gmt_namespace_list <- readGmt(gmt_file, uniqGenes=FALSE, namespace="myNamespace") ## suppose we have two lists of gene-sets to read in test_gmt_file <- system.file("extdata/test.gmt", package="BioQC") gmt_twons_list <- readGmt(gmt_file, test_gmt_file, namespace=c("BioQC", "test")) ## alternatively gmt_twons_list <- readGmt(BioQC=gmt_file, test=test_gmt_file)gmt_file <- system.file("extdata/exp.tissuemark.affy.roche.symbols.gmt", package="BioQC") gmt_list <- readGmt(gmt_file) gmt_nonUniqGenes_list <- readGmt(gmt_file, uniqGenes=FALSE) gmt_namespace_list <- readGmt(gmt_file, uniqGenes=FALSE, namespace="myNamespace") ## suppose we have two lists of gene-sets to read in test_gmt_file <- system.file("extdata/test.gmt", package="BioQC") gmt_twons_list <- readGmt(gmt_file, test_gmt_file, namespace=c("BioQC", "test")) ## alternatively gmt_twons_list <- readGmt(BioQC=gmt_file, test=test_gmt_file)
Read signed GMT files
readSignedGmt( filename, posPattern = "_UP$", negPattern = "_DN$", nomatch = c("ignore", "pos", "neg"), uniqGenes = TRUE, namespace = NULL )readSignedGmt( filename, posPattern = "_UP$", negPattern = "_DN$", nomatch = c("ignore", "pos", "neg"), uniqGenes = TRUE, namespace = NULL )
filename |
A gmt file |
posPattern |
Pattern of positive gene sets |
negPattern |
Pattern of negative gene sets |
nomatch |
options to deal with gene sets that match to neither posPattern nor negPattern patterns |
uniqGenes |
Logical, whether genes should be made unique |
namespace |
Character string or |
gmtlist2signedGenesets for parameters posPattern, negPattern, and nomatch
testGmtFile <- system.file("extdata/test.gmt", package="BioQC") testSignedGenesets.ignore <- readSignedGmt(testGmtFile, nomatch="ignore") testSignedGenesets.pos <- readSignedGmt(testGmtFile, nomatch="pos") testSignedGenesets.neg <- readSignedGmt(testGmtFile, nomatch="neg")testGmtFile <- system.file("extdata/test.gmt", package="BioQC") testSignedGenesets.ignore <- readSignedGmt(testGmtFile, nomatch="ignore") testSignedGenesets.pos <- readSignedGmt(testGmtFile, nomatch="pos") testSignedGenesets.neg <- readSignedGmt(testGmtFile, nomatch="neg")
Entropy-based sample specialization
sampleSpecialization(mat, norm = TRUE)sampleSpecialization(mat, norm = TRUE)
mat |
A matrix (usually an expression matrix), with genes (features) in rows and samples in columns. |
norm |
Logical, whether the specialization should be normalized by |
A vector as long as the column number of the input matrix
Martinez and Reyes-Valdes (2008) Defining diversity, specialization, and gene specificity in transcriptomes through information theory. PNAS 105(28):9709–9714
myMat <- rbind(c(3,4,5),c(6,6,6), c(0,2,4)) sampleSpecialization(myMat) sampleSpecialization(myMat, norm=TRUE) myRandomMat <- matrix(runif(1000), ncol=20) sampleSpecialization(myRandomMat) sampleSpecialization(myRandomMat, norm=TRUE)myMat <- rbind(c(3,4,5),c(6,6,6), c(0,2,4)) sampleSpecialization(myMat) sampleSpecialization(myMat, norm=TRUE) myRandomMat <- matrix(runif(1000), ncol=20) sampleSpecialization(myRandomMat) sampleSpecialization(myRandomMat, norm=TRUE)
Set gene-set description as namespace
setDescAsNamespace(x)setDescAsNamespace(x)
x |
A This function wrapps |
Set the namespace field in each gene-set within a GmtList
setNamespace(x, namespace)setNamespace(x, namespace)
x |
A |
namespace |
It can be either a function that applies to a Note that using vectors as |
myGmtList <- GmtList(list(list(name="GeneSet1", desc="Namespace1", genes=LETTERS[1:3]), list(name="GeneSet2", desc="Namespace1", genes=rep(LETTERS[4:6],2)), list(name="GeneSet1", desc="Namespace1", genes=LETTERS[4:6]), list(name="GeneSet3", desc="Namespace2", genes=LETTERS[1:5]))) hasNamespace(myGmtList) myGmtList2 <- setNamespace(myGmtList, namespace=function(x) x$desc) gsNamespace(myGmtList2) ## the function can provide flexible ways to encode the gene-set namespace myGmtList3 <- setNamespace(myGmtList, namespace=function(x) gsub("Namespace", "C", x$desc)) gsNamespace(myGmtList3) ## using vectors myGmtList4 <- setNamespace(myGmtList, namespace=c("C1", "C1", "C1", "C2")) gsNamespace(myGmtList4) myGmtList2null <- setNamespace(myGmtList2, namespace=NULL) hasNamespace(myGmtList2null)myGmtList <- GmtList(list(list(name="GeneSet1", desc="Namespace1", genes=LETTERS[1:3]), list(name="GeneSet2", desc="Namespace1", genes=rep(LETTERS[4:6],2)), list(name="GeneSet1", desc="Namespace1", genes=LETTERS[4:6]), list(name="GeneSet3", desc="Namespace2", genes=LETTERS[1:5]))) hasNamespace(myGmtList) myGmtList2 <- setNamespace(myGmtList, namespace=function(x) x$desc) gsNamespace(myGmtList2) ## the function can provide flexible ways to encode the gene-set namespace myGmtList3 <- setNamespace(myGmtList, namespace=function(x) gsub("Namespace", "C", x$desc)) gsNamespace(myGmtList3) ## using vectors myGmtList4 <- setNamespace(myGmtList, namespace=c("C1", "C1", "C1", "C2")) gsNamespace(myGmtList4) myGmtList2null <- setNamespace(myGmtList2, namespace=NULL) hasNamespace(myGmtList2null)
Show method for GmtList
## S4 method for signature 'GmtList' show(object)## S4 method for signature 'GmtList' show(object)
object |
An object of the class |
Show method for IndexList
## S4 method for signature 'IndexList' show(object)## S4 method for signature 'IndexList' show(object)
object |
An object of the class |
Show method for SignedGenesets
## S4 method for signature 'SignedGenesets' show(object)## S4 method for signature 'SignedGenesets' show(object)
object |
An object of the class |
Show method for SignedIndexList
## S4 method for signature 'SignedIndexList' show(object)## S4 method for signature 'SignedIndexList' show(object)
object |
An object of the class |
Convert a list to a SignedGenesets object
SignedGenesets(list)SignedGenesets(list)
list |
A list of genesets; each geneset is a list of at least three fields: 'name', 'pos', and 'neg'. 'name' contains one non-null character string, and both 'pos' and 'neg' can be either NULL or a character vector. |
GmtList
testList <- list(list(name="GS_A", pos=NULL, neg=LETTERS[1:3]), list(name="GS_B", pos=LETTERS[1:5], neg=LETTERS[7:9]), list(name="GS_C", pos=LETTERS[1:5], neg=NULL), list(name="GS_D", pos=NULL, neg=NULL)) testSigndGS <- SignedGenesets(testList)testList <- list(list(name="GS_A", pos=NULL, neg=LETTERS[1:3]), list(name="GS_B", pos=LETTERS[1:5], neg=LETTERS[7:9]), list(name="GS_C", pos=LETTERS[1:5], neg=NULL), list(name="GS_D", pos=NULL, neg=NULL)) testSigndGS <- SignedGenesets(testList)
An S4 class to hold signed genesets, each item in the list is in in turn a list containing following items: name, pos, and neg.
Convert a list into a SignedIndexList
SignedIndexList(object, ...) ## S4 method for signature 'list' SignedIndexList(object, keepNA = FALSE, keepDup = FALSE, offset = 1L)SignedIndexList(object, ...) ## S4 method for signature 'list' SignedIndexList(object, keepNA = FALSE, keepDup = FALSE, offset = 1L)
object |
A list of lists, each with two elements named 'pos' or 'neg', can be logical vectors or integer indices |
... |
additional arguments, currently ignored |
keepNA |
Logical, whether NA indices should be kept or not. Default: FALSE (removed) |
keepDup |
Logical, whether duplicated indices should be kept or not. Default: FALSE (removed) |
offset |
offset; 1 if missing |
A SignedIndexList, a list of lists, containing two vectors named 'positive' and 'negative', which contain the indices of genes that are either positively or negatively associated with a certain phenotype
myList <- list(a = list(pos = list(1, 2, 2, 4), neg = c(TRUE, FALSE, TRUE)), b = list(NA), c = list(pos = c(c(2, 3), c(1, 3)))) SignedIndexList(myList) ## a special case of input is a single list with two elements, \code{pos} and \code{neg} SignedIndexList(myList[[1]])myList <- list(a = list(pos = list(1, 2, 2, 4), neg = c(TRUE, FALSE, TRUE)), b = list(NA), c = list(pos = c(c(2, 3), c(1, 3)))) SignedIndexList(myList) ## a special case of input is a single list with two elements, \code{pos} and \code{neg} SignedIndexList(myList[[1]])
An S4 class to hold a list of signed integers as indices, with the possibility to specify the offset of the indices
offsetAn integer specifying the value of first element. Default 1
keepNALogical, whether NA is kept during construction
keepDupLogical, whether duplicated values are kept during construction
Simplify matrix in case of single row/columns
simplifyMatrix(matrix)simplifyMatrix(matrix)
matrix |
A matrix of any dimension If only one row/column is present, the dimension is dropped and a vector will be returned |
testMatrix <- matrix(round(rnorm(9),2), nrow=3) simplifyMatrix(testMatrix) simplifyMatrix(testMatrix[1L,,drop=FALSE]) simplifyMatrix(testMatrix[,1L,drop=FALSE])testMatrix <- matrix(round(rnorm(9),2), nrow=3) simplifyMatrix(testMatrix) simplifyMatrix(testMatrix[1L,,drop=FALSE]) simplifyMatrix(testMatrix[,1L,drop=FALSE])
Make names of gene-sets unique by namespace, and member genes of gene-sets unique
uniqGenesetsByNamespace(gmtList)uniqGenesetsByNamespace(gmtList)
gmtList |
A The function make sure that
Gene-sets with duplicated names and different |
A GmtList object, with unique gene-sets and unique gene lists. If not already present, a new item namespace is appended to each list element in the GmtList object, recording the namespace used to make gene-sets unique. The order of the returned GmtList object is given by the unique gene-set name of the input object.
myGmtList <- GmtList(list(list(name="GeneSet1", desc="Namespace1", genes=LETTERS[1:3]), list(name="GeneSet2", desc="Namespace1", genes=rep(LETTERS[4:6],2)), list(name="GeneSet1", desc="Namespace1", genes=LETTERS[4:6]), list(name="GeneSet3", desc="Namespace2", genes=LETTERS[1:5]))) print(myGmtList) myGmtList <- setNamespace(myGmtList, namespace=function(x) x$desc) myUniqGmtList <- uniqGenesetsByNamespace(myGmtList) print(myUniqGmtList)myGmtList <- GmtList(list(list(name="GeneSet1", desc="Namespace1", genes=LETTERS[1:3]), list(name="GeneSet2", desc="Namespace1", genes=rep(LETTERS[4:6],2)), list(name="GeneSet1", desc="Namespace1", genes=LETTERS[4:6]), list(name="GeneSet3", desc="Namespace2", genes=LETTERS[1:5]))) print(myGmtList) myGmtList <- setNamespace(myGmtList, namespace=function(x) x$desc) myUniqGmtList <- uniqGenesetsByNamespace(myGmtList) print(myUniqGmtList)
prints the options of valTypes of wmwTest
valTypes()valTypes()
Identify BioQC leading-edge genes of one gene-set
wmwLeadingEdge( matrix, indexVector, valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater", "log10p.less", "abs.log10p.two.sided", "Q", "r", "f", "U1", "U2"), thr = 0.05, reference = c("background", "geneset") )wmwLeadingEdge( matrix, indexVector, valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater", "log10p.less", "abs.log10p.two.sided", "Q", "r", "f", "U1", "U2"), thr = 0.05, reference = c("background", "geneset") )
matrix |
A numeric matrix |
indexVector |
An integer vector, giving indices of a gene-set of interest |
valType |
Value type, consistent with the types in |
thr |
Threshold of the value, greater or less than which the gene-set is considered significantly enriched in one sample |
reference |
Character string, which reference is used? If |
A list of integer vectors.
BioQC leading-edge genes are defined as those features whose expression is higher than the median expression of the background in a sample. The function identifies leading-edge genes of a given dataset (specified by the index vector) in a number of samples (specified by the matrix, with genes/features in rows and samples in columns) in three steps. The function calls wmwTest to run BioQC and identify samples in which the gene-set is significantly enriched. The enrichment criteria is specified by valType and thr. Then the function identifies genes in the gene-set that have greater or less expresion than the median value of the reference in those samples showing significant enrichment. Finally, it reports either leading-edge genes in individual samples, or the intersection/union of leading-edge genes in multiple samples.
myProfile <- c(rnorm(5, 3), rnorm(15, -3), rnorm(100, 0)) myProfile2 <- c(rnorm(15, 3), rnorm(5, -3), rnorm(100, 0)) myProfile3 <- c(rnorm(10, 5), rnorm(10, 0), rnorm(100, 0)) myProfileMat <- cbind(myProfile, myProfile2, myProfile3) wmwLeadingEdge(myProfileMat, 1:20, valType="p.greater") wmwLeadingEdge(myProfileMat, 1:20, valType="log10p.less") wmwLeadingEdge(myProfileMat, 1:20, valType="U", reference="geneset") wmwLeadingEdge(myProfileMat, 1:20, valType="abs.log10p.greater")myProfile <- c(rnorm(5, 3), rnorm(15, -3), rnorm(100, 0)) myProfile2 <- c(rnorm(15, 3), rnorm(5, -3), rnorm(100, 0)) myProfile3 <- c(rnorm(10, 5), rnorm(10, 0), rnorm(100, 0)) myProfileMat <- cbind(myProfile, myProfile2, myProfile3) wmwLeadingEdge(myProfileMat, 1:20, valType="p.greater") wmwLeadingEdge(myProfileMat, 1:20, valType="log10p.less") wmwLeadingEdge(myProfileMat, 1:20, valType="U", reference="geneset") wmwLeadingEdge(myProfileMat, 1:20, valType="abs.log10p.greater")
wmwTest is a highly efficient Wilcoxon-Mann-Whitney rank sum
test for high-dimensional data, such as gene expression profiling. For datasets with
more than 100 features (genes), the function can be more than 1,000
times faster than its R implementations (wilcox.test in
stats, or rankSumTestWithCorrelation in limma).
wmwTest( x, indexList, col = "GeneSymbol", valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater", "log10p.less", "abs.log10p.two.sided", "Q", "r", "f", "U1", "U2"), simplify = TRUE ) ## S4 method for signature 'matrix,IndexList' wmwTest(x, indexList, valType = "p.greater", simplify = TRUE) ## S4 method for signature 'numeric,IndexList' wmwTest(x, indexList, valType = "p.greater", simplify = TRUE) ## S4 method for signature 'matrix,GmtList' wmwTest(x, indexList, valType = "p.greater", simplify = TRUE) ## S4 method for signature 'eSet,GmtList' wmwTest( x, indexList, col = "GeneSymbol", valType = "p.greater", simplify = TRUE ) ## S4 method for signature 'eSet,numeric' wmwTest( x, indexList, col = "GeneSymbol", valType = "p.greater", simplify = TRUE ) ## S4 method for signature 'eSet,logical' wmwTest( x, indexList, col = "GeneSymbol", valType = "p.greater", simplify = TRUE ) ## S4 method for signature 'eSet,list' wmwTest( x, indexList, col = "GeneSymbol", valType = "p.greater", simplify = TRUE ) ## S4 method for signature 'ANY,numeric' wmwTest(x, indexList, valType = "p.greater", simplify = TRUE) ## S4 method for signature 'ANY,logical' wmwTest(x, indexList, valType = "p.greater", simplify = TRUE) ## S4 method for signature 'ANY,list' wmwTest(x, indexList, valType = "p.greater", simplify = TRUE) ## S4 method for signature 'matrix,SignedIndexList' wmwTest(x, indexList, valType, simplify = TRUE) ## S4 method for signature 'matrix,SignedGenesets' wmwTest(x, indexList, valType, simplify = TRUE) ## S4 method for signature 'numeric,SignedIndexList' wmwTest(x, indexList, valType, simplify = TRUE) ## S4 method for signature 'eSet,SignedIndexList' wmwTest(x, indexList, valType, simplify = TRUE) ## S4 method for signature 'eSet,SignedGenesets' wmwTest( x, indexList, col = "GeneSymbol", valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater", "log10p.less", "abs.log10p.two.sided", "Q", "r", "f", "U1", "U2"), simplify = TRUE )wmwTest( x, indexList, col = "GeneSymbol", valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater", "log10p.less", "abs.log10p.two.sided", "Q", "r", "f", "U1", "U2"), simplify = TRUE ) ## S4 method for signature 'matrix,IndexList' wmwTest(x, indexList, valType = "p.greater", simplify = TRUE) ## S4 method for signature 'numeric,IndexList' wmwTest(x, indexList, valType = "p.greater", simplify = TRUE) ## S4 method for signature 'matrix,GmtList' wmwTest(x, indexList, valType = "p.greater", simplify = TRUE) ## S4 method for signature 'eSet,GmtList' wmwTest( x, indexList, col = "GeneSymbol", valType = "p.greater", simplify = TRUE ) ## S4 method for signature 'eSet,numeric' wmwTest( x, indexList, col = "GeneSymbol", valType = "p.greater", simplify = TRUE ) ## S4 method for signature 'eSet,logical' wmwTest( x, indexList, col = "GeneSymbol", valType = "p.greater", simplify = TRUE ) ## S4 method for signature 'eSet,list' wmwTest( x, indexList, col = "GeneSymbol", valType = "p.greater", simplify = TRUE ) ## S4 method for signature 'ANY,numeric' wmwTest(x, indexList, valType = "p.greater", simplify = TRUE) ## S4 method for signature 'ANY,logical' wmwTest(x, indexList, valType = "p.greater", simplify = TRUE) ## S4 method for signature 'ANY,list' wmwTest(x, indexList, valType = "p.greater", simplify = TRUE) ## S4 method for signature 'matrix,SignedIndexList' wmwTest(x, indexList, valType, simplify = TRUE) ## S4 method for signature 'matrix,SignedGenesets' wmwTest(x, indexList, valType, simplify = TRUE) ## S4 method for signature 'numeric,SignedIndexList' wmwTest(x, indexList, valType, simplify = TRUE) ## S4 method for signature 'eSet,SignedIndexList' wmwTest(x, indexList, valType, simplify = TRUE) ## S4 method for signature 'eSet,SignedGenesets' wmwTest( x, indexList, col = "GeneSymbol", valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater", "log10p.less", "abs.log10p.two.sided", "Q", "r", "f", "U1", "U2"), simplify = TRUE )
x |
A numeric matrix. All other data types (e.g. numeric vectors
or |
indexList |
A list of integer indices (starting from 1) indicating
signature genes. Can be of length zero. Other data types (e.g. a list
of numeric or logical vectors, or a numeric or logical vector) are
coerced into such a list. See |
col |
a string sometimes used with a |
valType |
The value type to be returned, allowed values
include |
simplify |
Logical. If not, the returning value is in matrix
format; if set to |
The basic application of the function is to test the enrichment of gene sets in expression profiling data or differentially expressed data (the matrix with feature/gene in rows and samples in columns).
A special case is when x is an eSet object
(e.g. ExpressionSet), and indexList is a list returned
from readGmt function. In this case, the only requirement is
that one column named GeneSymbol in the featureData
contain gene symbols used in the GMT file. The same applies to signed Gmt files. See the example below.
Besides the conventional value types such as ‘p.greater’,
‘p.less’, ‘p.two.sided’ , and ‘U’ (the U-statistic),
wmwTest (from version 0.99-1) provides further value types:
abs.log10p.greater and log10p.less perform log10
transformation on respective p-values and give the
transformed value a proper sign (positive for greater than, and
negative for less than); abs.log10p.two.sided transforms
two-sided p-values to non-negative values; and Q score
reports absolute log10-transformation of p-value of the
two-side variant, and gives a proper sign to it, depending on whether it is
rather greater than (positive) or less than (negative).
From version 1.19.1, the rank-biserial correlation coefficient (‘r’) and the common language effect size (‘f’) are supported value types.
Before version 1.19.3, the ‘U’ statistic returned is in fact ‘U2’. From version 1.19.3, ‘U1’ is returned when ‘U’ is used, and users can specify additional parameter values ‘U1’ and ‘U2’. The sum of ‘U1’ and ‘U2’ is the product of the sizes of two vectors to be compared.
A numeric matrix or vector containing the statistic.
x = matrix,indexList = IndexList: x is a matrix and indexList is a IndexList
x = numeric,indexList = IndexList: x is a numeric and indexList is a IndexList
x = matrix,indexList = GmtList: x is a matrix and indexList is a GmtList
x = eSet,indexList = GmtList: x is a eSet and indexList is a GmtList
x = eSet,indexList = numeric: x is a eSet and indexList is a numeric
x = eSet,indexList = logical: x is a eSet and indexList is a logical
x = eSet,indexList = list: x is a eSet and indexList is a list
x = ANY,indexList = numeric: x is ANY and indexList is a numeric
x = ANY,indexList = logical: x is ANY and indexList is a logical
x = ANY,indexList = list: x is ANY and indexList is a list
x = matrix,indexList = SignedIndexList: x is a matrix and indexList is a
SignedIndexList
x = matrix,indexList = SignedGenesets: x is a eSet and indexList is a
SignedIndexList
x = numeric,indexList = SignedIndexList: x is a numeric and indexList is a
SignedIndexList
x = eSet,indexList = SignedIndexList: x is a eSet and indexList is a
SignedIndexList
x = eSet,indexList = SignedGenesets: x is a eSet and indexList is a
SignedIndexList
The function has been optimized for expression profiling data. It
avoids repetitive ranking of data as done by native R implementations
and uses efficient C code to increase the performance and control
memory use. Simulation studies using expression profiles of 22000
genes in 2000 samples and 200 gene sets suggested that the C
implementation can be >1000 times faster than the R
implementation. And it is possible to further accelerate by
parallel calling the function with mclapply in the multicore package.
Jitao David Zhang <[email protected]>, with critical inputs from Jan Aettig and Iakov Davydov about U statistics.
Barry, W.T., Nobel, A.B., and Wright, F.A. (2008). A statistical framework for testing functional namespaces in microarray data. _Annals of Applied Statistics_ 2, 286-315.
Wu, D, and Smyth, GK (2012). Camera: a competitive gene set test accounting for inter-gene correlation. _Nucleic Acids Research_ 40(17):e133
Zar, JH (1999). _Biostatistical Analysis 4th Edition_. Prentice-Hall International, Upper Saddle River, New Jersey.
codewilcox.test in the stats package, and rankSumTestWithCorrelation in
the limma package.
## R-native data structures set.seed(1887) rd <- rnorm(1000) rl <- sample(c(TRUE, FALSE), 1000, replace=TRUE) wmwTest(rd, rl, valType="p.two.sided") wmwTest(rd, which(rl), valType="p.two.sided") rd1 <- rd + ifelse(rl, 0.5, 0) wmwTest(rd1, rl, valType="p.greater") wmwTest(rd1, rl, valType="U") rd2 <- rd - ifelse(rl, 0.2, 0) wmwTest(rd2, rl, valType="p.greater") wmwTest(rd2, rl, valType="p.two.sided") wmwTest(rd2, rl, valType="p.less") wmwTest(rd2, rl, valType="r") wmwTest(rd2, rl, valType="f") ## matrix forms rmat <- matrix(c(rd, rd1, rd2), ncol=3, byrow=FALSE) wmwTest(rmat, rl, valType="p.two.sided") wmwTest(rmat, rl, valType="p.greater") wmwTest(rmat, which(rl), valType="p.two.sided") wmwTest(rmat, which(rl), valType="p.greater") ## other valTypes wmwTest(rmat, which(rl), valType="U") wmwTest(rmat, which(rl), valType="abs.log10p.greater") wmwTest(rmat, which(rl), valType="log10p.less") wmwTest(rmat, which(rl), valType="abs.log10p.two.sided") wmwTest(rmat, which(rl), valType="Q") wmwTest(rmat, which(rl), valType="r") wmwTest(rmat, which(rl), valType="f") ## using ExpressionSet data(sample.ExpressionSet) testSet <- sample.ExpressionSet fData(testSet)$GeneSymbol <- paste("GENE_",1:nrow(testSet), sep="") mySig1 <- sample(c(TRUE, FALSE), nrow(testSet), prob=c(0.25, 0.75), replace=TRUE) wmwTest(testSet, which(mySig1), valType="p.greater") ## using integer exprs(testSet)[,1L] <- exprs(testSet)[,1L] + ifelse(mySig1, 50, 0) wmwTest(testSet, which(mySig1), valType="p.greater") ## using lists mySig2 <- sample(c(TRUE, FALSE), nrow(testSet), prob=c(0.6, 0.4), replace=TRUE) wmwTest(testSet, list(first=mySig1, second=mySig2)) ## using GMT file gmt_file <- system.file("extdata/exp.tissuemark.affy.roche.symbols.gmt", package="BioQC") gmt_list <- readGmt(gmt_file) gss <- sample(unlist(sapply(gmt_list, function(x) x$genes)), 1000) eset<-new("ExpressionSet", exprs=matrix(rnorm(10000), nrow=1000L), phenoData=new("AnnotatedDataFrame", data.frame(Sample=LETTERS[1:10])), featureData=new("AnnotatedDataFrame",data.frame(GeneSymbol=gss))) esetWmwRes <- wmwTest(eset ,gmt_list, valType="p.greater") summary(esetWmwRes) ## using signed GMT file signed_gmt_file <- system.file("extdata/test.gmt", package="BioQC") signed_gmt <- readSignedGmt(signed_gmt_file) esetSignedWmwRes <- wmwTest(eset, signed_gmt, valType="p.greater") esetMat <- exprs(eset); rownames(esetMat) <- fData(eset)$GeneSymbol esetSignedWmwRes2 <- wmwTest(esetMat, signed_gmt, valType="p.greater")## R-native data structures set.seed(1887) rd <- rnorm(1000) rl <- sample(c(TRUE, FALSE), 1000, replace=TRUE) wmwTest(rd, rl, valType="p.two.sided") wmwTest(rd, which(rl), valType="p.two.sided") rd1 <- rd + ifelse(rl, 0.5, 0) wmwTest(rd1, rl, valType="p.greater") wmwTest(rd1, rl, valType="U") rd2 <- rd - ifelse(rl, 0.2, 0) wmwTest(rd2, rl, valType="p.greater") wmwTest(rd2, rl, valType="p.two.sided") wmwTest(rd2, rl, valType="p.less") wmwTest(rd2, rl, valType="r") wmwTest(rd2, rl, valType="f") ## matrix forms rmat <- matrix(c(rd, rd1, rd2), ncol=3, byrow=FALSE) wmwTest(rmat, rl, valType="p.two.sided") wmwTest(rmat, rl, valType="p.greater") wmwTest(rmat, which(rl), valType="p.two.sided") wmwTest(rmat, which(rl), valType="p.greater") ## other valTypes wmwTest(rmat, which(rl), valType="U") wmwTest(rmat, which(rl), valType="abs.log10p.greater") wmwTest(rmat, which(rl), valType="log10p.less") wmwTest(rmat, which(rl), valType="abs.log10p.two.sided") wmwTest(rmat, which(rl), valType="Q") wmwTest(rmat, which(rl), valType="r") wmwTest(rmat, which(rl), valType="f") ## using ExpressionSet data(sample.ExpressionSet) testSet <- sample.ExpressionSet fData(testSet)$GeneSymbol <- paste("GENE_",1:nrow(testSet), sep="") mySig1 <- sample(c(TRUE, FALSE), nrow(testSet), prob=c(0.25, 0.75), replace=TRUE) wmwTest(testSet, which(mySig1), valType="p.greater") ## using integer exprs(testSet)[,1L] <- exprs(testSet)[,1L] + ifelse(mySig1, 50, 0) wmwTest(testSet, which(mySig1), valType="p.greater") ## using lists mySig2 <- sample(c(TRUE, FALSE), nrow(testSet), prob=c(0.6, 0.4), replace=TRUE) wmwTest(testSet, list(first=mySig1, second=mySig2)) ## using GMT file gmt_file <- system.file("extdata/exp.tissuemark.affy.roche.symbols.gmt", package="BioQC") gmt_list <- readGmt(gmt_file) gss <- sample(unlist(sapply(gmt_list, function(x) x$genes)), 1000) eset<-new("ExpressionSet", exprs=matrix(rnorm(10000), nrow=1000L), phenoData=new("AnnotatedDataFrame", data.frame(Sample=LETTERS[1:10])), featureData=new("AnnotatedDataFrame",data.frame(GeneSymbol=gss))) esetWmwRes <- wmwTest(eset ,gmt_list, valType="p.greater") summary(esetWmwRes) ## using signed GMT file signed_gmt_file <- system.file("extdata/test.gmt", package="BioQC") signed_gmt <- readSignedGmt(signed_gmt_file) esetSignedWmwRes <- wmwTest(eset, signed_gmt, valType="p.greater") esetMat <- exprs(eset); rownames(esetMat) <- fData(eset)$GeneSymbol esetSignedWmwRes2 <- wmwTest(esetMat, signed_gmt, valType="p.greater")
Wilcoxon-Mann-Whitney test in R
wmwTestInR(x, sub, valType = c("p.greater", "p.less", "p.two.sided", "W"))wmwTestInR(x, sub, valType = c("p.greater", "p.less", "p.two.sided", "W"))
x |
A numerical vector |
sub |
A logical vector or integer vector to subset |
valType |
Type of retured-value. Supported values: p.greater, p.less, p.two.sided, and W statistic (note it is different from the U statistic) |
testNums <- 1:10 testSub <- rep_len(c(TRUE, FALSE), length.out=length(testNums)) wmwTestInR(testNums, testSub) wmwTestInR(testNums, testSub, valType="p.two.sided") wmwTestInR(testNums, testSub, valType="p.less") wmwTestInR(testNums, testSub, valType="W")testNums <- 1:10 testSub <- rep_len(c(TRUE, FALSE), length.out=length(testNums)) wmwTestInR(testNums, testSub) wmwTestInR(testNums, testSub, valType="p.two.sided") wmwTestInR(testNums, testSub, valType="p.less") wmwTestInR(testNums, testSub, valType="W")