| Title: | Permutation approximation methods for gene set enrichment analysis (non-permutation GSEA) |
|---|---|
| Description: | Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results. |
| Authors: | Jessica Larson and Art Owen |
| Maintainer: | Jessica Larson <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.41.0 |
| Built: | 2024-06-30 05:15:25 UTC |
| Source: | https://github.com/bioc/npGSEA |
alphaValue ~~This function returns the corresponding alpha value for the reference
beta distribution for the npGSEA analysis in the gene set in the
given experiment. This method is applicable for only the beta approximation method.
alphaValue(object)alphaValue(object)
object |
An object of type
|
signature(object = "npGSEAResultBeta")Returns the value for
alpha for a npGSEAResultBeta object
signature(object = "npGSEAResultBetaCollection")Returns a
list of the alpha values for a npGSEAResultBetaCollection
objects (1 for each set)
Jessica L. Larson
npGSEAResultBeta-class
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15, approx= "beta") alphaValue(res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15, approx= "beta") alphaValue(res)
betaHats ~~This function returns the betaHats
for all genes in the corresponding GeneSet in the given experiment,
or a list of such vectors for each set in a GeneSetCollection.
This corresponds to each gene's contrubution to the test statistic.
This method is applicable for all three approximation methods.
betaHats(object)betaHats(object)
object |
An object of type |
signature(object = "npGSEAResultNorm")Returns the betaHats
used for analysis to create a npGSEAResultNorm object
signature(object = "npGSEAResultBeta")Returns the betaHats
used for analysis to create a npGSEAResultBeta object
signature(object = "npGSEAResultChiSq")Returns the betaHats
used for analysis to create a npGSEAResultChiSq object
signature(object = "npGSEAResultNormCollection")Returns the betaHats
used for analysis to create the
npGSEAResultNormCollection objects (1 for each set)
signature(object = "npGSEAResultBetaCollection")Returns a list of
the betaHats used for analysis to create the
npGSEAResultBetaCollection objects (1 for each set)
signature(object = "npGSEAResultChiSqCollection")Returns a list of
the betaHats used for analysis to create the
npGSEAResultChiSqCollection objects (1 for each set)
npGSEAResultNorm-class
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) betaHats(res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) betaHats(res)
betaStat ~~This function returns the corresponding beta statistic
which is compared to the reference
beta distribution for the npGSEA analysis in the gene set in the
given experiment.
betaStat(object)betaStat(object)
object |
An object of type
|
signature(object = "npGSEAResultBeta")Returns the beta-statistic
for a npGSEAResultBeta object
signature(object = "npGSEAResultBetaCollection")Returns a list of
the beta-statistics for a npGSEAResultBetaCollection
objects (1 for each set)
Jessica L. Larson
npGSEAResultBeta-class
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15, approx= "beta") betaStat(res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15, approx= "beta") betaStat(res)
betaValue ~~This function returns the corresponding beta value for the reference
beta distribution for the npGSEA analysis in the gene set in the
given experiment. This method is applicable for only the beta approximation method.
betaValue(object)betaValue(object)
object |
An object of type
|
signature(object = "npGSEAResultBeta") Returns the
value for beta for a npGSEAResultBeta object
signature(object = "npGSEAResultBetaCollection") Returns a
list of the beta values for a npGSEAResultBetaCollection objects (1 for each set)
Jessica L. Larson
npGSEAResultBeta-class
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15, approx= "beta") betaValue(res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15, approx= "beta") betaValue(res)
chiSqStat ~~This function returns the chi-sq statistic (which is compared to a reference
Chi-sq distribution) for the chi-sq approximation of npGSEA for
a corresponding GeneSet
or a list of these statistics for a GeneSetCollection.
This method is applicable for only the chi-sq approximation method.
chiSqStat(object)chiSqStat(object)
object |
An object of type
|
signature(object = "npGSEAResultChiSq")Returns the chi-sq statistic for a
npGSEAResultChiSq object
signature(object = "npGSEAResultChiSqCollection")Returns a list of
the chi-sq statistics for a npGSEAResultChiSqCollection objects (1 for each set)
Jessica L. Larson
npGSEAResultChiSq-class
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15, approx= "chiSq") chiSqStat(res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15, approx= "chiSq") chiSqStat(res)
DF ~~This function returns the degrees of freedom for the chi-sq approximation of a
corresponding GeneSet
or a list of degrees of freedom for a GeneSetCollection.
This method is applicable for only the chi-sq approximation method.
DF(object)DF(object)
object |
An object of type
|
signature(object = "npGSEAResultChiSq")Returns the degrees of
freedom for a npGSEAResultChiSq object
signature(object = "npGSEAResultChiSqCollection")Returns a
list of the degrees of freedom for a npGSEAResultChiSqCollection
objects (1 for each set)
Jessica L. Larson
npGSEAResultChiSq-class
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15, approx= "chiSq") DF(res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15, approx= "chiSq") DF(res)
geneSetName ~~This function returns the name of the corresponding GeneSet
or a list of names for a GeneSetCollection.
This method is applicable for all three approximation methods.
geneSetName(object)geneSetName(object)
object |
An object of type |
signature(object = "npGSEAResultNorm")Returns a the name of
the gene set from a npGSEAResultNorm object
signature(object = "npGSEAResultBeta")Returns a the name of the
gene set from a npGSEAResultBeta object
signature(object = "npGSEAResultChiSq")Returns a the name of
the gene set from a npGSEAResultChiSq object
signature(object = "npGSEAResultNormCollection")Returns a list of
the names of the gene sets from a npGSEAResultNormCollection objects (1 for each set)
signature(object = "npGSEAResultBetaCollection")Returns a list of
the names of the gene sets from a npGSEAResultBetaCollection objects (1 for each set)
signature(object = "npGSEAResultChiSqCollection")Returns a list of the
names of the gene sets from a npGSEAResultChiSqCollection objects (1 for each set)
Jessica L. Larson
npGSEAResultNorm-class
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) geneSetName (res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) geneSetName (res)
Calculates the incidence of a gene set in an experiment
getIncidence(universeIDs, set)getIncidence(universeIDs, set)
universeIDs |
A vector containing the list of possible gene ids in the universe (or experiment). |
set |
A GeneSet object containing a set of genes of interest |
getIncidence returns an incidence vector of the location of the genes within a gene set in a list of genes in an experiment and vise-versa.
A list of inSet and inExp. inSet is a vector with the same length as universeIDs. Each value of inSet is 1 if the gene is in the set and 0 otherwise. inExp is a vector with the same length as geneIds(set), the number of genes in the set. Each value of inExp is 1 if the gene is in universeIDs and 0 otherwise.
Jessica L. Larson and Art Owen
Jessica L Larson and Art B Owen: Moment based gene set tests. BMC Bioinformatics 2015, 16:132. http://www.biomedcentral.com/1471-2105/16/132
geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") getIncidence(letters, geneSetABC15)geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") getIncidence(letters, geneSetABC15)
This function calculates the permutation gene set enrichment analysis test statistic and p-value without actually running the permutation. We account for the covariance among the genes within the set and approximate the corresponding permutation distribution. For more details on the method see Larson and Owen (2014).
npGSEA(x, y, set, covars = NULL, approx = c("norm", "beta", "chiSq"), w = NULL, epsilonBetaAdj=TRUE, scaleXY=TRUE, uniVarX=TRUE )npGSEA(x, y, set, covars = NULL, approx = c("norm", "beta", "chiSq"), w = NULL, epsilonBetaAdj=TRUE, scaleXY=TRUE, uniVarX=TRUE )
x |
A matrix of expression data or an object of type ExpressionSet.
The columns of x represent samples in a given experiment. The rows are genes.
The names of each row (or |
y |
A vector containing the treatment for each sample. The length of y must be more than 4 for the "chisq" approximation. Each treatment group must have at least two observations for all approximation methods. There can only be two treatment groups. |
covars |
A vector or matrix containing covariate(s) of interest, optional |
set |
A |
approx |
A string of either "norm" (default), "beta" or "chiSq". If "norm", the normal approximation to the non-permutation GSEA is calculated and returned. If "beta", the beta approximation is reported. If "chiSq", the Chi-squared approximation to the permutation GSEA is calculated. |
w |
A vector or list containing the weights of each gene in the set or sets, optional. If w is a list, the number of elements in the list must correspond to the number of gene sets in the collection. |
epsilonBetaAdj |
A boolean indicating whether or to not to use an epsilon adjusted p-value for the Beta approximation. When TRUE, this prevents observed p-values of 0. The default is TRUE. |
scaleXY |
A boolean indicating whether or to not to scale x and y. The default is TRUE. |
uniVarX |
A boolean indicating whether or to not to scale x to have unit variance. The default is TRUE. |
An object with the corresponding GSEA results. If approx="norm" an npGSEAResultNorm
object is returned. If approx="beta" a npGSEAResultBeta object is returned. If approx="chiSq"
a npGSEAResultChiSq object is returned. If set is a GeneSetCollection (i.e., multiple
sets of interest), then the corresponding npGSEAResultNormCollection,
npGSEAResultBetaCollection, or npGSEAResultChiSqCollection is returned.
Jessica L. Larson and Art Owen
Jessica L Larson and Art B Owen: Moment based gene set tests. BMC Bioinformatics 2015, 16:132. http://www.biomedcentral.com/1471-2105/16/132
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15)
npGSEAPlot ~~This function plots the reference distribution and the
corresponding scaled statistic (Z, Beta, or Chi-sq) from the npGSEA analysis
for a given GeneSet.
This method is applicable for all three approximation methods.
npGSEAPlot(object)npGSEAPlot(object)
object |
An object of type |
signature(object = "npGSEAResultNorm")Plots the Z-statistic for a
npGSEAResultNorm object and the standard normal distribution
signature(object = "npGSEAResultBeta")Plots the beta statistic
for a npGSEAResultBeta object and the corresponding reference beta
distribution (with alpha and beta calculated from npGSEA).
signature(object = "npGSEAResultChiSq")Plots the beta
statistic for a npGSEAResultChiSq object and the corresponding reference
chi-squared distribution (with degrees of freedom calculated from npGSEA).
Jessica L. Larson
npGSEAResultNorm-class
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) ##npGSEAPlot (res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) ##npGSEAPlot (res)
"npGSEAResultBeta"
Objects of this class store results from running npGSEA with the beta approximation.
Objects can be created by calls of npGSEA.
geneSetName:Object of class "character", the name of the geneSet
betaStat:Object of class "numeric" , the test statistic, scaled to the corresponding beta distribution
ThatGw:Object of class "numeric", the test statistic for the set
varThatGw:Object of class "numeric", the variance of ThatGw
alpha:Object of class "numeric", the alpha value
beta:Object of class "numeric", the beta value
pLeft:Object of class "numeric", the p-value for the left-side hypothesis
pRight:Object of class "numeric", the p-value for the right-side hypothesis
pTwoSided:Object of class "numeric", the p-value for the two-sided hypothesis
xSet:Object of class "matrix", the centered and scaled x data for this set
betaHats:Object of class "vector", the betaHats for each gene in this set
Jessica L. Larson
Jessica L Larson and Art B Owen: Moment based gene set tests. BMC Bioinformatics 2015, 16:132. http://www.biomedcentral.com/1471-2105/16/132
showClass("npGSEAResultBeta")showClass("npGSEAResultBeta")
"npGSEAResultBetaCollection"
Objects of this class store results from running npGSEA with the beta approximation with a GeneSetCollection. npGSEAResultBetaCollection objects contain a list of npGSEAResultBeta objects (one result for each GeneSet).
Objects can be created by calls of npGSEA.
Jessica L. Larson
Jessica L Larson and Art B Owen: Moment based gene set tests. BMC Bioinformatics 2015, 16:132. http://www.biomedcentral.com/1471-2105/16/132
showClass("npGSEAResultBetaCollection")showClass("npGSEAResultBetaCollection")
"npGSEAResultChiSq"
Objects of this class store results from running npGSEA with the Chi-square approximation.
Objects can be created by calls of npGSEA.
geneSetName:Object of class "character", the name of the geneSet
chiSqStat:Object of class "numeric" , the test statistic, scaled to the corresponding chi-sq distribution
ChatGw:Object of class "numeric", the test statistic for the set
sigmaSq:Object of class "numeric", the variance
DF:Object of class "numeric", the degrees of freedom
pTwoSided:Object of class "numeric", the p-value for the two-sided hypothesis
xSet:Object of class "matrix", the centered and scaled x data for this set
betaHats:Object of class "vector", the betaHats for each gene in this set
Jessica L. Larson
Jessica L Larson and Art B Owen: Moment based gene set tests. BMC Bioinformatics 2015, 16:132. http://www.biomedcentral.com/1471-2105/16/132
showClass("npGSEAResultChiSq")showClass("npGSEAResultChiSq")
"npGSEAResultChiSqCollection"
Objects of this class store results from running npGSEA with the Chi-square approximation with a GeneSetCollection. npGSEAResultChiSqCollection objects contain a list of npGSEAResultChiSq objects (one result for each GeneSet).
Objects can be created by calls of npGSEA.
Jessica L. Larson
Jessica L Larson and Art B Owen: Moment based gene set tests. BMC Bioinformatics 2015, 16:132. http://www.biomedcentral.com/1471-2105/16/132
showClass("npGSEAResultChiSqCollection")showClass("npGSEAResultChiSqCollection")
"npGSEAResultNorm"
Objects of this class store results from running npGSEA with the Gaussian approximation.
Objects can be created by calls of npGSEA.
geneSetName:Object of class "character", the name of the geneSet
zStat:Object of class "numeric" , the test statistic, scaled to a standard normal
ThatGw:Object of class "numeric", the test statistic for the set
varThatGw:Object of class "numeric", the variance of ThatGw
pLeft:Object of class "numeric", the p-value for the left-side hypothesis
pRight:Object of class "numeric", the p-value for the right-side hypothesis
pTwoSided:Object of class "numeric", the p-value for the two-sided hypothesis
xSet:Object of class "matrix", the centered and scaled x data for this set
betaHats:Object of class "vector", the betaHats for each gene in this set
Jessica L. Larson
Jessica L Larson and Art B Owen: Moment based gene set tests. BMC Bioinformatics 2015, 16:132. http://www.biomedcentral.com/1471-2105/16/132
showClass("npGSEAResultNorm")showClass("npGSEAResultNorm")
"npGSEAResultNormCollection"
Objects of this class store results from running npGSEA with the Gaussian approximation with a GeneSetCollection. npGSEAResultNormCollection objects contain a list of npGSEAResultNorm objects (one result for each GeneSet).
Objects can be created by calls of npGSEA.
Jessica L. Larson
Jessica L Larson and Art B Owen: Moment based gene set tests. BMC Bioinformatics 2015, 16:132. http://www.biomedcentral.com/1471-2105/16/132
showClass("npGSEAResultNormCollection")showClass("npGSEAResultNormCollection")
pLeft ~~This function returns the left-sided p-value for the corresponding GeneSet
or a list of p-values for a GeneSetCollection.
This method is only applicable for the normal and beta approximation methods.
pLeft(object)pLeft(object)
object |
An object of type |
signature(object = "npGSEAResultNorm")Returns a left-sided p-value
for a npGSEAResultNorm object
signature(object = "npGSEAResultBeta")Returns a left-sided p-value
for a npGSEAResultBeta object
signature(object = "npGSEAResultNormCollection")Returns a list of
left-sided p-values for a npGSEAResultNormCollection objects (1 for each set)
signature(object = "npGSEAResultBetaCollection")Returns a list of
left-sided p-values for a npGSEAResultBetaCollection objects (1 for each set)
Jessica L. Larson
npGSEAResultNorm-class, pRight
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) pLeft (res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) pLeft (res)
pRight ~~This function returns the right-sided p-value for the corresponding GeneSet
or a list of p-values for a GeneSetCollection.
This method is only applicable for the normal and beta approximation methods.
pRight(object)pRight(object)
object |
An object of type |
signature(object = "npGSEAResultNorm")Returns a right-sided
p-value for a npGSEAResultNorm object
signature(object = "npGSEAResultBeta")Returns a right-sided
p-value for a npGSEAResultBeta object
signature(object = "npGSEAResultNormCollection")Returns a list
of right-sided p-values for a npGSEAResultNormCollection objects
(1 for each set)
signature(object = "npGSEAResultBetaCollection")Returns a list of
right-sided p-values for a npGSEAResultBetaCollection objects
(1 for each set)
Jessica L. Larson
npGSEAResultNorm-class, pLeft
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) pRight (res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) pRight (res)
pTwoSided ~~This function returns the two-sided p-value for the corresponding GeneSet
or a list of p-values for a GeneSetCollection.
This method is applicable for all three approximation methods.
pTwoSided(object)pTwoSided(object)
object |
An object of type |
signature(object = "npGSEAResultNorm")Returns a two-sided
p-value for a npGSEAResultNorm object
signature(object = "npGSEAResultBeta")Returns a two-sided
p-value for a npGSEAResultBeta object
signature(object = "npGSEAResultChiSq")Returns a two-sided
p-value for a npGSEAResultChiSq object
signature(object = "npGSEAResultNormCollection")Returns a list
of left-sided p-values for a npGSEAResultNormCollection objects
(1 for each set)
signature(object = "npGSEAResultBetaCollection")Returns a list of
left-sided p-values for a npGSEAResultBetaCollection objects (1 for each set)
signature(object = "npGSEAResultChiSqCollection")Returns a list of
two-sided p-values for a npGSEAResultChiSqCollection objects (1 for each set)
Jessica L. Larson
npGSEAResultNorm-class, pRight
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) pTwoSided (res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) pTwoSided (res)
pValues ~~~~ Methods for function pValues ~~
signature(x = "npGSEAResultNorm"),signature(x = "npGSEAResultBeta"),
signature(x = "npGSEAResultChiSq")
These methods display the corresponding p-values for the npGSEA analysis in the gene set in the given experiment.
Jessica L. Larson
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) pValues(res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) pValues(res)
sigmaSq ~~This function returns the corresponding variance of the statistic (linear or quadratic) from the npGSEA analysis
for a given GeneSet,
or a list of these variances for a given GeneSetCollection.
This method is applicable for all three approximation methods.
sigmaSq(object)sigmaSq(object)
object |
An object of type |
signature(object = "npGSEAResultNorm")Returns the variance
of the linear statistic for a npGSEAResultNorm object
signature(object = "npGSEAResultBeta")Returns the variance
of the linear statistic for a npGSEAResultBeta object
signature(object = "npGSEAResultChiSq")Returns the variance
of the quadratic statistic for a npGSEAResultChiSq object
signature(object = "npGSEAResultNormCollection")Returns a list
of the variances of the linear statistics for a npGSEAResultNormCollection
objects (1 for each set)
signature(object = "npGSEAResultBetaCollection")Returns a list of
the variances of the linear statistics for a npGSEAResultBetaCollection
objects (1 for each set)
signature(object = "npGSEAResultChiSqCollection")Returns a list of
the variances of the linear statistics for a npGSEAResultChiSqCollection
objects (1 for each set)
Jessica L. Larson
npGSEAResultNorm-class
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) sigmaSq(res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) sigmaSq(res)
stat ~~This function returns the corresponding statistic (linear or quadratic) from the npGSEA analysis
for a given GeneSet,
or a list of these statistics for a given GeneSetCollection.
This method is applicable for all three approximation methods.
stat(object)stat(object)
object |
An object of type |
signature(object = "npGSEAResultNorm")Returns the linear statistic
for a npGSEAResultNorm object
signature(object = "npGSEAResultBeta")Returns the linear statistic
for a npGSEAResultBeta object
signature(object = "npGSEAResultChiSq")Returns the quadratic
statistic for a npGSEAResultChiSq object
signature(object = "npGSEAResultNormCollection")Returns a list of
the linear statistics for a npGSEAResultNormCollection objects (1 for each set)
signature(object = "npGSEAResultBetaCollection")Returns a list of
the linear statistics for a npGSEAResultBetaCollection objects (1 for each set)
signature(object = "npGSEAResultChiSqCollection")Returns a list of
the quadratic statistics for a npGSEAResultChiSqCollection objects (1 for each set)
Jessica L. Larson
npGSEAResultNorm-class
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) stat(res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) stat(res)
show in Package base ~~~~ Methods for function show in package base ~~
signature(x = "npGSEAResultNorm"),signature(x = "npGSEAResultBeta"),
signature(x = "npGSEAResultChiSq")
These methods display the corresponding statistics (linear or quadratic)for the npGSEA analysis in the gene set in the given experiment.
Jessica L. Larson
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) resset.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) res
xSet ~~This function returns the scaled and centered expression data
for all genes in the corresponding GeneSet in the given experiment,
or a list of such matrices for each set in a GeneSetCollection.
This method is applicable for all three approximation methods.
xSet(object)xSet(object)
object |
An object of type |
signature(object = "npGSEAResultNorm")Returns the centered and
scaled X matrix used for analysis to create a npGSEAResultNorm object
signature(object = "npGSEAResultBeta")Returns the centered and
scaled X matrix used for analysis to create a npGSEAResultBeta object
signature(object = "npGSEAResultChiSq")Returns the centered and
scaled X matrix used for analysis to create a npGSEAResultChiSq object
signature(object = "npGSEAResultNormCollection")Returns a list of
centered and scaled X matrices used for analysis to create the
npGSEAResultNormCollection objects (1 for each set)
signature(object = "npGSEAResultBetaCollection")Returns a list of
centered and scaled X matrices used for analysis to create the
npGSEAResultBetaCollection objects (1 for each set)
signature(object = "npGSEAResultChiSqCollection")Returns a list of
centered and scaled X matrices used for analysis to create the
npGSEAResultChiSqCollection objects (1 for each set)
npGSEAResultNorm-class
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) head( xSet(res) )set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15) head( xSet(res) )
zStat ~~This function returns the Z-statistic (which is compared to a reference
standard normal distribution) for the normal approximation of npGSEA for
a corresponding GeneSet
or a list of these statistics for a GeneSetCollection.
This method is applicable for only the normal approximation method.
zStat(object)zStat(object)
object |
An object of type
|
signature(object = "npGSEAResultNorm")Returns the
Z-statistic for a npGSEAResultNorm object
signature(object = "npGSEAResultNormCollection")Returns a
list of the Z- statistics for a npGSEAResultNormCollection objects (1 for each set)
Jessica L. Larson
npGSEAResultNorm-class
set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15, approx= "norm") zStat(res)set.seed(15) yFactor <- as.factor( c(rep("treated", 5), rep("control", 5)) ) xData <- matrix(data=rnorm(length(letters)*10) ,nrow=length(letters), ncol=10) rownames(xData) <- letters geneSetABC15 <- GeneSet(geneIds=letters[1:15], setName="setABC15") res <- npGSEA(x = xData, y = yFactor, set = geneSetABC15, approx= "norm") zStat(res)