| Title: | Analysis of an ICA decomposition obtained on genomics data |
|---|---|
| Description: | The goal of MineICA is to perform Independent Component Analysis (ICA) on multiple transcriptome datasets, integrating additional data (e.g molecular, clinical and pathological). This Integrative ICA helps the biological interpretation of the components by studying their association with variables (e.g sample annotations) and gene sets, and enables the comparison of components from different datasets using correlation-based graph. |
| Authors: | Anne Biton |
| Maintainer: | Anne Biton <[email protected]> |
| License: | GPL-2 |
| Version: | 1.47.0 |
| Built: | 2024-10-31 01:18:27 UTC |
| Source: | https://github.com/bioc/MineICA |
These generic functions access and set the attributes S, SByGene and A
stored in an object of class IcaSet.
S(object) S(object) <- value SByGene(object) SByGene(object) <- value A(object) A(object) <- value nbComp(object)S(object) S(object) <- value SByGene(object) SByGene(object) <- value A(object) A(object) <- value nbComp(object)
object |
object of class |
value |
Data.frame with rows representing: features (for
|
S returns a data.frame containing feature projection values;
SByGene returns a data.frame containing gene projection values;
A returns a data.frame containing sample contribution values.
nbComp returns the number of components, i.e the number of
columns of A.
Anne Biton
IcaSet object
as a list.This generic function retrieves, from an IcaSet object,
the sample contributions contained in
the attribute A as
a list where sample IDs are preserved.
Alist(object)Alist(object)
object |
Object of class |
Alist returns a list whose length equals the number of
components contained in the IcaSet object. Each element of this
list contains a vector of sample contributions
indexed by the sample IDs.
Anne Biton
class-IcaSet
Given a data.frame consisting of sample annotations, this function returns a vector which gives a colour per annotation level.
annot2Color(annot)annot2Color(annot)
annot |
a data.frame containing the sample annotations (of dimension 'samples x annotations'). |
Arbitrary colours are attributed to some specific
annotations met by the author, and for the remaining
annotation levels, the colours are attributed using
packages RColorBrewer and rcolorspace.
A vector of colours indexed by the annotation levels.
Anne Biton
Contains annotations for 93 samples of Carbayo data.
Anne Biton
http://jco.ascopubs.org/content/24/5/778/suppl/DC1
This function annotates a set of features
annotFeatures(features, type, annotation)annotFeatures(features, type, annotation)
features |
Feature IDs to be annotated |
type |
The object from the package used to annotate
the features, must be available in
|
annotation |
An annotation package |
A vector of gene/object IDs indexed by the feature IDs.
Anne Biton
library(hgu133a.db) annotFeatures(features = c("1007_s_at", "1053_at", "117_at", "121_at", "1255_g_at"), type="SYMBOL", annotation="hgu133a.db")library(hgu133a.db) annotFeatures(features = c("1007_s_at", "1053_at", "117_at", "121_at", "1255_g_at"), type="SYMBOL", annotation="hgu133a.db")
##' This function annotates the features of an object of
class IcaSet, and fills its attributes
SByGene and datByGene.
annotFeaturesComp(icaSet, params, type = toupper(typeID(icaSet)["geneID_annotation"]), featureId = typeID(icaSet)["featureID_biomart"], geneId = typeID(icaSet)["geneID_biomart"])annotFeaturesComp(icaSet, params, type = toupper(typeID(icaSet)["geneID_annotation"]), featureId = typeID(icaSet)["featureID_biomart"], geneId = typeID(icaSet)["geneID_biomart"])
icaSet |
An object of class |
params |
An object of class
|
type |
The ID of the object of the annotation
package to be used for the annotation, must be available
in |
featureId |
The type of the feature IDs, in the
|
geneId |
The type of the gene IDs, in the
|
This function is called by function
annotInGene which will check the validity
of the attributes annotation, typeID, chipManu and
eventually chipVersion of icaSet. If
available, the attribute annotation of argument
icaSet must be an annotation package and will be
used to annotate the featureNames of
icaSet. If attribute annotation of argument
icaSet is not available (of length 0),
biomaRt is used to annotate the features.
This function fills the attributes SByGene and
datByGene of the argument icaSet. When
several feature IDs are available for a same gene ID, the
median value of the corresponding features IDs is
attributed to the gene (the median of projection values
is used for attribute SByGene, and the median of
expression values is used for attribute
datByGene).
When attribute chipManu of the argument
icaSet is "illumina", the features are first
converted into nuID using the package 'lumi*Mapping' and
then annotated into genes. In that case, features can
only be annotated in ENTREZID or SYMBOL. It means that
typeID(icaSet)['geneID_annotation'] must be either
'ENTREZID' or 'SYMBOL'. You will need to annotate
yourself the IcaSet object if you want to
use different IDs.
This function returns the argument icaSet with
attributes SByGene and datByGene filled.
Anne Biton
annotFeatures,
annotFeaturesWithBiomaRt,
annotInGene
## load an example of IcaSet data(icaSetCarbayo) params <- buildMineICAParams() require(hgu133a.db) ####=================================================== ## Use of annotation package contained in annotation(icaSet) ####==================================================== ## annotation in SYMBOL icaSetCarbayo_annot <- annotFeaturesComp(icaSet=icaSetCarbayo, params=params, type="SYMBOL") # arg 'type' is optional since the function uses contents of typeID(icaSet) as the defaults, # it is specified in these examples for pedagogy views ## annotation in Entrez Gene icaSetCarbayo_annot <- annotFeaturesComp(icaSet=icaSetCarbayo, params=params, type="ENTREZID") ## Not run: ####=================================================== ## Use of biomaRt, when annotation(icaSet) is of length 0 ####==================================================== ## empty attribute 'annotation' of the IcaSet object # when this attribute is not specified, biomaRt is used for annotation annotation(icaSetCarbayo) <- character() # make sure the mart attribute is correctly defined mart(icaSetCarbayo) <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl") ## make sure elements "featureID_biomaRt" and "geneID_biomaRt" of typeID(icaSet) are correctly filled # they will be used by function 'annotFeaturesComp' through biomaRt to query the database typeID(icaSetCarbayo) ## run annotation of HG-U133A probe set IDs into Gene Symbols using biomaRt icaSetCarbayo_annot <- annotFeaturesComp(icaSet=icaSetCarbayo, params=params) ## End(Not run)## load an example of IcaSet data(icaSetCarbayo) params <- buildMineICAParams() require(hgu133a.db) ####=================================================== ## Use of annotation package contained in annotation(icaSet) ####==================================================== ## annotation in SYMBOL icaSetCarbayo_annot <- annotFeaturesComp(icaSet=icaSetCarbayo, params=params, type="SYMBOL") # arg 'type' is optional since the function uses contents of typeID(icaSet) as the defaults, # it is specified in these examples for pedagogy views ## annotation in Entrez Gene icaSetCarbayo_annot <- annotFeaturesComp(icaSet=icaSetCarbayo, params=params, type="ENTREZID") ## Not run: ####=================================================== ## Use of biomaRt, when annotation(icaSet) is of length 0 ####==================================================== ## empty attribute 'annotation' of the IcaSet object # when this attribute is not specified, biomaRt is used for annotation annotation(icaSetCarbayo) <- character() # make sure the mart attribute is correctly defined mart(icaSetCarbayo) <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl") ## make sure elements "featureID_biomaRt" and "geneID_biomaRt" of typeID(icaSet) are correctly filled # they will be used by function 'annotFeaturesComp' through biomaRt to query the database typeID(icaSetCarbayo) ## run annotation of HG-U133A probe set IDs into Gene Symbols using biomaRt icaSetCarbayo_annot <- annotFeaturesComp(icaSet=icaSetCarbayo, params=params) ## End(Not run)
biomaRt
This function annotates a set of features using
biomaRt
annotFeaturesWithBiomaRt(features, featureId, geneId, mart = useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl"))annotFeaturesWithBiomaRt(features, featureId, geneId, mart = useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl"))
features |
Feature IDs to be annotated |
featureId |
The type of the feature IDs, in the
|
geneId |
The type of the gene IDs, in the
|
mart |
The mart object (database and dataset) used
for annotation, see function |
A vector of gene IDs indexed by the feature IDs.
Anne Biton
if (interactive()) { # define the database to be queried by biomaRt mart <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl") # annotate a set of HG-U133a probe sets IDs into Gene Symbols annotFeaturesWithBiomaRt(features = c("1007_s_at", "1053_at", "117_at", "121_at", "1255_g_at"), featureId="affy_hg_u133a", geneId="hgnc_symbol", mart=mart) # annotate a set of Ensembl Gene IDs into Gene Symbols annotFeaturesWithBiomaRt(features = c("ENSG00000101412", "ENSG00000112242", "ENSG00000148773", "ENSG00000131747", "ENSG00000170312", "ENSG00000117399"), featureId="ensembl_gene_id", geneId="hgnc_symbol", mart=mart) }if (interactive()) { # define the database to be queried by biomaRt mart <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl") # annotate a set of HG-U133a probe sets IDs into Gene Symbols annotFeaturesWithBiomaRt(features = c("1007_s_at", "1053_at", "117_at", "121_at", "1255_g_at"), featureId="affy_hg_u133a", geneId="hgnc_symbol", mart=mart) # annotate a set of Ensembl Gene IDs into Gene Symbols annotFeaturesWithBiomaRt(features = c("ENSG00000101412", "ENSG00000112242", "ENSG00000148773", "ENSG00000131747", "ENSG00000170312", "ENSG00000117399"), featureId="ensembl_gene_id", geneId="hgnc_symbol", mart=mart) }
This function annotates the features of an
IcaSet object and fills its attributes
SByGene and datByGene.
annotInGene(icaSet, params, annot = TRUE)annotInGene(icaSet, params, annot = TRUE)
icaSet |
An object of class |
params |
An object of class
|
annot |
TRUE (default) if the IcaSet object must indeed be annotated |
When attribute annotation of icaSet is not
specified (of length 0), biomaRt is used to
annotate the features through function
annotFeaturesWithBiomaRt.
When specified, attribute annotation of argument
icaSet must be an annotation package and will be
used to annotate the featureNames of
icaSet. In addition, the attribute typeID
(a vector) of argument icaSet must contain a valid
element geneID_annotation that determines the
object of the package to be used for the annotation, see
IcaSet.
When argument annot is TRUE, this function fills
the attributes SByGene and datByGene of
icaSet. When several feature IDs are available for
a same gene ID, the median value of the corresponding
features IDs is attributed to the gene (the median of the
projection values is used for attribute SByGene,
and the median of the expression values is used for
attribute datByGene).
When attribute chipManu of the argument
icaSet is "illumina", the features are first
converted into nuID using the package 'lumi*Mapping' and
then annotated into genes. In that case, features can
only be annotated in ENTREZID or SYMBOL. It means that
typeID(icaSet)['geneID_annotation'] must be either
'ENTREZID' or 'SYMBOL'. You will need to annotate
yourself the IcaSet object if you want to use different
IDs.
The modified argument icaSet, with filled
attributes SByGene and datByGene.
Anne Biton
#load data data(icaSetCarbayo) require(hgu133a.db) # run annotation of the features into gene Symbols as specified in 'typeID(icaSetCarbayo)["geneID_annotation"]', # using package hgu133a.db as defined in 'annotation(icaSetMainz)' icaSetCarbayo <- annotInGene(icaSet=icaSetCarbayo, params=buildMineICAParams()) ## Not run: #load data library(breastCancerMAINZ) data(mainz) #run ICA resJade <- runICA(X=exprs(mainz), nbComp=5, method = "JADE", maxit=10000) #build params params <- buildMineICAParams(resPath="mainz/") #build a new IcaSet object, omitting annotation of the features (runAnnot=FALSE) #but specifying the element "geneID_annotation" of argument 'typeID' icaSetMainz <- buildIcaSet(params=params, A=data.frame(resJade$A), S=data.frame(resJade$S), dat=exprs(mainz), pData=pData(mainz), annotation="hgu133a.db", typeID= c(geneID_annotation = "SYMBOL", geneID_biomart = "hgnc_symbol", featureID_biomart = "affy_hg_u133a"), chipManu = "affymetrix", runAnnot=FALSE, mart=useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl")) #Attributes SByGene is empty and attribute datByGene refers to assayData SByGene(icaSetMainz) head(datByGene(icaSetMainz)) # run annotation of the features into gene Symbols as specified in 'typeID(icaSetMainz)["geneID_annotation"]', # using package hgu133a.db as defined in 'annotation(icaSetMainz)' icaSetMainz <- annotInGene(icaSet=icaSetMainz, params=params) ## End(Not run)#load data data(icaSetCarbayo) require(hgu133a.db) # run annotation of the features into gene Symbols as specified in 'typeID(icaSetCarbayo)["geneID_annotation"]', # using package hgu133a.db as defined in 'annotation(icaSetMainz)' icaSetCarbayo <- annotInGene(icaSet=icaSetCarbayo, params=buildMineICAParams()) ## Not run: #load data library(breastCancerMAINZ) data(mainz) #run ICA resJade <- runICA(X=exprs(mainz), nbComp=5, method = "JADE", maxit=10000) #build params params <- buildMineICAParams(resPath="mainz/") #build a new IcaSet object, omitting annotation of the features (runAnnot=FALSE) #but specifying the element "geneID_annotation" of argument 'typeID' icaSetMainz <- buildIcaSet(params=params, A=data.frame(resJade$A), S=data.frame(resJade$S), dat=exprs(mainz), pData=pData(mainz), annotation="hgu133a.db", typeID= c(geneID_annotation = "SYMBOL", geneID_biomart = "hgnc_symbol", featureID_biomart = "affy_hg_u133a"), chipManu = "affymetrix", runAnnot=FALSE, mart=useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl")) #Attributes SByGene is empty and attribute datByGene refers to assayData SByGene(icaSetMainz) head(datByGene(icaSetMainz)) # run annotation of the features into gene Symbols as specified in 'typeID(icaSetMainz)["geneID_annotation"]', # using package hgu133a.db as defined in 'annotation(icaSetMainz)' icaSetMainz <- annotInGene(icaSet=icaSetMainz, params=params) ## End(Not run)
This function notes edges of a graph as reciprocal or not.
annotReciprocal(dataGraph, file, keepOnlyReciprocal = FALSE)annotReciprocal(dataGraph, file, keepOnlyReciprocal = FALSE)
dataGraph |
data.frame which contains the graph
description, must have two columns |
file |
file where the graph description is written |
keepOnlyReciprocal |
if TRUE |
This function returns the argument dataGraph with
an additional column named 'reciprocal' which contains
TRUE if the edge described by the row is reciprocal, and
FALSE if it is not reciprocal.
Anne Biton
dg <- data.frame(n1=c("A","B","B","C","C","D","E","F"),n2=c("B","G","A","B","D","C","F","E")) annotReciprocal(dataGraph=dg)dg <- data.frame(n1=c("A","B","B","C","C","D","E","F"),n2=c("B","G","A","B","D","C","F","E")) annotReciprocal(dataGraph=dg)
IcaSet.This function builds an object of class
IcaSet.
buildIcaSet(params, A, S, dat, pData = new("data.frame"), fData = new("data.frame"), witGenes = new("character"), compNames = new("character"), refSamples = new("character"), annotation = new("character"), chipManu = new("character"), chipVersion = new("character"), alreadyAnnot = FALSE, typeID = c(geneID_annotation = "SYMBOL", geneID_biomart = "hgnc_symbol", featureID_biomart = ""), runAnnot = TRUE, organism = "Human", mart = new("Mart"))buildIcaSet(params, A, S, dat, pData = new("data.frame"), fData = new("data.frame"), witGenes = new("character"), compNames = new("character"), refSamples = new("character"), annotation = new("character"), chipManu = new("character"), chipVersion = new("character"), alreadyAnnot = FALSE, typeID = c(geneID_annotation = "SYMBOL", geneID_biomart = "hgnc_symbol", featureID_biomart = ""), runAnnot = TRUE, organism = "Human", mart = new("Mart"))
params |
An object of class
|
A |
The mixing matrix of the ICA decomposition (of dimension samples x components). |
S |
The source matrix of the ICA decomposition (of dimension features x components). |
dat |
The data matrix the ICA was applied to (of dimension features x samples). |
pData |
Phenotype data, a data.frame which contains the sample informations of dimension samples x annotations. |
fData |
Feature data, a data.frame which contrains the feature descriptions of dimensions features x annotations. |
witGenes |
A vector of witness genes. They are
representative of the expression behavior of the
contributing genes of each component. If missing or NULL,
they will be automatically attributed using function
|
compNames |
A vector of component labels. |
refSamples |
A vector of reference sample IDs (e.g the "normal" samples). |
annotation |
An annotation package (e.g a ".db"
package specific to the microarray used to generate
|
chipManu |
If microarray data, the manufacturer: either 'affymetrix' or 'illumina'. |
chipVersion |
For illumina microarrays: the version of the microarray. |
alreadyAnnot |
TRUE if the feature IDs contained in
the row names of |
typeID |
A character vector specifying the annotation IDs, it includes three elements :
|
runAnnot |
If TRUE, |
organism |
The organism the data correspond to. |
mart |
The mart object (database and dataset) used
for annotation, see function |
An object of class IcaSet
Anne Biton
selectWitnessGenes,
annotInGene
dat <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat) <- paste("g", 1:1000, sep="") colnames(dat) <- paste("s", 1:10, sep="") ## build a data.frame containing sample annotations annot <- data.frame(type=c(rep("a",5),rep("b",5))) rownames(annot) <- colnames(dat) ## run ICA resJade <- runICA(X=dat, nbComp=3, method = "JADE") ## build params params <- buildMineICAParams(resPath="toy/") ## build IcaSet object icaSettoy <- buildIcaSet(params=params, A=data.frame(resJade$A), S=data.frame(resJade$S), dat=dat, pData=annot, alreadyAnnot=TRUE) params <- icaSettoy$params icaSettoy <- icaSettoy$icaSet ## Not run: ## load data library(breastCancerMAINZ) data(mainz) ## run ICA resJade <- runICA(X=dataMainz, nbComp=10, method = "JADE", maxit=10000) ## build params params <- buildMineICAParams(resPath="mainz/") ## build IcaSet object # fill typeID, Mainz data originate from affymetrix HG-U133a microarray and are indexed by probe sets # we want to annotate the probe sets into Gene Symbols typeIDmainz <- c(geneID_annotation="SYMBOL", geneID_biomart="hgnc_symbol", featureID_biomart="affy_hg_u133a") icaSetMainz <- buildIcaSet(params=params, A=data.frame(resJade$A), S=data.frame(resJade$S), dat=exprs(mainz), pData=pData(mainz), annotation="hgu133a.db", typeID= c(geneID_annotation = "SYMBOL", geneID_biomart = "hgnc_symbol", featureID_biomart = "affy_hg_u133a"), chipManu = "affymetrix", runAnnot=TRUE, mart=useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl")) ## End(Not run)dat <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat) <- paste("g", 1:1000, sep="") colnames(dat) <- paste("s", 1:10, sep="") ## build a data.frame containing sample annotations annot <- data.frame(type=c(rep("a",5),rep("b",5))) rownames(annot) <- colnames(dat) ## run ICA resJade <- runICA(X=dat, nbComp=3, method = "JADE") ## build params params <- buildMineICAParams(resPath="toy/") ## build IcaSet object icaSettoy <- buildIcaSet(params=params, A=data.frame(resJade$A), S=data.frame(resJade$S), dat=dat, pData=annot, alreadyAnnot=TRUE) params <- icaSettoy$params icaSettoy <- icaSettoy$icaSet ## Not run: ## load data library(breastCancerMAINZ) data(mainz) ## run ICA resJade <- runICA(X=dataMainz, nbComp=10, method = "JADE", maxit=10000) ## build params params <- buildMineICAParams(resPath="mainz/") ## build IcaSet object # fill typeID, Mainz data originate from affymetrix HG-U133a microarray and are indexed by probe sets # we want to annotate the probe sets into Gene Symbols typeIDmainz <- c(geneID_annotation="SYMBOL", geneID_biomart="hgnc_symbol", featureID_biomart="affy_hg_u133a") icaSetMainz <- buildIcaSet(params=params, A=data.frame(resJade$A), S=data.frame(resJade$S), dat=exprs(mainz), pData=pData(mainz), annotation="hgu133a.db", typeID= c(geneID_annotation = "SYMBOL", geneID_biomart = "hgnc_symbol", featureID_biomart = "affy_hg_u133a"), chipManu = "affymetrix", runAnnot=TRUE, mart=useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl")) ## End(Not run)
This function builds an object of class
MineICAParams.
It contains the parameters that will be used by function
runAn to analyze the ICA decomposition
contained in an object of class
IcaSet.
buildMineICAParams(Sfile = new("character"), Afile = new("character"), datfile = new("character"), annotfile = new("character"), resPath = "", genesPath, annot2col = new("character"), pvalCutoff = 0.05, selCutoff = 3)buildMineICAParams(Sfile = new("character"), Afile = new("character"), datfile = new("character"), annotfile = new("character"), resPath = "", genesPath, annot2col = new("character"), pvalCutoff = 0.05, selCutoff = 3)
Sfile |
A txt file containing the Source matrix S. |
Afile |
A txt file containing the Mixing matrix A. |
datfile |
A txt file containing the data (e.g expression data) on which the decomposition was calculated. |
annotfile |
Either a "rda" or "txt" file containing the annotation data for the samples (must be of dimensions samples x annotations). |
resPath |
The path where the outputs of the analysis will be written, default is the current directory. |
genesPath |
The path _within_ the resPath where the
gene projections will be written. If missing, will be
automatically attributed as |
annot2col |
A vector of colors indexed by annotation
levels. If missing, will be automatically attributed
using function |
pvalCutoff |
The cutoff used to consider a p-value significant, default is 0.05. |
selCutoff |
The cutoff applied to the absolute feature/gene projection values to consider them as contributors, default is 3. Must be either of length 1 and the same treshold is applied to all components, or of length equal to the number of components in order to a specific threshold is for each component. |
An object of class MineICAParams
Anne Biton
## define default parameters and fill resPath params <- buildMineICAParams(resPath="resMineICACarbayo/") ## change the default cutoff for selection of contribugint genes/features params <- buildMineICAParams(resPath="resMineICACarbayo/", selCutoff=4)## define default parameters and fill resPath params <- buildMineICAParams(resPath="resMineICACarbayo/") ## change the default cutoff for selection of contribugint genes/features params <- buildMineICAParams(resPath="resMineICACarbayo/", selCutoff=4)
This function runs the fastICA algorithm several times with random initializations. The obtained components are clustered and the medoids of these clusters are used as the final estimates. The returned estimates are ordered by decreasing Iq values which measure the compactness of the clusters (see details).
clusterFastICARuns(X, nbComp, nbIt = 100, alg.type = c("deflation", "parallel"), fun = c("logcosh", "exp"), maxit = 500, tol = 10^-6, funClus = c("hclust", "agnes", "pam", "kmeans"), row.norm = FALSE, bootstrap = FALSE, ...)clusterFastICARuns(X, nbComp, nbIt = 100, alg.type = c("deflation", "parallel"), fun = c("logcosh", "exp"), maxit = 500, tol = 10^-6, funClus = c("hclust", "agnes", "pam", "kmeans"), row.norm = FALSE, bootstrap = FALSE, ...)
X |
A data matrix with n rows representing observations (e.g genes) and p columns representing variables (e.g samples). |
nbComp |
The number of components to be extracted. |
nbIt |
The number of iterations of FastICA |
alg.type |
If |
fun |
The functional form of the G function used in
the approximation to neg-entropy (see 'details' of the
help of function |
row.norm |
a logical value indicating whether rows
of the data matrix |
maxit |
The maximum number of iterations to perform. |
tol |
A positive scalar giving the tolerance at which the un-mixing matrix is considered to have converged. |
funClus |
The clustering function to be used to cluster the estimates |
bootstrap |
if TRUE the data is bootstraped before each fastICA iteration, else (default) only random initializations are done |
... |
Additional parameters for codefunClus |
This function implements in R fastICA iterations followed by a clustering step, as defined in the matlab package 'icasso'. Among the indices computed by icasso, only the Iq index is currently computed. As defined in 'icasso', the Iq index measures the difference between the intra-cluster similarity and the extra-cluster similiarity. No visualization of the clusters is yet available.
If bootstrap=TRUE a bootstrap (applied to the
observations) is used to perturb the data before each
iteration, then function fastICA is applied with
random initializations.
By default, in 'icasso', agglomerative hierarchical
clustering with average linkage is performed. To use the
same clustering, please use funClus="hclust" and
method="average". But this function also allows
you to apply the clustering of your choice among
kmeans, pam, hclust, agnes by specifying
funClus and adding the adequat additional
parameters.
See details of the functions
fastICA.
A list consisting of:
the estimated mixing matrix
the estimated source matrix
, itemWthe estimated unmixing matrix,
Iq indices.
Anne Biton
## generate a data set.seed(2004); M <- matrix(rnorm(5000*6,sd=0.3),ncol=10) M[1:100,1:3] <- M[1:100,1:3] + 2 M[1:200,1:3] <- M[1:200,4:6] +1 ## Random initializations are used for each iteration of FastICA ## Estimates are clustered using hierarchical clustering with average linkage res <- clusterFastICARuns(X=M, nbComp=2, alg.type="deflation", nbIt=3, funClus="hclust", method="average") ## Data are boostraped before each iteration and random initializations ## are used for each iteration of FastICA ## Estimates are clustered using hierarchical clustering with ward res <- clusterFastICARuns(X=M, nbComp=2, alg.type="deflation", nbIt=3, funClus="hclust", method="ward")## generate a data set.seed(2004); M <- matrix(rnorm(5000*6,sd=0.3),ncol=10) M[1:100,1:3] <- M[1:100,1:3] + 2 M[1:200,1:3] <- M[1:200,4:6] +1 ## Random initializations are used for each iteration of FastICA ## Estimates are clustered using hierarchical clustering with average linkage res <- clusterFastICARuns(X=M, nbComp=2, alg.type="deflation", nbIt=3, funClus="hclust", method="average") ## Data are boostraped before each iteration and random initializations ## are used for each iteration of FastICA ## Estimates are clustered using hierarchical clustering with ward res <- clusterFastICARuns(X=M, nbComp=2, alg.type="deflation", nbIt=3, funClus="hclust", method="ward")
This function allows to cluster samples according to the results of an ICA decomposition. One clustering is run independently for each component.
clusterSamplesByComp(icaSet, params, funClus = c("Mclust", "kmeans", "pam", "pamk", "hclust", "agnes"), filename, clusterOn = c("A", "S"), level = c("genes", "features"), nbClus, metric = "euclidean", method = "ward", ...)clusterSamplesByComp(icaSet, params, funClus = c("Mclust", "kmeans", "pam", "pamk", "hclust", "agnes"), filename, clusterOn = c("A", "S"), level = c("genes", "features"), nbClus, metric = "euclidean", method = "ward", ...)
icaSet |
An |
params |
A |
funClus |
The function to be used for clustering,
must be one of
|
filename |
A file name to write the results of the clustering in |
clusterOn |
Specifies the matrix used to apply clustering:
|
level |
The level of projections to be used when
|
nbClus |
The number of clusters to be computed,
either a single number or a numeric vector whose length
equals the number of components. If missing (only allowed
if |
metric |
Metric used in |
method |
Method of hierarchical clustering, used in
|
... |
Additional parameters required by the
clustering function |
A list consisting of three elements
a list specifying the sample clustering for each component,
the complete output of the clustering function,
the function used to perform the clustering.
. When clusterOn="S", if some
components were not used because no contributing elements
is selected using the cutoff, the icaSet with the
corresponding component deleted is also returned.
Anne Biton
Mclust, kmeans, pam, pamk,
hclust, agnes, cutree
data(icaSetCarbayo) params <- buildMineICAParams(resPath="carbayo/", selCutoff=4) ## cluster samples according to their contributions # using Mclust without a number of clusters res <- clusterSamplesByComp(icaSet=icaSetCarbayo, params=params, funClus="Mclust", clusterOn="A", filename="clusA") # using kmeans res <- clusterSamplesByComp(icaSet=icaSetCarbayo, params=params, funClus="kmeans", clusterOn="A", nbClus=2, filename="clusA")data(icaSetCarbayo) params <- buildMineICAParams(resPath="carbayo/", selCutoff=4) ## cluster samples according to their contributions # using Mclust without a number of clusters res <- clusterSamplesByComp(icaSet=icaSetCarbayo, params=params, funClus="Mclust", clusterOn="A", filename="clusA") # using kmeans res <- clusterSamplesByComp(icaSet=icaSetCarbayo, params=params, funClus="kmeans", clusterOn="A", nbClus=2, filename="clusA")
This function allows to cluster samples according to the results of an ICA decomposition. Several clustering functions and several levels of data for clustering can be performed by the function.
clusterSamplesByComp_multiple(icaSet, params, funClus = c("Mclust", "kmeans", "pam", "pamk", "hclust", "agnes"), filename, clusterOn = c("A", "S"), level = c("genes", "features"), nbClus, metric = "euclidean", method = "ward", ...)clusterSamplesByComp_multiple(icaSet, params, funClus = c("Mclust", "kmeans", "pam", "pamk", "hclust", "agnes"), filename, clusterOn = c("A", "S"), level = c("genes", "features"), nbClus, metric = "euclidean", method = "ward", ...)
icaSet |
An |
params |
A |
funClus |
The function to be used for clustering,
must be several of
|
filename |
A file name to write the results of the clustering in |
clusterOn |
Specifies the matrix used to apply clustering, can be several of:
|
level |
The level of projections to be used when
|
nbClus |
The number of clusters to be computed,
either a single number or a numeric vector whose length
equals the number of components. If missing (only allowed
if |
metric |
Metric used in |
method |
Method of hierarchical clustering, used in
|
... |
Additional parameters required by the
clustering function |
One clustering is run independently for each component.
A list consisting of three elements
a data.frame specifying the sample clustering for each component using the different ways of clustering,
the complete output of the clustering function(s),
the adjusted Rand indices, used to compare the clusterings obtained for a same component.
Anne
Mclust, adjustedRandIndex, kmeans,
pam, pamk, hclust, agnes,
cutree
data(icaSetCarbayo) params <- buildMineICAParams(resPath="carbayo/", selCutoff=3) ## compare kmeans clustering applied to A and data restricted to the contributing genes ## on components 1 to 3 res <- clusterSamplesByComp_multiple(icaSet=icaSetCarbayo[,,1:3], params=params, funClus="kmeans", nbClus=2, clusterOn=c("A","S"), level="features") head(res$clus)data(icaSetCarbayo) params <- buildMineICAParams(resPath="carbayo/", selCutoff=3) ## compare kmeans clustering applied to A and data restricted to the contributing genes ## on components 1 to 3 res <- clusterSamplesByComp_multiple(icaSet=icaSetCarbayo[,,1:3], params=params, funClus="kmeans", nbClus=2, clusterOn=c("A","S"), level="features") head(res$clus)
From a clustering of samples performed according to their contribution to each component, this function computes the chi-squared test of association between each variable level and the cluster, and summarizes the results in an HTML file.
clusVarAnalysis(icaSet, params, resClus, keepVar, keepComp, funClus = "", adjustBy = c("none", "component", "variable"), method = "BH", doPlot = FALSE, cutoff = params["pvalCutoff"], path = paste(resPath(params), "clus2var/", sep = ""), onlySign = TRUE, typeImage = "png", testBy = c("variable", "level"), filename)clusVarAnalysis(icaSet, params, resClus, keepVar, keepComp, funClus = "", adjustBy = c("none", "component", "variable"), method = "BH", doPlot = FALSE, cutoff = params["pvalCutoff"], path = paste(resPath(params), "clus2var/", sep = ""), onlySign = TRUE, typeImage = "png", testBy = c("variable", "level"), filename)
icaSet |
An object of class
|
params |
An object of class
|
resClus |
A list of numeric vectors indexed by
sample IDs, which specifies the sample clusters. There
must be one clustering by component of |
keepVar |
The variable labels to be considered, i.e
a subset of the variables of icaSet available in
|
keepComp |
A subset of components available in
|
funClus |
The name of the function used to perform the clustering (just for text in written files). |
adjustBy |
The way the p-values of the Wilcoxon and
Kruskal-Wallis tests should be corrected for multiple
testing: |
testBy |
Chi-square tests of association can be
performed either by |
method |
The correction method, see
|
doPlot |
If TRUE, the barplots showing the distribution of the annotation levels among the clusters are plotted and the results are provided in an HTML file 'cluster2annot.htm', else no plot is created. |
cutoff |
The threshold for statistical significance. |
filename |
File name for test results, if
|
path |
A directory _within resPath(params)_ where
the outputs are saved if |
onlySign |
If TRUE (default), only the significant results are plotted. |
typeImage |
The type of image file where each plot is saved. |
When doPlot=TRUE, this function writes an HTML
file containing the results of the tests as a table of
dimension 'variable levels x components' which contains
the p-values of the tests. When a p-value is considered
as significant according to the threshold cutoff,
it is written in bold and filled with a link pointing to
the corresponding barplot displaying the distribution of
the clusters across the levels of the variables.
One image is created by plot and located into the
sub-directory "plots/" of path. Each image is
named by index-of-component_var.png
This function returns a list whose each element gives, for each component, the results of the association chi-squared tests between the clusters and the annotation levels.
Anne Biton
clusterSamplesByComp
## load an example of IcaSet data(icaSetCarbayo) ## build object of class MineICAParams params <- buildMineICAParams(resPath="carbayo/") ## cluster samples according to the columns of the mixing matrix A with kmeans in 2 groups resClus <- clusterSamplesByComp(icaSet=icaSetCarbayo, params=params, funClus="kmeans", clusterOn="A", nbClus=2)$clus ## specify directory for the function outputs (here same directory as the default one) ## this directory will be created by the function in resPath(params) dir <- "clus2var/" ## compute chi-square tests of association, p-value are not adjusted (adjustBy="none"), # test results are written in txt format (doPlot=FALSE and filename not missing) resChi <- clusVarAnalysis(icaSet=icaSetCarbayo, params=params, resClus=resClus, funClus="kmeans", adjustBy="none", doPlot=FALSE, path=dir, filename="clusVarTests") ## Not run: ## compute chi-square tests of association, p-value are not adjusted (adjustBy="none"), # write results and plots in HTML files (doPlot=TRUE) resChi <- clusVarAnalysis(icaSet=icaSetCarbayo, params=params, resClus=resClus, funClus="kmeans", path=dir, adjustBy="none", doPlot=TRUE, filename="clusVarTests") ## compute chi-square tests of association by only considering a subset of components and variables, # adjust p-values by component (adjustBy="component"), # do not write results (doPlot=FALSE and filename is missing). resChi <- clusVarAnalysis(icaSet=icaSetCarbayo, params=params, resClus=resClus, keepComp = 1:10, keepVar=c("GENDER","STAGE"), funClus="kmeans", adjustBy="none", doPlot=FALSE) ## End(Not run)## load an example of IcaSet data(icaSetCarbayo) ## build object of class MineICAParams params <- buildMineICAParams(resPath="carbayo/") ## cluster samples according to the columns of the mixing matrix A with kmeans in 2 groups resClus <- clusterSamplesByComp(icaSet=icaSetCarbayo, params=params, funClus="kmeans", clusterOn="A", nbClus=2)$clus ## specify directory for the function outputs (here same directory as the default one) ## this directory will be created by the function in resPath(params) dir <- "clus2var/" ## compute chi-square tests of association, p-value are not adjusted (adjustBy="none"), # test results are written in txt format (doPlot=FALSE and filename not missing) resChi <- clusVarAnalysis(icaSet=icaSetCarbayo, params=params, resClus=resClus, funClus="kmeans", adjustBy="none", doPlot=FALSE, path=dir, filename="clusVarTests") ## Not run: ## compute chi-square tests of association, p-value are not adjusted (adjustBy="none"), # write results and plots in HTML files (doPlot=TRUE) resChi <- clusVarAnalysis(icaSet=icaSetCarbayo, params=params, resClus=resClus, funClus="kmeans", path=dir, adjustBy="none", doPlot=TRUE, filename="clusVarTests") ## compute chi-square tests of association by only considering a subset of components and variables, # adjust p-values by component (adjustBy="component"), # do not write results (doPlot=FALSE and filename is missing). resChi <- clusVarAnalysis(icaSet=icaSetCarbayo, params=params, resClus=resClus, keepComp = 1:10, keepVar=c("GENDER","STAGE"), funClus="kmeans", adjustBy="none", doPlot=FALSE) ## End(Not run)
Compare IcaSet objects by computing the
correlation between either projection values of common
features or genes, or contributions of common samples.
compareAn(icaSets, labAn, type.corr = c("pearson", "spearman"), cutoff_zval = 0, level = c("samples", "features", "genes"))compareAn(icaSets, labAn, type.corr = c("pearson", "spearman"), cutoff_zval = 0, level = c("samples", "features", "genes"))
icaSets |
list of IcaSet objects, e.g results of ICA decompositions obtained on several datasets. |
labAn |
vector of names for each icaSet, e.g the the names of the datasets on which were calculated the decompositions. |
type.corr |
Type of correlation to compute, either
|
cutoff_zval |
either NULL or 0 (default) if all genes are used to compute the correlation between the components, or a threshold to compute the correlation on the genes that have at least a scaled projection higher than cutoff_zval. Will be used only when correlations are calculated on S or SByGene. |
level |
Data level of the |
The user must carefully choose the object on which the
correlation will be computed. If level='samples',
the correlations are based on the mixing matrices of the
ICA decompositions (of dimension samples x components).
'A' will be typically chosen when the ICA
decompositions were computed on the same dataset, or on
datasets that include the same samples. If
level='features' is chosen, the correlation is
calculated between the source matrices (of dimension
features x components) of the ICA decompositions.
'S' will be typically used when the ICA
decompositions share common features (e.g same
microarrays). If level='genes', the correlations
are calculated on the attributes 'SByGene' which
store the projections of the annotated features.
'SByGene' will be typically chosen when ICA were
computed on datasets from different technologies, for
which comparison is possible only after annotation into a
common ID, like genes.
cutoff_zval is only used when level is one
of c('genes','features'), in order to restrict the
correlation to the contributing features or genes.
When cutoff_zval is specified, for each pair of
components, genes or features that are included in the
circle of center 0 and radius cutoff_zval are
excluded from the computation of the correlation.
It must be taken into account by the user that if
cutoff_zval is different from NULL or
0, the computation will be much slowler since each
pair of component is treated individually.
A list whose length equals the number of pairs of
IcaSet and whose elements are outputs of function
cor2An.
Anne Biton
dat1 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat1) <- paste("g", 1:1000, sep="") colnames(dat1) <- paste("s", 1:10, sep="") dat2 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat2) <- paste("g", 1:1000, sep="") colnames(dat2) <- paste("s", 1:10, sep="") ## run ICA resJade1 <- runICA(X=dat1, nbComp=3, method = "JADE") resJade2 <- runICA(X=dat2, nbComp=3, method = "JADE") ## build params params <- buildMineICAParams(resPath="toy/") ## build IcaSet object icaSettoy1 <- buildIcaSet(params=params, A=data.frame(resJade1$A), S=data.frame(resJade1$S), dat=dat1, alreadyAnnot=TRUE)$icaSet icaSettoy2 <- buildIcaSet(params=params, A=data.frame(resJade2$A), S=data.frame(resJade2$S), dat=dat2, alreadyAnnot=TRUE)$icaSet listPairCor <- compareAn(icaSets=list(icaSettoy1,icaSettoy2), labAn=c("toy1","toy2"), type.corr="pearson", level="genes", cutoff_zval=0) ## Not run: #### Comparison of 2 ICA decompositions obtained on 2 different gene expression datasets. ## load the two datasets library(breastCancerMAINZ) library(breastCancerVDX) data(mainz) data(vdx) ## Define a function used to build two examples of IcaSet objects treat <- function(es, annot="hgu133a.db") { es <- selectFeatures_IQR(es,10000) exprs(es) <- t(apply(exprs(es),1,scale,scale=FALSE)) colnames(exprs(es)) <- sampleNames(es) resJade <- runICA(X=exprs(es), nbComp=10, method = "JADE", maxit=10000) resBuild <- buildIcaSet(params=buildMineICAParams(), A=data.frame(resJade$A), S=data.frame(resJade$S), dat=exprs(es), pData=pData(es), refSamples=character(0), annotation=annot, typeID= typeIDmainz, chipManu = "affymetrix", mart=mart) icaSet <- resBuild$icaSet } ## Build the two IcaSet objects icaSetMainz <- treat(mainz) icaSetVdx <- treat(vdx) ## The pearson correlation is used as a measure of association between the gene projections # on the different components (type.corr="pearson"). listPairCor <- compareAn(icaSets=list(icaSetMainz,icaSetVdx), labAn=c("Mainz","Vdx"), type.corr="pearson", level="genes", cutoff_zval=0) ## Same thing but adding a selection of genes on which the correlation between two components is computed: # when considering pairs of components, only projections whose scaled values are not located within # the circle of radius 1 are used to compute the correlation (cutoff_zval=1). listPairCor <- compareAn(icaSets=list(icaSetMainz,icaSetVdx), labAn=c("Mainz","Vdx"), type.corr="pearson", cutoff_zval=1, level="genes") ## End(Not run)dat1 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat1) <- paste("g", 1:1000, sep="") colnames(dat1) <- paste("s", 1:10, sep="") dat2 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat2) <- paste("g", 1:1000, sep="") colnames(dat2) <- paste("s", 1:10, sep="") ## run ICA resJade1 <- runICA(X=dat1, nbComp=3, method = "JADE") resJade2 <- runICA(X=dat2, nbComp=3, method = "JADE") ## build params params <- buildMineICAParams(resPath="toy/") ## build IcaSet object icaSettoy1 <- buildIcaSet(params=params, A=data.frame(resJade1$A), S=data.frame(resJade1$S), dat=dat1, alreadyAnnot=TRUE)$icaSet icaSettoy2 <- buildIcaSet(params=params, A=data.frame(resJade2$A), S=data.frame(resJade2$S), dat=dat2, alreadyAnnot=TRUE)$icaSet listPairCor <- compareAn(icaSets=list(icaSettoy1,icaSettoy2), labAn=c("toy1","toy2"), type.corr="pearson", level="genes", cutoff_zval=0) ## Not run: #### Comparison of 2 ICA decompositions obtained on 2 different gene expression datasets. ## load the two datasets library(breastCancerMAINZ) library(breastCancerVDX) data(mainz) data(vdx) ## Define a function used to build two examples of IcaSet objects treat <- function(es, annot="hgu133a.db") { es <- selectFeatures_IQR(es,10000) exprs(es) <- t(apply(exprs(es),1,scale,scale=FALSE)) colnames(exprs(es)) <- sampleNames(es) resJade <- runICA(X=exprs(es), nbComp=10, method = "JADE", maxit=10000) resBuild <- buildIcaSet(params=buildMineICAParams(), A=data.frame(resJade$A), S=data.frame(resJade$S), dat=exprs(es), pData=pData(es), refSamples=character(0), annotation=annot, typeID= typeIDmainz, chipManu = "affymetrix", mart=mart) icaSet <- resBuild$icaSet } ## Build the two IcaSet objects icaSetMainz <- treat(mainz) icaSetVdx <- treat(vdx) ## The pearson correlation is used as a measure of association between the gene projections # on the different components (type.corr="pearson"). listPairCor <- compareAn(icaSets=list(icaSetMainz,icaSetVdx), labAn=c("Mainz","Vdx"), type.corr="pearson", level="genes", cutoff_zval=0) ## Same thing but adding a selection of genes on which the correlation between two components is computed: # when considering pairs of components, only projections whose scaled values are not located within # the circle of radius 1 are used to compute the correlation (cutoff_zval=1). listPairCor <- compareAn(icaSets=list(icaSetMainz,icaSetVdx), labAn=c("Mainz","Vdx"), type.corr="pearson", cutoff_zval=1, level="genes") ## End(Not run)
This function builds a correlation graph from the outputs
of function compareAn.
compareAn2graphfile(listPairCor, useMax = TRUE, cutoff = NULL, useVal = c("cor", "pval"), file = NULL)compareAn2graphfile(listPairCor, useMax = TRUE, cutoff = NULL, useVal = c("cor", "pval"), file = NULL)
listPairCor |
The output of the function
|
useMax |
If TRUE, the graph is restricted to edges that correspond to maximum score, see details |
cutoff |
Cutoff used to select pairs that will be included in the graph. |
useVal |
The value on which is based the graph,
either |
file |
File name. |
When correlations are considered (useVal="cor"),
absolute values are used since the components have no
direction.
If useMax is TRUE each component is linked
to the most correlated component of each different
IcaSet.
If cutoff is specified, only correlations
exceeding this value are taken into account during the
graph construction. For example, if cutoff is 1,
only relationships between components that correspond to
a correlation value larger than 1 will be included.
When useVal="pval" and useMax=TRUE, the
minimum value is taken instead of the maximum.
A data.frame with the graph description, has two columns
n1 and n2 filled with node IDs, each row
denotes that there is an edge from n1 to
n2. Additional columns quantify the strength of
association: correlation (cor), p-value
(pval), (1-abs(cor)) (distcor),
log10-pvalue (logpval).
Anne Biton
dat1 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat1) <- paste("g", 1:1000, sep="") colnames(dat1) <- paste("s", 1:10, sep="") dat2 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat2) <- paste("g", 1:1000, sep="") colnames(dat2) <- paste("s", 1:10, sep="") ## run ICA resJade1 <- runICA(X=dat1, nbComp=3, method = "JADE") resJade2 <- runICA(X=dat2, nbComp=3, method = "JADE") ## build params params <- buildMineICAParams(resPath="toy/") ## build IcaSet object icaSettoy1 <- buildIcaSet(params=params, A=data.frame(resJade1$A), S=data.frame(resJade1$S), dat=dat1, alreadyAnnot=TRUE)$icaSet icaSettoy2 <- buildIcaSet(params=params, A=data.frame(resJade2$A), S=data.frame(resJade2$S), dat=dat2, alreadyAnnot=TRUE)$icaSet resCompareAn <- compareAn(icaSets=list(icaSettoy1,icaSettoy2), labAn=c("toy1","toy2"), type.corr="pearson", level="genes", cutoff_zval=0) ## Build a graph where edges correspond to maximal correlation value (useVal="cor"), compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, useVal="cor", file="myGraph.txt") ## Not run: #### Comparison of 2 ICA decompositions obtained on 2 different gene expression datasets. ## load the two datasets library(breastCancerMAINZ) library(breastCancerVDX) data(mainz) data(vdx) ## Define a function used to build two examples of IcaSet objects treat <- function(es, annot="hgu133a.db") { es <- selectFeatures_IQR(es,10000) exprs(es) <- t(apply(exprs(es),1,scale,scale=FALSE)) colnames(exprs(es)) <- sampleNames(es) resJade <- runICA(X=exprs(es), nbComp=10, method = "JADE", maxit=10000) resBuild <- buildIcaSet(params=buildMineICAParams(), A=data.frame(resJade$A), S=data.frame(resJade$S), dat=exprs(es), pData=pData(es), refSamples=character(0), annotation=annot, typeID= typeIDmainz, chipManu = "affymetrix", mart=mart) icaSet <- resBuild$icaSet } ## Build the two IcaSet objects icaSetMainz <- treat(mainz) icaSetVdx <- treat(vdx) ## Compute correlation between every pair of IcaSet objects. resCompareAn <- compareAn(icaSets=list(icaSetMainz,icaSetVdx), labAn=c("Mainz","Vdx"), type.corr="pearson", level="genes", cutoff_zval=0) ## Same thing but adding a selection of genes on which the correlation between two components is computed: # when considering pairs of components, only projections whose scaled values are not located within # the circle of radius 1 are used to compute the correlation (cutoff_zval=1). resCompareAn <- compareAn(icaSets=list(icaSetMainz,icaSetVdx), labAn=c("Mainz","Vdx"), type.corr="pearson", cutoff_zval=1, level="genes") ## Build a graph where edges correspond to maximal correlation value (useVal="cor"), ## i.e, component A of analysis i is linked to component B of analysis j, ## only if component B is the most correlated component to A amongst all component of analysis j. compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, useVal="cor", file="myGraph.txt") ## Restrict the graph to correlation values exceeding 0.4 compareAn2graphfile(listPairCor=resCompareAn, useMax=FALSE, cutoff=0.4, useVal="cor", file="myGraph.txt") ## End(Not run)dat1 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat1) <- paste("g", 1:1000, sep="") colnames(dat1) <- paste("s", 1:10, sep="") dat2 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat2) <- paste("g", 1:1000, sep="") colnames(dat2) <- paste("s", 1:10, sep="") ## run ICA resJade1 <- runICA(X=dat1, nbComp=3, method = "JADE") resJade2 <- runICA(X=dat2, nbComp=3, method = "JADE") ## build params params <- buildMineICAParams(resPath="toy/") ## build IcaSet object icaSettoy1 <- buildIcaSet(params=params, A=data.frame(resJade1$A), S=data.frame(resJade1$S), dat=dat1, alreadyAnnot=TRUE)$icaSet icaSettoy2 <- buildIcaSet(params=params, A=data.frame(resJade2$A), S=data.frame(resJade2$S), dat=dat2, alreadyAnnot=TRUE)$icaSet resCompareAn <- compareAn(icaSets=list(icaSettoy1,icaSettoy2), labAn=c("toy1","toy2"), type.corr="pearson", level="genes", cutoff_zval=0) ## Build a graph where edges correspond to maximal correlation value (useVal="cor"), compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, useVal="cor", file="myGraph.txt") ## Not run: #### Comparison of 2 ICA decompositions obtained on 2 different gene expression datasets. ## load the two datasets library(breastCancerMAINZ) library(breastCancerVDX) data(mainz) data(vdx) ## Define a function used to build two examples of IcaSet objects treat <- function(es, annot="hgu133a.db") { es <- selectFeatures_IQR(es,10000) exprs(es) <- t(apply(exprs(es),1,scale,scale=FALSE)) colnames(exprs(es)) <- sampleNames(es) resJade <- runICA(X=exprs(es), nbComp=10, method = "JADE", maxit=10000) resBuild <- buildIcaSet(params=buildMineICAParams(), A=data.frame(resJade$A), S=data.frame(resJade$S), dat=exprs(es), pData=pData(es), refSamples=character(0), annotation=annot, typeID= typeIDmainz, chipManu = "affymetrix", mart=mart) icaSet <- resBuild$icaSet } ## Build the two IcaSet objects icaSetMainz <- treat(mainz) icaSetVdx <- treat(vdx) ## Compute correlation between every pair of IcaSet objects. resCompareAn <- compareAn(icaSets=list(icaSetMainz,icaSetVdx), labAn=c("Mainz","Vdx"), type.corr="pearson", level="genes", cutoff_zval=0) ## Same thing but adding a selection of genes on which the correlation between two components is computed: # when considering pairs of components, only projections whose scaled values are not located within # the circle of radius 1 are used to compute the correlation (cutoff_zval=1). resCompareAn <- compareAn(icaSets=list(icaSetMainz,icaSetVdx), labAn=c("Mainz","Vdx"), type.corr="pearson", cutoff_zval=1, level="genes") ## Build a graph where edges correspond to maximal correlation value (useVal="cor"), ## i.e, component A of analysis i is linked to component B of analysis j, ## only if component B is the most correlated component to A amongst all component of analysis j. compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, useVal="cor", file="myGraph.txt") ## Restrict the graph to correlation values exceeding 0.4 compareAn2graphfile(listPairCor=resCompareAn, useMax=FALSE, cutoff=0.4, useVal="cor", file="myGraph.txt") ## End(Not run)
Compute and annotate the intersection or union between contributiong genes of components originating from different IcaSet objects.
compareGenes(keepCompByIcaSet, icaSets, lab, cutoff = 0, type = c("union", "intersection"), annotate = TRUE, file, mart = useMart("ensembl", "hsapiens_gene_ensembl"))compareGenes(keepCompByIcaSet, icaSets, lab, cutoff = 0, type = c("union", "intersection"), annotate = TRUE, file, mart = useMart("ensembl", "hsapiens_gene_ensembl"))
icaSets |
List of |
keepCompByIcaSet |
Indices of the components to be
considered in each |
lab |
The names of the icaSets (e.g the names of the datasets they originate from). |
cutoff |
The cutoff (on the absolute centered and scaled projections) above which the genes have to be considered. |
type |
|
annotate |
If TRUE (default) the genes are annotated
using function |
file |
The HTML file name where the genes and their
annotations are written, default is
|
mart |
The mart object (database and dataset) used
for annotation, see function |
A data.frame containing
typeID(icaSets[[1]])['geneID_biomart']:the gene IDs,
the median of the ranks of
each gene across the IcaSet objects,
the labels of the IcaSet objects
in which each gene is above the given cutoff
the minimum of the ranks of each gene
across the IcaSet objects,
the ranks
of each gene in each IcaSet where it is
available,
the centered and reduced
projection of each gene in each IcaSet where it is
available.
Anne Biton
## Not run: data(icaSetCarbayo) mart <- useMart("ensembl", "hsapiens_gene_ensembl") ## comparison of two components ## here the components come from the same IcaSet for convenience ## but they must come from different IcaSet in practice. compareGenes(keepCompByIcaSet = c(9,4), icaSets = list(icaSetCarbayo, icaSetCarbayo), lab=c("Carbayo", "Carbayo2"), cutoff=3, type="union", mart=mart) ## End(Not run)## Not run: data(icaSetCarbayo) mart <- useMart("ensembl", "hsapiens_gene_ensembl") ## comparison of two components ## here the components come from the same IcaSet for convenience ## but they must come from different IcaSet in practice. compareGenes(keepCompByIcaSet = c(9,4), icaSets = list(icaSetCarbayo, icaSetCarbayo), lab=c("Carbayo", "Carbayo2"), cutoff=3, type="union", mart=mart) ## End(Not run)
This function measures the correlation between two matrices containing the results of two decompositions.
cor2An(mat1, mat2, lab, type.corr = c("pearson", "spearman"), cutoff_zval = 0)cor2An(mat1, mat2, lab, type.corr = c("pearson", "spearman"), cutoff_zval = 0)
mat1 |
matrix of dimension features/genes x number of components, e.g the results of an ICA decomposition |
mat2 |
matrix of dimension features/genes x number of components, e.g the results of an ICA decomposition |
lab |
The vector of labels for mat1 and mat2, e.g the the names of the two datasets on which were calculated the two decompositions |
type.corr |
Type of correlation, either
|
cutoff_zval |
cutoff_zval: 0 (default) if all genes are used to compute the correlation between the components, or a threshold to compute the correlation on the genes that have at least a scaled projection higher than cutoff_zval. |
Before computing the correlations, the components are scaled and restricted to common row names.
It must be taken into account by the user that if
cutoff_zval is different from NULL or zero, the
computation will be slowler since each pair of component
is treated individually.
When cutoff_zval is specified, for each pair of
components, genes that are included in the circle of
center 0 and radius cutoff_zval are excluded from
the computation of the correlation between the gene
projection of the two components.
This function returns a list consisting of:
cor |
a matrix of dimensions '(nbcomp1+nbcomp2) x (nbcomp1*nbcomp2)', containing the correlation values between each pair of components, |
pval |
a matrix of dimension '(nbcomp1+nbcomp2) x (nbcomp1*nbcomp2)', containing the p-value of the correlation tests for each pair of components, |
inter |
the intersection
between the features/genes of |
labAn |
the labels of the compared matrices. |
Anne Biton
rcorr, cor.test, compareAn
cor2An(mat1=matrix(rnorm(10000),nrow=1000,ncol=10), mat2=matrix(rnorm(10000),nrow=1000,ncol=10), lab=c("An1","An2"), type.corr="pearson")cor2An(mat1=matrix(rnorm(10000),nrow=1000,ncol=10), mat2=matrix(rnorm(10000),nrow=1000,ncol=10), lab=c("An1","An2"), type.corr="pearson")
This function computes the correlation between two components.
correl2Comp(comp1, comp2, type.corr = "pearson", plot = FALSE, cutoff_zval = 0, test = FALSE, alreadyTreat = FALSE)correl2Comp(comp1, comp2, type.corr = "pearson", plot = FALSE, cutoff_zval = 0, test = FALSE, alreadyTreat = FALSE)
comp1 |
The first component, a vector of projections or contributions indexed by labels |
comp2 |
The second component, a vector of projections or contributions indexed by labels |
type.corr |
Type of correlation to be computed, either |
plot |
if |
cutoff_zval |
either NULL or 0 (default) if all genes are used to compute the correlation between the components, or a threshold to compute the correlation on the genes that have at least a scaled projection higher than cutoff_zval. |
test |
if TRUE the correlation test p-value is returned instead of the correlation value |
alreadyTreat |
if TRUE comp1 and comp2 are considered as being already treated (i.e scaled and restricted to common elements) |
Before computing the correlation, the components are scaled and restricted to common labels.
When cutoff_zval is different from 0, the elements that are included in the circle of center 0 and radius cutoff_zval are not taken into account during the computation of the correlation.
This function returns either the correlation value or the p-value of the correlation test.
Anne Biton
These generic functions access and set the attributes dat stored in an object of class IcaSet.
dat(object) dat(object) <- value datByGene(object) datByGene(object) <- value geneNames(object)dat(object) dat(object) <- value datByGene(object) datByGene(object) <- value geneNames(object)
object |
object of class |
value |
Matrix with rows representing features or genes and columns samples. |
dat and datByGene return a matrix containing measured values (e.g
expression data) indexed by features and genes, respectively.
geneNames returns the names of the genes, i.e the row names of
datByGene.
Anne
Contains bladder cancer expression data based on on HG-U133A Affymetrix microarrays. The data include 93 samples, were normalized with MAS5 by the authors of the paper using Quantile normalization and log2-transformation. They are restricted to the 10000 most variable probe sets.
Anne Biton
http://jco.ascopubs.org/content/24/5/778/suppl/DC1
IcaSet object.This generic function retrieves, from an IcaSet object,
the feature projections (contained in attribute S) and
sample contributions (contained in attribute A)
corresponding to a specific component.
getComp(object, level, ind)getComp(object, level, ind)
object |
Object of class |
level |
Either "features" to retrieve projections contained in
|
ind |
The index of the component to be retrieved. |
getComp returns a list containing two elements:
the feature or gene projections on the given component,
the sample contributions on the given component.
Anne Biton
class-IcaSet
Extract projection values of a given set of IDs on a subset of components.
getProj(icaSet, ids, keepComp, level = c("features", "genes"))getProj(icaSet, ids, keepComp, level = c("features", "genes"))
icaSet |
An object of class |
ids |
feature or gene IDs |
keepComp |
Index of the components to be conserved,
must be in |
level |
The level of projections to be extracted,
either |
A vector or a list of projection values
Anne Biton
## load an example of IcaSet data(icaSetCarbayo) ##get the projection of your favorite proliferation genes #on all components getProj(icaSetCarbayo, ids=c("TOP2A","CDK1","CDC20"), level="genes") #on some components getProj(icaSetCarbayo, ids=c("TOP2A","CDK1","CDC20"), keepComp=c(1,6,9,12),level="genes") ##get the gene projection values on the sixth component getProj(icaSetCarbayo, keepComp=6,level="genes")## load an example of IcaSet data(icaSetCarbayo) ##get the projection of your favorite proliferation genes #on all components getProj(icaSetCarbayo, ids=c("TOP2A","CDK1","CDC20"), level="genes") #on some components getProj(icaSetCarbayo, ids=c("TOP2A","CDK1","CDC20"), keepComp=c(1,6,9,12),level="genes") ##get the gene projection values on the sixth component getProj(icaSetCarbayo, keepComp=6,level="genes")
GOstats.Runs an enrichment analysis of the contributing genes
associated with each component, using the function
hyperGTest of package
GOstats. The easiest way to run
enrichment analysis is to use function
runEnrich.
hypergeoAn(icaSet, params, path = paste(resPath(params), "GOstatsEnrichAnalysis/", sep = "/"), SlistSel, hgCutoff = 0.01, db = "go", onto = "BP", cond = TRUE, universe, entrez2symbol)hypergeoAn(icaSet, params, path = paste(resPath(params), "GOstatsEnrichAnalysis/", sep = "/"), SlistSel, hgCutoff = 0.01, db = "go", onto = "BP", cond = TRUE, universe, entrez2symbol)
icaSet |
An object of class |
params |
An object of class
|
path |
The path where results will be saved |
SlistSel |
A list of contributing gene projection values per component. Each element of the list corresponds to a component and is restricted to the features or genes exceeding a given threshold. If missing, is computed by the function. |
hgCutoff |
The p-value threshold |
db |
The database to be used ( |
onto |
A character specifying the GO ontology to
use. Must be one of |
cond |
A logical indicating whether the calculation
should conditioned on the GO structure, see
|
universe |
The universe for the hypergeometric
tests, see
|
entrez2symbol |
A vector of all gene Symbols
involved in the analysis indexed by their Entrez Gene
IDs. It is only used when |
An annotation package must be available in
annotation(icaSet) to provide the contents of the
gene sets. If none corresponds to the technology you deal
with, please choose the org.*.eg.db package according to
the organism (for example org.Hs.eg.db for Homo sapiens).
Save results of the enrichment tests in a '.rda' file
located in
path/db/onto/zvalCutoff(params).
Anne Biton
runEnrich, xtable,
useMart,
hyperGTest,
GOHyperGParams,
mergeGostatsResults
## Not run: ## load an example of IcaSet data(icaSetCarbayo) ## define params # Use threshold 3 to select contributing genes. # Results of enrichment analysis will be written in path 'resPath(params)/GOstatsEnrichAnalysis' params <- buildMineICAParams(resPath="~/resMineICACarbayo/", selCutoff=3) ## Annotation package for IcaSetCarbayo is hgu133a.db. # check annotation package annotation(icaSetCarbayo) ## Define universe, i.e the set of EntrezGene IDs mapping to the feature IDs of the IcaSet object. universe <- as.character(na.omit(unique(unlist(AnnotationDbi::mget(featureNames(icaSetCarbayo), hgu133aENTREZID, ifnotfound = NA))))) ## Apply enrichement analysis (of the contributing genes) to the first components using gene sets from KEGG. # Since an annotation package is available, we don't need to fill arg 'entrez2symbol'. # run the actual enrichment analysis hypergeoAn(icaSet=icaSetCarbayo[,,1], params=params, db="GO",onto="BP", universe=universe) ## End(Not run)## Not run: ## load an example of IcaSet data(icaSetCarbayo) ## define params # Use threshold 3 to select contributing genes. # Results of enrichment analysis will be written in path 'resPath(params)/GOstatsEnrichAnalysis' params <- buildMineICAParams(resPath="~/resMineICACarbayo/", selCutoff=3) ## Annotation package for IcaSetCarbayo is hgu133a.db. # check annotation package annotation(icaSetCarbayo) ## Define universe, i.e the set of EntrezGene IDs mapping to the feature IDs of the IcaSet object. universe <- as.character(na.omit(unique(unlist(AnnotationDbi::mget(featureNames(icaSetCarbayo), hgu133aENTREZID, ifnotfound = NA))))) ## Apply enrichement analysis (of the contributing genes) to the first components using gene sets from KEGG. # Since an annotation package is available, we don't need to fill arg 'entrez2symbol'. # run the actual enrichment analysis hypergeoAn(icaSet=icaSetCarbayo[,,1], params=params, db="GO",onto="BP", universe=universe) ## End(Not run)
Container for high-throughput data and results of ICA decomposition
obtained on these data. IcaSet class is derived from
eSet, and requires a matrix named dat as
assayData member.
Directly extends class eSet.
new("IcaSet")
new("IcaSet",
annotation = character(0),
experimentData = new("MIAME"),
featureData = new("AnnotatedDataFrame"),
phenoData = new("AnnotatedDataFrame"),
protocolData = phenoData[,integer(0)],
dat = new("matrix"),
A=new("data.frame"),
S=new("data.frame"), ...)
This creates an IcaSet with assayData implicitly
created to contain dat.
new("IcaSet",
annotation = character(0),
assayData = assayDataNew(dat=new("matrix")),
experimentData = new("MIAME"),
featureData = new("AnnotatedDataFrame"),
phenoData = new("AnnotatedDataFrame"),
protocolData = phenoData[,integer(0)],
A=new("data.frame"),
S=new("data.frame"), ...)
This creates an IcaSet with assayData provided
explicitly.
IcaSet instances are usually created through
new("IcaSet", ...). Usually the arguments to new
include dat ('features x samples', e.g a matrix of expression
data), phenoData ('samples x annotations', a
matrix of sample annotations), S the Source
matrix of the ICA decomposition ('features x comp'), A the Mixing matrix of the ICA
decomposition ('samples x comp'), annotation the annotation
package, typeID the description of the feature and gene IDs.
The other attributes can be missing, in which case they are assigned default values.
The function buildIcaSet is a more convenient way to
create IcaSet instances, and allows to automatically annotate
the features.
Inherited from eSet:
annotation:See eSet
assayData:Contains matrices with equal
dimensions, and with column number equal to
nrow(phenoData). assayData must contain a matrix
dat with rows representing features (e.g., reporters)
and columns representing samples. Class:AssayData-class
experimentData:See eSet
featureData:See eSet
phenoData:See eSet
protocolData:See eSet
Specific slot:
organism:Contains the name of the species. Currently
only Human ("Human" or "Homo sapiens") and Mouse ("Mouse" or "Mus
musculus") are supported. Only used when chipManu="illumina"
mart:An output of useMart of package biomaRt. Only useful if no annotation package is available for argument icaSet.
datByGene:Data.frame containing the data dat where
features have been replaced by their annotations (e.g, gene IDs). Rows
represent annotations of the features (e.g., gene IDs) and
columns represent samples.
A:The mixing matrix of the ICA decomposition, contained
in a data.frame whose
column number equals the number of components and row number equals
nrow(phenoData) (dimension: 'samples x comp').
S:The source matrix of the ICA decomposition, contained
in a data.frame whose
column number equals the number of components and row number equals
nrow(assayData) (dimension: 'features x comp').
SByGene:The matrix Source of the ICA decomposition, contained
in a data.frame whose
column number equals the number of components and row number equals
nrow(datByGene) (dimension: 'annotatedFeatures x comp').
compNames:A vector of component labels with length equal to the number of component.
indComp:A vector of component indices with length equal to the number of component.
witGenes:A vector of gene IDs with length equal to the number of component.
chipManu:The manufacturer of the technology the data originates from. Useful for the annotation of the features when data originates from an _illumina_ microarray.
chipVersion:The version of the chip, only useful for
when chipManu="illumina"
refSamples:A vector of sample IDs including the reference samples, e.g the "normal" samples.
Must be included in sampleNames(object), i.e in colnames(dat).
typeID:A vector of characters providing the annotation IDs. It includes three elements:
the IDs from the
package to be used to annotate the features into genes. It will be used to
fill the attributes datByGene and SByGene of the icaSet.
It must match one of the objects the corresponding package supports
(you can access the list of objects by typing ls("package:packagename")). If
no annotation package is provided, this element is not useful.
the type of gene IDs, as available in
listFilters(mart); where mart is specified as described in useMart.
If you have directly built the IcaSet at the
gene level (i.e if no annotation package is used), featureID_biomart and
geneID_biomart will be identical.
the
type of feature IDs, as available in listFilters(mart); where
mart is specified as described in function useMart.
Not useful if you work at the gene level.
Class-specific methods.
getComp(IcaSet, ind,
level=c("features","genes"))Given a component index, extract
the corresponding sample contribution values from A, and the
feature (level="features") or gene (level="genes")
projections from S. Returns a list with two elements:
contrib the sample contributions and proj the
feature or gene projections.
Access and set any slot specific to IcaSet:
slotName(IcaSet), and
slotName(IcaSet)<-:Accessing and setting any slot
of name slotName contained in an IcaSet object.
IcaSet["slotName"], and
IcaSet["slotName"]<-:Accessing and setting any slot
of name slotName contained in an IcaSet object.
Most used accessors and settors:
A(IcaSet), and
A(IcaSet)<-:Accessing and setting Mixing matrix A.
S(IcaSet), and
S(IcaSet)<-:Accessing and setting
the data.frame Source S.
Slist(IcaSet):Accessing the data.frame Source as a list where names are preserved.
SByGene(IcaSet), and
SByGene(IcaSet)<-:Accessing
and setting the _annotated_ data.frame Source SByGene.
SlistByGene(IcaSet):Accessing the _annotated_ Source matrix as a list where names are preserved.
organism(IcaSet), organism(IcaSet,characte)<-
Access and
set value in the organism slot.
dat(IcaSet), dat(IcaSet,matrix)<-
Access and
set elements named dat in the AssayData-class
slot.
Derived from eSet:
pData(IcaSet), pData(IcaSet,value)<-:See eSet
assayData(IcaSet):See eSet
sampleNames(IcaSet) and sampleNames(IcaSet)<-:See eSet
featureNames(IcaSet), featureNames(IcaSet, value)<-:See eSet
dims(IcaSet):See eSet
phenoData(IcaSet), phenoData(IcaSet,value)<-:See eSet
varLabels(IcaSet), varLabels(IcaSet, value)<-:See eSet
varMetadata(IcaSet), varMetadata(IcaSet,value)<-:See eSet
varMetadata(IcaSet), varMetadata(IcaSet,value)
See eSet
experimentData(IcaSet),experimentData(IcaSet,value)<-:See eSet
pubMedIds(IcaSet), pubMedIds(IcaSet,value)
See eSet
abstract(IcaSet):See eSet
annotation(IcaSet), annotation(IcaSet,value)<-
See eSet
protocolData(IcaSet), protocolData(IcaSet,value)<-
See eSet
combine(IcaSet,IcaSet):See eSet
storageMode(IcaSet), storageMode(IcaSet,character)<-:See eSet
Standard generic methods:
initialize(IcaSet):Object instantiation, used
by new; not to be called directly by the user.
validObject(IcaSet):Validity-checking method, ensuring
that dat is a member of
assayData, and that the number of features, genes, samples,
and components are consistent across all the attributes of the
IcaSet object. checkValidity(IcaSet) imposes this
validity check, and the validity checks of eSet.
IcaSet[slotName], IcaSet[slotName]<-:Accessing
and setting any slot of name slotName contained in an
IcaSet object.
IcaSet[i, j, k]:Extract object of class "IcaSet" for features or genes with names i, samples with names or indices j, and components with names or indices k.
makeDataPackage(object, author, email, packageName, packageVersion, license, biocViews, filePath, description=paste(abstract(object), collapse="\n\n"), ...)Create a data package based on an IcaSet object. See
makeDataPackage.
show(IcaSet):See eSet
dim(IcaSet), ncol:See eSet
IcaSet[(index)]:See eSet
IcaSet$, IcaSet$<-:See eSet
IcaSet[[i]], IcaSet[[i]]<-:See eSet
Anne Biton
eSet-class, buildIcaSet,
class-IcaSet, class-MineICAParams.
# create an instance of IcaSet new("IcaSet") dat <- matrix(runif(100000), nrow=1000, ncol=100) rownames(dat) <- 1:nrow(dat) new("IcaSet", dat=dat, A=as.data.frame(matrix(runif(1000), nrow=100, ncol=10)), S=as.data.frame(matrix(runif(10000), nrow=1000, ncol=10), row.names = 1:nrow(dat)))# create an instance of IcaSet new("IcaSet") dat <- matrix(runif(100000), nrow=1000, ncol=100) rownames(dat) <- 1:nrow(dat) new("IcaSet", dat=dat, A=as.data.frame(matrix(runif(1000), nrow=100, ncol=10)), S=as.data.frame(matrix(runif(10000), nrow=1000, ncol=10), row.names = 1:nrow(dat)))
Object of class IcaSet containing an ICA
decomposition calculated by the FastICA algorithm
(through matlab function "icasso") on bladder cancer
expression data measured on HG-U133A Affymetrix
microarrays. The original expression data were normalized
with MAS5 by the authors of the paper followed by
log2-transformation. ICA was run on the dataset
restricted to the 10000 most variable probe sets (based
on IQR values). 10 components were computed. Only probe
sets/genes having an absolute projection higher than 3
are stored in this object.
Anne Biton
http://jco.ascopubs.org/content/24/5/778/suppl/DC1
Object of class IcaSet containing an ICA
decomposition calculated by the FastICA algorithm
(through matlab function "icasso") on bladder cancer
expression data measured on illumina Human-6 BeadChip,
version 2. It contains 20 independent components. The
original expression data contain 165 tumor samples, were
normalized by the authors of the paper with Illumina
BeadStudio software using Quantile normalization and log2
transformation, and are restricted to the 10000 most
variable probe sets.
Anne
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13507
Object of class IcaSet containing an ICA
decomposition calculated by the FastICA algorithm
(through matlab function "icasso") on gene expression
data of urothelial tumors. measured on a HG-U133-plus2
Affymetrix microarrays. It contains 20 independent
components. The original expression data contain 93 tumor
samples, were normalized with GCRMA with
log2-transformation, and are restricted to the 10000 most
variable probe sets.
Anne Biton
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31684
Object of class IcaSet containing an ICA
decomposition calculated by the FastICA algorithm
(through matlab function "icasso") on bladder cancer
expression data measured on HG-U133-95a and HG-U133-95av2
Affymetrix microarrays. It contains 20 independent
components. The original expression data contain 63 tumor
samples and were normalized by RMA with
log2-transformation.
Anne Biton
http://microarrays.curie.fr/publications/oncologie_moleculaire/bladder_TCM/
These generic functions access and set the attributes compNames, indComp and witGenes
stored in an object of class IcaSet.
indComp(object) indComp(object) <- value compNames(object) compNames(object) <- value witGenes(object) witGenes(object) <- valueindComp(object) indComp(object) <- value compNames(object) compNames(object) <- value witGenes(object) witGenes(object) <- value
object |
object of class |
value |
Numeric vector for |
indComp returns a numeric vector containing component indices;
compNames returns a character vector containing component labels;
witGenes returns a character vector containing witness genes IDs.
Anne Biton
Container for parameters used during the analysis of an ICA decomposition obtained on genomics data.
new("MineICAParams")
new("MineICAParams",
resPath="",
genesPath="ProjByComp",
pvalCutoff=0.05,
selCutoff=3)
SfileA txt file containing the Source matrix S.
AfileA txt file containing the Mixing matrix A.
datfileA txt file containing the data (typically expression data) on which the decomposition was calculated.
annotfileEither a RData or txt file containing the annotation data for the samples (must be of dimensions samples*annotations).
resPathThe path where the outputs of the analysis will be written.
genesPathThe path _within_ the resPath where the gene projections will be written. If missing, will be automatically attributed as resPath/gene2components/.
annot2col A vector of colors indexed by annotation levels. If missing, will be automatically attributed using function annot2Color.
pvalCutoffThe cutoff used to consider a p-value significant, default is 0.05.
selCutoffThe cutoff applied on the absolute feature/gene projection values to consider gene as contributing to a component, default is 3. Must be either of length 1 and the same treshold is applied to all components, or of length equal to the number of components in order to use a specific threshold for each component.
For any slot:
slotName(MineICAParams) and
slotName(MineICAParams)<-:Accessing and setting any slot
of name slotName contained in an MineICAParams object.
MineICAParams["slotName"] and
MineICAParams["slotName"]<-:Accessing and setting any
slot of name slotName contained in an MineICAParams object.
Anne Biton
# create an instance of LocSet new("MineICAParams")# create an instance of LocSet new("MineICAParams")
For each feature/gene, this function returns the indices of the components they contribute to.
nbOccByGeneInComp(Slist, cutoff, sel)nbOccByGeneInComp(Slist, cutoff, sel)
Slist |
A list whose each element contains projection values of features/genes on a component. |
cutoff |
A threshold to be used to define a gene as contributor |
sel |
A list whose each element contains projection values of
contributing features/genes on a component (the difference with arg
|
This function returns a list which gives for each feature/gene the indices of the components it contributes to.
Anne Biton
c1 <- rnorm(100); names(c1) <- paste("g",100:199,sep="") c2 <- rnorm(100); names(c2) <- paste("g",1:99,sep="") MineICA:::nbOccByGeneInComp(Slist=list(c1,c2), cutoff= 0.5)c1 <- rnorm(100); names(c1) <- paste("g",100:199,sep="") c2 <- rnorm(100); names(c2) <- paste("g",1:99,sep="") MineICA:::nbOccByGeneInComp(Slist=list(c1,c2), cutoff= 0.5)
For each feature/gene, this function returns the components they contribute to and their projection values across all the components.
nbOccInComp(icaSet, params, selectionByComp = NULL, level = c("features", "genes"), file = NULL)nbOccInComp(icaSet, params, selectionByComp = NULL, level = c("features", "genes"), file = NULL)
icaSet |
An object of class |
params |
An object of class
|
selectionByComp |
The list of components already restricted to the contributing genes |
level |
The attribute of |
file |
The file where the output data.frame and plots are written. |
A feature/gene is considered as a contributor when its
scaled projection value exceeds the threshold
selCutoff(icaSet).
This function plots the number of times the feature/gene is a contributor as a function of the standard deviation of its expression profile.
The created files are located in genePath(params).
An extensiom '.htm' and '.pdf' is respectively added to
the file name for the data.frame and the plot
outputs.
Returns a data.frame whose columns are: 'gene' the feature or gene ID, 'nbOcc' the number of components on which the gene contributes according to the threshold, 'components' the indices of these components, and then the component indices which contain its projection values.
Anne Biton
data(icaSetCarbayo) params <- buildMineICAParams(resPath="carbayo/") nbOcc <- nbOccInComp(icaSet=icaSetCarbayo, params=params, level="genes", file="gene2MixingMatrix")data(icaSetCarbayo) params <- buildMineICAParams(resPath="carbayo/") nbOcc <- nbOccInComp(icaSet=icaSetCarbayo, params=params, level="genes", file="gene2MixingMatrix")
This function builds a data.frame describing for each node of the graph its ID and which analysis/data it comes from.
nodeAttrs(nbAn, nbComp, labAn, labComp, file)nodeAttrs(nbAn, nbComp, labAn, labComp, file)
nbAn |
Number of analyses being considered, i.e number of IcaSet objects |
nbComp |
Number of components by analysis, if of length 1 then it is assumed that each analysis has the same number of components. |
labAn |
Labels of the analysis, if missing it will be generated as an1, an2, ... |
labComp |
List containing the component labels indexed by analysis, if missing will be generated as comp1, comp2, ... |
file |
File where the description of the node attributes will be written |
The created file is used in Cytoscape.
A data.frame describing each node/component
Anne Biton
## 4 datasets, 20 components calculated in each dataset, labAn nodeAttrs(nbAn=4, nbComp=20, labAn=c("tutu","titi","toto","tata"))## 4 datasets, 20 components calculated in each dataset, labAn nodeAttrs(nbAn=4, nbComp=20, labAn=c("tutu","titi","toto","tata"))
This function plots the heatmaps representing the measured values of the contributing features/genes on each component. It also plots the sample annotations above each heatmap using colours.
plot_heatmapsOnSel(icaSet, selCutoff = 4, level = c("features", "genes"), samplesOrder, featuresOrder, selectionByComp, keepVar, keepComp = indComp(icaSet), doSamplesDendro = TRUE, doGenesDendro = TRUE, heatmapCol = maPalette(low = "blue", high = "red", mid = "yellow", k = 44), file = "", path = "", annot2col, ...)plot_heatmapsOnSel(icaSet, selCutoff = 4, level = c("features", "genes"), samplesOrder, featuresOrder, selectionByComp, keepVar, keepComp = indComp(icaSet), doSamplesDendro = TRUE, doGenesDendro = TRUE, heatmapCol = maPalette(low = "blue", high = "red", mid = "yellow", k = 44), file = "", path = "", annot2col, ...)
icaSet |
The IcaSet object |
selCutoff |
A numeric threshold used to select the contributing genes based on their projection values. Must be either of length 1 and the same treshold is applied to all components, or of length equal to the number of components and one specific threshold is used for each component. |
samplesOrder |
A list providing the order of the samples, per component, to be used in the heatmaps. If missing, the contribution values of the samples are used to rank the columns of the heatmaps. |
featuresOrder |
A list providing the order of the genes, per component, to be used in the heatmaps. If missing, the projection values of the genes are used to rank the rows of the heatmaps. |
selectionByComp |
A list of gene projections per component already restricted to the contributing genes, if missing is computed by the function. |
level |
A character indicating which data level is
used to plot the heatmaps: either |
keepVar |
The variable labels to be considered, i.e
a subset of the column labels of the pheno data of icaSet
available in ( |
keepComp |
A subset of components, must be included
in |
doSamplesDendro |
A logical indicating whether a hierarchical clustering has to be performed on the data matrix restricted to the contributing features/genes, and whether the corresponding dendrogram has to be plotted, default is TRUE. |
doGenesDendro |
A logical indicating if the dendrogram of features/genes has to be plotted, default is FALSE. |
heatmapCol |
A list of colors used to for heatmap
coloring (see argument |
file |
A character to add to each pdf file name.
This function creates one file by component named
"index-of-component_ |
path |
A directory for the output pdf files, must end with "/". Default is current directory. |
annot2col |
A vector of colours indexed by the
levels of the variables of |
... |
Additional parameters for function
|
This function restricts the data matrix of an
IcaSet object to the contributing
genes/features, and order features/genes and samples
either as asked by the user or according to their values
in the ICA decomposition.
The heatmap is plotted using a slightly modified version
of the function heatmap.plus from the package of
the same name. By default in this function, the
hierarchical clustering is calculated using the function
agnes with euclidean metric and
Ward's method.
A list with one element per component, each of them being a list consisting of three elements:
the matrix represented by the heatmap
,
the breaks used for the colours of the heatmap
,
the dendrogram
.
Anne Biton
heatmap.plus, image,
annot2Color,
build_sortHeatmap
## Not run: ## load an example of IcaSet object data(icaSetCarbayo) ## check which variables you would like to use in the heatmap varLabels(icaSetCarbayo) keepVar <- c("STAGE","SEX") ## Use only component 1 keepComp <- 1 ## For each component, select contributing *genes* using a threshold of 2 on the absolute projection values, ## and plot heatmaps of these contributing genes by ordering genes and samples according to their contribution values plot_heatmapsOnSel(icaSet = icaSetCarbayo, selCutoff = 2, level = "genes", keepVar = keepVar, keepComp=1, doSamplesDendro = TRUE, doGenesDendro = TRUE, heatmapCol = maPalette(low = "blue",high = "red", mid = "yellow", k=44), file = "heatmapWithoutDendro_zval3.pdf") ## For each considered component, select contributing *features* using a threshold of 2 on the absolute projection values, ## and plot heatmaps of these contributing genes with dendrograms plot_heatmapsOnSel(icaSet = icaSetCarbayo, selCutoff = 2, level = "features", keepVar = keepVar, keepComp=1, doSamplesDendro = TRUE, doGenesDendro = TRUE, heatmapCol = maPalette(low = "blue",high = "red", mid = "yellow", k=44), file = "heatmapWithDendro_zval3.pdf") ## End(Not run)## Not run: ## load an example of IcaSet object data(icaSetCarbayo) ## check which variables you would like to use in the heatmap varLabels(icaSetCarbayo) keepVar <- c("STAGE","SEX") ## Use only component 1 keepComp <- 1 ## For each component, select contributing *genes* using a threshold of 2 on the absolute projection values, ## and plot heatmaps of these contributing genes by ordering genes and samples according to their contribution values plot_heatmapsOnSel(icaSet = icaSetCarbayo, selCutoff = 2, level = "genes", keepVar = keepVar, keepComp=1, doSamplesDendro = TRUE, doGenesDendro = TRUE, heatmapCol = maPalette(low = "blue",high = "red", mid = "yellow", k=44), file = "heatmapWithoutDendro_zval3.pdf") ## For each considered component, select contributing *features* using a threshold of 2 on the absolute projection values, ## and plot heatmaps of these contributing genes with dendrograms plot_heatmapsOnSel(icaSet = icaSetCarbayo, selCutoff = 2, level = "features", keepVar = keepVar, keepComp=1, doSamplesDendro = TRUE, doGenesDendro = TRUE, heatmapCol = maPalette(low = "blue",high = "red", mid = "yellow", k=44), file = "heatmapWithDendro_zval3.pdf") ## End(Not run)
Mclust on several numeric vectorsGiven a result of function Mclust applied on
several numeric vectors, this function plots the fitted
Gaussian on their histograms.
plotAllMix(mc, A, nbMix = NULL, pdf, nbBreaks = 20, xlim = NULL)plotAllMix(mc, A, nbMix = NULL, pdf, nbBreaks = 20, xlim = NULL)
mc |
A list consisting of outputs of function
|
A |
A data.frame of dimensions 'samples x components'. |
nbMix |
The number of Gaussian to be fitted. |
nbBreaks |
The number of breaks for the histogram. |
xlim |
x-axis limits to be used in the plot. |
pdf |
A pdf file. |
This function can only deal with at the most three Gaussian
A list of Mclust results.
Anne Biton
A <-matrix(c(c(rnorm(80,mean=-0.5,sd=1),rnorm(80,mean=1,sd=0.2)),rnorm(160,mean=0.5,sd=1), c(rnorm(80,mean=-1,sd=0.3),rnorm(80,mean=0,sd=0.2))),ncol=3) ## apply function Mclust to each column of A mc <- apply(A,2,Mclust) ## plot the corresponding Gaussians on the histogram of each column plotAllMix(mc=mc,A=A) ## apply function Mclust to each column of A, and impose the fit of two Gaussian (G=2) mc <- apply(A,2,Mclust,G=2) ## plot the corresponding Gaussians on the histogram of each column plotAllMix(mc=mc,A=A) ## When arg 'mc' is missing, Mclust is applied by the function plotAllMix(A=A)A <-matrix(c(c(rnorm(80,mean=-0.5,sd=1),rnorm(80,mean=1,sd=0.2)),rnorm(160,mean=0.5,sd=1), c(rnorm(80,mean=-1,sd=0.3),rnorm(80,mean=0,sd=0.2))),ncol=3) ## apply function Mclust to each column of A mc <- apply(A,2,Mclust) ## plot the corresponding Gaussians on the histogram of each column plotAllMix(mc=mc,A=A) ## apply function Mclust to each column of A, and impose the fit of two Gaussian (G=2) mc <- apply(A,2,Mclust,G=2) ## plot the corresponding Gaussians on the histogram of each column plotAllMix(mc=mc,A=A) ## When arg 'mc' is missing, Mclust is applied by the function plotAllMix(A=A)
This function plots the correlation graph in an
interactive device using function tkplot.
plotCorGraph(dataGraph, edgeWeight = "cor", nodeAttrs, nodeShape, nodeCol = "labAn", nodeName = "indComp", col, shape, title = "", reciproCol = "reciprocal", tkplot = FALSE, ...)plotCorGraph(dataGraph, edgeWeight = "cor", nodeAttrs, nodeShape, nodeCol = "labAn", nodeName = "indComp", col, shape, title = "", reciproCol = "reciprocal", tkplot = FALSE, ...)
dataGraph |
A data.frame containing the graph
description. It must have two columns |
edgeWeight |
The column of dataGraph used to weight edges. |
nodeAttrs |
A data.frame with node description, see
function |
nodeShape |
Denotes the column of |
nodeCol |
Denotes the column of |
nodeName |
Denotes the column of |
col |
A vector of colors, for the nodes, indexed by
the unique elements of |
shape |
A vector of shapes indexed by the unique
elements of column |
title |
Title for the plot |
reciproCol |
Denotes the column of |
tkplot |
If TRUE, performs interactive plot with
function |
... |
Additional parameters as required by
|
You have to slighly move the nodes to see cliques because
strongly related nodes are often superimposed. The
edgeWeight column is used to weight the edges
within the fruchterman.reingold layout available in the
package igraph.
The argument nodeCol typically denotes the column
containing the names of the datasets. Colors are
automatically attributed to the nodes using palette Set3
of package RColorBrewer. The corresponding colors
can be directly specified in the 'col' argument. In that
case, 'col' must be a vector of colors indexed by the
unique elements contained in nodeCol column (e.g
dataset ids).
As for colors, one can define the column of
nodeAttrs that is used to define the node shapes.
The corresponding shapes can be directly specified in the
shape argument. In that case, shape must be
one of c("circle","square", " vcsquare",
"rectangle", "crectangle", "vrectangle") and must be
indexed by the unique elements of nodeShape
column.
Unfortunately, shapes can't be taken into account when tkplot is TRUE (interactive plot).
If reciproCol is not missing, it is used to color
the edges, either in grey if the edge is not reciprocal
or in black if the edge is reciprocal.
A list consisting of
a data.frame defining the correlation graph
a data.frame describing the node of the graph
the graph as an object of class
igraph
the id of the graph plotted
using tkplot
Anne Biton
compareAn, nodeAttrs,
compareAn2graphfile,
runCompareIcaSets
dat1 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat1) <- paste("g", 1:1000, sep="") colnames(dat1) <- paste("s", 1:10, sep="") dat2 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat2) <- paste("g", 1:1000, sep="") colnames(dat2) <- paste("s", 1:10, sep="") ## run ICA resJade1 <- runICA(X=dat1, nbComp=3, method = "JADE") resJade2 <- runICA(X=dat2, nbComp=3, method = "JADE") ## build params params <- buildMineICAParams(resPath="toy/") ## build IcaSet object icaSettoy1 <- buildIcaSet(params=params, A=data.frame(resJade1$A), S=data.frame(resJade1$S), dat=dat1, alreadyAnnot=TRUE)$icaSet icaSettoy2 <- buildIcaSet(params=params, A=data.frame(resJade2$A), S=data.frame(resJade2$S), dat=dat2, alreadyAnnot=TRUE)$icaSet icaSets <- list(icaSettoy1, icaSettoy2) resCompareAn <- compareAn(icaSets=list(icaSettoy1,icaSettoy2), labAn=c("toy1","toy2"), type.corr="pearson", level="genes", cutoff_zval=0) ## Build a graph where edges correspond to maximal correlation value (useVal="cor"), dataGraph <- compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, useVal="cor", file="myGraph.txt") ## construction of the data.frame with the node description nbComp <- rep(3,2) #each IcaSet contains 3 components nbAn <- 2 # we are comparing 2 IcaSets # labels of components created as comp*i* labComp <- foreach(icaSet=icaSets, nb=nbComp, an=1:nbAn) %do% { paste(rep("comp",sum(nb)),1:nbComp(icaSet),sep = "")} # creation of the data.frame with the node description nodeDescr <- nodeAttrs(nbAn = nbAn, nbComp = nbComp, labComp = labComp, labAn = c("toy1","toy2"), file = "nodeInfo.txt") ## Plot correlation graph, slightly move the attached nodes to make the cliques visible ## use tkplot=TRUE to have an interactive graph res <- plotCorGraph(title = "Compare toy 1 and 2", dataGraph = dataGraph, nodeName = "indComp", tkplot = FALSE, nodeAttrs = nodeDescr, edgeWeight = "cor", nodeShape = "labAn", reciproCol = "reciprocal") ## Not run: ## load two microarray datasets library(breastCancerMAINZ) library(breastCancerVDX) data(mainz) data(vdx) ## Define a function used to build two examples of IcaSet objects treat <- function(es, annot="hgu133a.db") { es <- selectFeatures_IQR(es,10000) exprs(es) <- t(apply(exprs(es),1,scale,scale=FALSE)) colnames(exprs(es)) <- sampleNames(es) resJade <- runICA(X=exprs(es), nbComp=10, method = "JADE", maxit=10000) resBuild <- buildIcaSet(params=buildMineICAParams(), A=data.frame(resJade$A), S=data.frame(resJade$S), dat=exprs(es), pData=pData(es), refSamples=character(0), annotation=annot, typeID= typeIDmainz, chipManu = "affymetrix", mart=mart) icaSet <- resBuild$icaSet } ## Build the two IcaSet objects icaSetMainz <- treat(mainz) icaSetVdx <- treat(vdx) icaSets <- list(icaSetMainz, icaSetVdx) labAn <- c("Mainz", "Vdx") ## correlations between gene projections of each pair of IcaSet resCompareAn <- compareAn(icaSets = icaSets, level = "genes", type.corr= "pearson", labAn = labAn, cutoff_zval=0) ## construction of the correlation graph using previous output dataGraph <- compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, file="corGraph.txt") ## construction of the data.frame with the node description nbComp <- rep(10,2) #each IcaSet contains 10 components nbAn <- 2 # we are comparing 2 IcaSets # labels of components created as comp*i* labComp <- foreach(icaSet=icaSets, nb=nbComp, an=1:nbAn) %do% { paste(rep("comp",sum(nb)),1:nbComp(icaSet),sep = "")} # creation of the data.frame with the node description nodeDescr <- nodeAttrs(nbAn = nbAn, nbComp = nbComp, labComp = labComp, labAn = labAn, file = "nodeInfo.txt") ## Plot correlation graph, slightly move the attached nodes to make the cliques visible res <- plotCorGraph(title = "Compare two ICA decomsitions obtained on \n two microarray-based data of breast tumors", dataGraph = dataGraph, nodeName = "indComp", nodeAttrs = nodeDescr, edgeWeight = "cor", nodeShape = "labAn", reciproCol = "reciprocal") ## End(Not run)dat1 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat1) <- paste("g", 1:1000, sep="") colnames(dat1) <- paste("s", 1:10, sep="") dat2 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat2) <- paste("g", 1:1000, sep="") colnames(dat2) <- paste("s", 1:10, sep="") ## run ICA resJade1 <- runICA(X=dat1, nbComp=3, method = "JADE") resJade2 <- runICA(X=dat2, nbComp=3, method = "JADE") ## build params params <- buildMineICAParams(resPath="toy/") ## build IcaSet object icaSettoy1 <- buildIcaSet(params=params, A=data.frame(resJade1$A), S=data.frame(resJade1$S), dat=dat1, alreadyAnnot=TRUE)$icaSet icaSettoy2 <- buildIcaSet(params=params, A=data.frame(resJade2$A), S=data.frame(resJade2$S), dat=dat2, alreadyAnnot=TRUE)$icaSet icaSets <- list(icaSettoy1, icaSettoy2) resCompareAn <- compareAn(icaSets=list(icaSettoy1,icaSettoy2), labAn=c("toy1","toy2"), type.corr="pearson", level="genes", cutoff_zval=0) ## Build a graph where edges correspond to maximal correlation value (useVal="cor"), dataGraph <- compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, useVal="cor", file="myGraph.txt") ## construction of the data.frame with the node description nbComp <- rep(3,2) #each IcaSet contains 3 components nbAn <- 2 # we are comparing 2 IcaSets # labels of components created as comp*i* labComp <- foreach(icaSet=icaSets, nb=nbComp, an=1:nbAn) %do% { paste(rep("comp",sum(nb)),1:nbComp(icaSet),sep = "")} # creation of the data.frame with the node description nodeDescr <- nodeAttrs(nbAn = nbAn, nbComp = nbComp, labComp = labComp, labAn = c("toy1","toy2"), file = "nodeInfo.txt") ## Plot correlation graph, slightly move the attached nodes to make the cliques visible ## use tkplot=TRUE to have an interactive graph res <- plotCorGraph(title = "Compare toy 1 and 2", dataGraph = dataGraph, nodeName = "indComp", tkplot = FALSE, nodeAttrs = nodeDescr, edgeWeight = "cor", nodeShape = "labAn", reciproCol = "reciprocal") ## Not run: ## load two microarray datasets library(breastCancerMAINZ) library(breastCancerVDX) data(mainz) data(vdx) ## Define a function used to build two examples of IcaSet objects treat <- function(es, annot="hgu133a.db") { es <- selectFeatures_IQR(es,10000) exprs(es) <- t(apply(exprs(es),1,scale,scale=FALSE)) colnames(exprs(es)) <- sampleNames(es) resJade <- runICA(X=exprs(es), nbComp=10, method = "JADE", maxit=10000) resBuild <- buildIcaSet(params=buildMineICAParams(), A=data.frame(resJade$A), S=data.frame(resJade$S), dat=exprs(es), pData=pData(es), refSamples=character(0), annotation=annot, typeID= typeIDmainz, chipManu = "affymetrix", mart=mart) icaSet <- resBuild$icaSet } ## Build the two IcaSet objects icaSetMainz <- treat(mainz) icaSetVdx <- treat(vdx) icaSets <- list(icaSetMainz, icaSetVdx) labAn <- c("Mainz", "Vdx") ## correlations between gene projections of each pair of IcaSet resCompareAn <- compareAn(icaSets = icaSets, level = "genes", type.corr= "pearson", labAn = labAn, cutoff_zval=0) ## construction of the correlation graph using previous output dataGraph <- compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, file="corGraph.txt") ## construction of the data.frame with the node description nbComp <- rep(10,2) #each IcaSet contains 10 components nbAn <- 2 # we are comparing 2 IcaSets # labels of components created as comp*i* labComp <- foreach(icaSet=icaSets, nb=nbComp, an=1:nbAn) %do% { paste(rep("comp",sum(nb)),1:nbComp(icaSet),sep = "")} # creation of the data.frame with the node description nodeDescr <- nodeAttrs(nbAn = nbAn, nbComp = nbComp, labComp = labComp, labAn = labAn, file = "nodeInfo.txt") ## Plot correlation graph, slightly move the attached nodes to make the cliques visible res <- plotCorGraph(title = "Compare two ICA decomsitions obtained on \n two microarray-based data of breast tumors", dataGraph = dataGraph, nodeName = "indComp", nodeAttrs = nodeDescr, edgeWeight = "cor", nodeShape = "labAn", reciproCol = "reciprocal") ## End(Not run)
Mclust
Given a result of function Mclust applied to a
numeric vector, this function draws the fitted Gaussian
on the histogram of the data values.
plotMix(mc, data, nbBreaks, traceDensity = TRUE, title = "", xlim, ylim, ...)plotMix(mc, data, nbBreaks, traceDensity = TRUE, title = "", xlim, ylim, ...)
mc |
The result of Mclust function applied to
argument |
data |
A vector of numeric values |
nbBreaks |
The number of breaks for the histogram |
traceDensity |
If TRUE (default) density are displayed on the y-axis, else if FALSE counts are displayed on the y-acis |
title |
A title for the plot |
xlim |
x-axis limits to be used in the plot |
ylim |
y-axis limits to be used in the plot |
... |
additional arguments for hist |
A shapiro test p-value is added to the plot title. This function can only deal with at the most three Gaussian.
NULL
Anne Biton
## create a mix of two Gaussian v <-c(rnorm(80,mean=-0.5,sd=1),rnorm(80,mean=1,sd=0.2)) ## apply Mclust mc <- Mclust(v) ## plot fitted Gaussian on histogram of v plotMix(mc=mc,data=v,nbBreaks=30)## create a mix of two Gaussian v <-c(rnorm(80,mean=-0.5,sd=1),rnorm(80,mean=1,sd=0.2)) ## apply Mclust mc <- Mclust(v) ## plot fitted Gaussian on histogram of v plotMix(mc=mc,data=v,nbBreaks=30)
This function plots the positions of groups of samples
formed by the variables (i.e the sample annotations)
across all the components of an object of class
icaSet. For each
variable level (e.g for each tumor stage) this function
plots the positions of the corresponding samples (e.g the
subset of samples having this tumor stage) within the
histogram of the global sample contributions. The plots
are saved in pdf file, one file is created per variable.
The pdf files are names 'variable.pdf' and save either in
pathPlot if specified or the current directory.
plotPosAnnotInComp(icaSet, params, keepVar = varLabels(icaSet), keepComp = indComp(icaSet), keepSamples = sampleNames(icaSet), pathPlot = NULL, breaks = 20, colAll = "grey74", colSel, resClus, funClus = c("Mclust", "kmeans"), nbClus = 2, by = c("annot", "component"), typeImage = c("pdf", "png", "none"), ...)plotPosAnnotInComp(icaSet, params, keepVar = varLabels(icaSet), keepComp = indComp(icaSet), keepSamples = sampleNames(icaSet), pathPlot = NULL, breaks = 20, colAll = "grey74", colSel, resClus, funClus = c("Mclust", "kmeans"), nbClus = 2, by = c("annot", "component"), typeImage = c("pdf", "png", "none"), ...)
icaSet |
An object of class |
params |
A |
keepVar |
The variable labels to be considered, i.e
a subset of the column labels of the pheno data of icaSet
available in ( |
keepComp |
A subset of components available in
|
keepSamples |
A subset of samples, must be available
in |
pathPlot |
A character specifying the path where the plots will be saved |
breaks |
The number of breaks to be used in the histograms |
colSel |
The colour of the histogram of the group of interest, default is "red" |
colAll |
The colour of the global histogram, default is "grey74" |
resClus |
A list containing the outputs of function
|
funClus |
The clustering method to be used, either
|
nbClus |
If |
by |
Either |
typeImage |
The type of image to be created, either "pdf" (default) or "png". "png" is not recommended, unless there are at the most 4 histograms to be plotted, because it does not allow to deal with multiple pages of plots. |
... |
Additional parameters for function
|
The plotted values are the sample contributions across
the components, i.e across the columns of
A(icaSet).
If argument resClus is missing, the function
computes the clustering of the samples on each component
(i.e on each column of A(icaSet)) using
funClus and nbClus.
The association between the clusters and the considered sample group is tested using a chi-square test. The p-values of these tests are available in the title of each plot.
When by="annot" this function plots the histograms
of each variable across all components, to plot the
histograms for each component across variables, please
use by="component".
NULL
Anne Biton
plotPosSamplesInComp, chisq.test
## Not run: ## load an example of IcaSet data(icaSetCarbayo) ## Use icaSetCarbayo, look at the available annotations varLabels(icaSetCarbayo) ## Plot positions of samples in components according to annotations 'SEX' and 'STAGE' # plots are saved in files SEX.pdf and STAGE.pdf created in the current directory plotPosAnnotInComp(icaSet=icaSetCarbayo, keepVar=c("SEX","STAGE"), keepComp=1:2, funClus="Mclust") # specifiy arg 'pathPlot' to save the pdf in another directory, but make sure it exists before # specifiy arg 'by="comp"' to create one pdf file per component ## End(Not run)## Not run: ## load an example of IcaSet data(icaSetCarbayo) ## Use icaSetCarbayo, look at the available annotations varLabels(icaSetCarbayo) ## Plot positions of samples in components according to annotations 'SEX' and 'STAGE' # plots are saved in files SEX.pdf and STAGE.pdf created in the current directory plotPosAnnotInComp(icaSet=icaSetCarbayo, keepVar=c("SEX","STAGE"), keepComp=1:2, funClus="Mclust") # specifiy arg 'pathPlot' to save the pdf in another directory, but make sure it exists before # specifiy arg 'by="comp"' to create one pdf file per component ## End(Not run)
This function tests if the groups of samples formed by
the variables are differently distributed on the
components, in terms of contribution value (i.e of values
in matrix A(icaSet)). The distribution of the
samples on the components are represented using either
density plots of boxplots. It is possible to restrict the
tests and the plots to a subset of samples and/or
components.
qualVarAnalysis(params, icaSet, keepVar, keepComp = indComp(icaSet), keepSamples = sampleNames(icaSet), adjustBy = c("none", "component", "variable"), method = "BH", doPlot = TRUE, typePlot = "density", addPoints = FALSE, onlySign = TRUE, cutoff = params["pvalCutoff"], colours = annot2col(params), path = "qualVarAnalysis/", filename = "qualVar", typeImage = "png")qualVarAnalysis(params, icaSet, keepVar, keepComp = indComp(icaSet), keepSamples = sampleNames(icaSet), adjustBy = c("none", "component", "variable"), method = "BH", doPlot = TRUE, typePlot = "density", addPoints = FALSE, onlySign = TRUE, cutoff = params["pvalCutoff"], colours = annot2col(params), path = "qualVarAnalysis/", filename = "qualVar", typeImage = "png")
params |
An object of class
|
icaSet |
An object of class
|
keepVar |
The variable labels to be considered, must
be a subset of |
keepComp |
A subset of components, must be included
in |
keepSamples |
A subset of samples, must be included
in |
adjustBy |
The way the p-values of the Wilcoxon and
Kruskal-Wallis tests should be corrected for multiple
testing: |
method |
The correction method, see
|
doPlot |
If TRUE (default), the plots are done, else only tests are performed. |
addPoints |
If TRUE, points are superimposed on the boxplot. |
typePlot |
The type of plot, either |
onlySign |
If TRUE (default), only the significant results are plotted. |
cutoff |
A threshold p-value for statistical significance. |
colours |
A vector of colours indexed by the
variable levels, if missing the colours are automatically
generated using |
path |
A directory _within resPath(params)_ where
the files containing the plots and the p-value results
will be located. Default is |
typeImage |
The type of image file to be used. |
filename |
The name of the HTML file containing the p-values of the tests, if NULL no file is created. |
This function writes an HTML file containing the results
of the tests as a an array of dimensions 'variables *
components' containing the p-values of the tests. When a
p-value is considered as significant according to the
threshold cutoff, it is written in bold and filled
with a link pointing to the corresponding plot. One image
is created by plot and located into the sub-directory
"plots/" of path. Each image is named by
index-of-component_var.png. Wilcoxon or Kruskal-Wallis
tests are performed depending on the number of groups of
interest in the considered variable (argument
keepLev).
Returns A data.frame of dimensions 'components x variables' containing the p-values of the non-parametric tests (Wilcoxon or Kruskal-Wallis tests) wich test if the samples groups defined by each variable are differently distributed on the components.
Anne Biton
, qualVarAnalysis, p.adjust,
link{writeHtmlResTestsByAnnot},
wilcox.test, kruskal.test
## load an example of IcaSet data(icaSetCarbayo) ## build MineICAParams object params <- buildMineICAParams(resPath="carbayo/") ## Define the directory containing the results dir <- paste(resPath(params), "comp2annot/", sep="") ## Run tests, make no adjustment of the p-values, # for variable grade and components 1 and 2, # and plot boxplots when 'doPlot=TRUE'. qualVarAnalysis(params=params, icaSet=icaSetCarbayo, adjustBy="none", typePlot="boxplot", keepVar="GRADE", keepComp=1:2, path=dir, doPlot=FALSE)## load an example of IcaSet data(icaSetCarbayo) ## build MineICAParams object params <- buildMineICAParams(resPath="carbayo/") ## Define the directory containing the results dir <- paste(resPath(params), "comp2annot/", sep="") ## Run tests, make no adjustment of the p-values, # for variable grade and components 1 and 2, # and plot boxplots when 'doPlot=TRUE'. qualVarAnalysis(params=params, icaSet=icaSetCarbayo, adjustBy="none", typePlot="boxplot", keepVar="GRADE", keepComp=1:2, path=dir, doPlot=FALSE)
This function tests if numeric variables are correlated with components.
quantVarAnalysis(params, icaSet, keepVar, keepComp = indComp(icaSet), keepSamples = sampleNames(icaSet), adjustBy = c("none", "component", "variable"), method = "BH", typeCor = "pearson", doPlot = TRUE, onlySign = TRUE, cutoff = 0.4, cutoffOn = c("cor", "pval"), colours, path = "quantVarAnalysis/", filename = "quantVar", typeImage = "png")quantVarAnalysis(params, icaSet, keepVar, keepComp = indComp(icaSet), keepSamples = sampleNames(icaSet), adjustBy = c("none", "component", "variable"), method = "BH", typeCor = "pearson", doPlot = TRUE, onlySign = TRUE, cutoff = 0.4, cutoffOn = c("cor", "pval"), colours, path = "quantVarAnalysis/", filename = "quantVar", typeImage = "png")
params |
An object of class
|
icaSet |
An object of class
|
keepVar |
The variable labels to be considered, must
be a subset of |
keepComp |
A subset of components, must be included
in |
keepSamples |
A subset of samples, must be included
in |
adjustBy |
The way the p-values of the Wilcoxon and
Kruskal-Wallis tests should be corrected for multiple
testing: |
method |
The correction method, see
|
doPlot |
If TRUE (default), the plots are done, else only tests are performed. |
onlySign |
If TRUE (default), only the significant results are plotted. |
cutoff |
A threshold p-value for statistical significance. |
cutoffOn |
The value the cutoff is applied to, either "cor" for correlation or "pval" for p-value |
typeCor |
the type of correlation to be used, one of
|
colours |
A vector of colours indexed by the
variable levels, if missing the colours are automatically
generated using |
path |
A directory _within resPath(params)_ where
the files containing the plots and the p-value results
will be located. Default is |
typeImage |
The type of image file to be used. |
filename |
The name of the HTML file containing the p-values of the tests, if NULL no file is created. |
This function writes an HTML file containing the
correlation values and test p-values as a an array of
dimensions 'variables * components' containing the
p-values of the tests. When a p-value is considered as
significant according to the threshold cutoff, it
is written in bold and filled with a link pointing to the
corresponding plot. One image is created by plot and
located into the sub-directory "plots/" of path.
Each image is named by index-of-component_var.png.
Returns A data.frame of dimensions 'components x variables' containing the p-values of the non-parametric tests (Wilcoxon or Kruskal-Wallis tests) wich test if the samples groups defined by each variable are differently distributed on the components.
Anne Biton
qualVarAnalysis, p.adjust,
link{writeHtmlResTestsByAnnot}, code
## load an example of IcaSet data(icaSetCarbayo) # build MineICAParams object params <- buildMineICAParams(resPath="carbayo/") # Define the directory containing the results dir <- paste(resPath(params), "comp2annottest/", sep="") # Check which variables are numeric looking at the pheno data, here only one -> AGE # pData(icaSetCarbayo) ## Perform pearson correlation tests and plots association corresponding # to correlation values larger than 0.2 quantVarAnalysis(params=params, icaSet=icaSetCarbayo, keepVar="AGE", keepComp=1:2, adjustBy="none", path=dir, cutoff=0.2, cutoffOn="cor") ## Not run: ## Perform Spearman correlation tests and do scatter plots for all pairs quantVarAnalysis(params=params, icaSet=icaSetCarbayo, keepVar="AGE", adjustBy="none", path=dir, cutoff=0.1, cutoffOn="cor", typeCor="spearman", onlySign=FALSE) ## Perform pearson correlation tests and plots association corresponding # to p-values lower than 0.05 when 'doPlot=TRUE' quantVarAnalysis(params=params, icaSet=icaSetCarbayo, keepVar="AGE", adjustBy="none", path=dir, cutoff=0.05, cutoffOn="pval", doPlot=FALSE) ## End(Not run)## load an example of IcaSet data(icaSetCarbayo) # build MineICAParams object params <- buildMineICAParams(resPath="carbayo/") # Define the directory containing the results dir <- paste(resPath(params), "comp2annottest/", sep="") # Check which variables are numeric looking at the pheno data, here only one -> AGE # pData(icaSetCarbayo) ## Perform pearson correlation tests and plots association corresponding # to correlation values larger than 0.2 quantVarAnalysis(params=params, icaSet=icaSetCarbayo, keepVar="AGE", keepComp=1:2, adjustBy="none", path=dir, cutoff=0.2, cutoffOn="cor") ## Not run: ## Perform Spearman correlation tests and do scatter plots for all pairs quantVarAnalysis(params=params, icaSet=icaSetCarbayo, keepVar="AGE", adjustBy="none", path=dir, cutoff=0.1, cutoffOn="cor", typeCor="spearman", onlySign=FALSE) ## Perform pearson correlation tests and plots association corresponding # to p-values lower than 0.05 when 'doPlot=TRUE' quantVarAnalysis(params=params, icaSet=icaSetCarbayo, keepVar="AGE", adjustBy="none", path=dir, cutoff=0.05, cutoffOn="pval", doPlot=FALSE) ## End(Not run)
Computes the relative path between two imbricated paths
relativePath(path1, path2)relativePath(path1, path2)
path1 |
The first path |
path2 |
The second path |
path1 and path2 must be imbricated.
The relative path between path1 and path2
Anne Biton
path1 <- "home/lulu/res/gene2comp/" path2 <- "home/lulu/res/comp2annot/invasive/" relativePath(path1,path2)path1 <- "home/lulu/res/gene2comp/" path2 <- "home/lulu/res/comp2annot/invasive/" relativePath(path1,path2)
This function runs the analysis of an ICA decomposition contained in an IcaSet object, according to the parameters entered by the user and contained in a MineICAParams.
runAn(params, icaSet, keepVar, heatmapCutoff = params["selCutoff"], funClus = c("Mclust", "kmeans"), nbClus, clusterOn = "A", keepComp, keepSamples, adjustBy = c("none", "component", "variable"), typePlot = c("boxplot", "density"), mart = useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl"), dbGOstats = c("KEGG", "GO"), ontoGOstats = "BP", condGOstats = TRUE, cutoffGOstats = params["pvalCutoff"], writeGenesByComp = TRUE, writeFeaturesByComp = FALSE, selCutoffWrite = 2.5, runVarAnalysis = TRUE, onlySign = T, runClustering = FALSE, runGOstats = TRUE, plotHist = TRUE, plotHeatmap = TRUE)runAn(params, icaSet, keepVar, heatmapCutoff = params["selCutoff"], funClus = c("Mclust", "kmeans"), nbClus, clusterOn = "A", keepComp, keepSamples, adjustBy = c("none", "component", "variable"), typePlot = c("boxplot", "density"), mart = useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl"), dbGOstats = c("KEGG", "GO"), ontoGOstats = "BP", condGOstats = TRUE, cutoffGOstats = params["pvalCutoff"], writeGenesByComp = TRUE, writeFeaturesByComp = FALSE, selCutoffWrite = 2.5, runVarAnalysis = TRUE, onlySign = T, runClustering = FALSE, runGOstats = TRUE, plotHist = TRUE, plotHeatmap = TRUE)
params |
An object of class
|
icaSet |
An object of class
|
keepVar |
The variable labels to be considered, i.e
a subset of the annotation variables available in
( |
keepSamples |
The samples to be considered, i.e a
subset of ( |
heatmapCutoff |
The cutoff (applied to the scaled feature/gene projections contained in S/SByGene) used to select the contributing features/genes. |
funClus |
The function to be used to cluster the
samples, must be one of
|
nbClus |
The number of clusters to be computed when
applying |
keepComp |
The indices of the components to be
analyzed, must be included in |
adjustBy |
The way the p-values of the Wilcoxon and
Kruskal-Wallis tests should be corrected for multiple
testing: |
typePlot |
The type of plot used to show distribution of sample-groups contributions, either "density" or "boxplot" |
mart |
A mart object used for annotation, see
function |
dbGOstats |
The used database to use ('GO' and/or 'KEGG'), default is both. |
ontoGOstats |
A string specifying the GO ontology to
use. Must be one of 'BP', 'CC', or 'MF', see
|
condGOstats |
A logical indicating whether the
calculation should conditioned on the GO structure, see
|
cutoffGOstats |
The p-value threshold used for selecting enriched gene sets, default is params["pvalCutoff"] |
writeGenesByComp |
If TRUE (default) the gene
projections ( |
writeFeaturesByComp |
If TRUE (default) the feature
projections ( |
runGOstats |
If TRUE the enrichment analysis of the
contributing genes is run for each component using
package |
plotHist |
If TRUE the position of the sample annotations within the histograms of the sample contributions are plotted. |
plotHeatmap |
If TRUE the heatmap of the contributing features/genes are plotted for each component. |
runClustering |
If TRUE the potential associations between a clustering of the samples (performed according to the components), and the sample annotations, are tested using chi-squared tests. |
runVarAnalysis |
If TRUE the potential associations
between sample contributions (contained in
|
onlySign |
If TRUE (default), only the significant
results are plotted in functions |
selCutoffWrite |
The cutoff applied to the absolute
feature/gene projection values to select the
features/genes that will be annotated using package
|
clusterOn |
Specifies the matrix used to apply
clustering if
|
This function calls functions of the MineICA package depending on the arguments:
writeProjByComp (if
writeGenesByComp=TRUE or
writeFeaturesByComp)which writes in html files the description of the features/genes contributing to each component, and their projection values on all the components.
plot_heatmapsOnSel (if
plotHeatmap=TRUE)which plots heatmaps of the data restricted to the contributing features/genes of each component.
plotPosAnnotInComp
(if plotHist=TRUE)which plots, within the
histogram of the sample contribution values of every
component, the position of groups of samples formed
according to the sample annotations contained in
pData(icaSet).
clusterSamplesByComp (if
runClustering=TRUE)which clusters the samples according to each component.
clusVarAnalysis (if
runClustering=TRUE)which computes the
chi-squared test of association between a given
clustering of the samples and each annotation level
contained in pData(icaSet), and summarizes the
results in an HTML file.
runEnrich
(if runGOstats=TRUE)which perforns enrichment analysis of the contributing genes of the components using package GOstats.
qualVarAnalysis and
quantVarAnalysis (if
varAnalysis=TRUE)which tests if the groups of
samples formed according to sample annotations contained
in pData(icaSet) are differently distributed on
the components, in terms of contribution value.
Several directories containing the results of each analysis are created by the function:
contains the annotations of the features or genes, one file per component;
contains two directories: 'qual/' and 'quant/' which respectively contain the results of the association between components qualitative and quantitative variables;
contains the heatmaps (one pdf file per component) of contributing genes by component;
contains athe histograms of sample contributions superimposed with the histograms of the samples grouped by variable;
contains the association between a
clustering of the samples performed on the mixing matrix
A and the variables.
NULL
Anne Biton
## Not run: ## load an example of IcaSet data(icaSetCarbayo) ## make sure the 'mart' attribute is correctly defined mart(icaSetCarbayo) <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl") ## creation of an object of class MineICAParams ## here we use a low threshold because 'icaSetCarbayo' is already # restricted to the contributing features/genes params <- buildMineICAParams(resPath="~/resMineICACarbayotestRunAn/", selCutoff=2, pvalCutoff=0.05) require(hgu133a.db) runAn(params=params, icaSet=icaSetCarbayo) ## End(Not run)## Not run: ## load an example of IcaSet data(icaSetCarbayo) ## make sure the 'mart' attribute is correctly defined mart(icaSetCarbayo) <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl") ## creation of an object of class MineICAParams ## here we use a low threshold because 'icaSetCarbayo' is already # restricted to the contributing features/genes params <- buildMineICAParams(resPath="~/resMineICACarbayotestRunAn/", selCutoff=2, pvalCutoff=0.05) require(hgu133a.db) runAn(params=params, icaSet=icaSetCarbayo) ## End(Not run)
This function encompasses the comparison of several IcaSet objects using correlations and the plot of the corresponding correlation graph. The IcaSet objects are compared by calculating the correlation between either projection values of common features or genes, or contributions of common samples.
runCompareIcaSets(icaSets, labAn, type.corr = c("pearson", "spearman"), cutoff_zval = 0, level = c("genes", "features", "samples"), fileNodeDescr = NULL, fileDataGraph = NULL, plot = TRUE, title = "", col, cutoff_graph = NULL, useMax = TRUE, tkplot = FALSE)runCompareIcaSets(icaSets, labAn, type.corr = c("pearson", "spearman"), cutoff_zval = 0, level = c("genes", "features", "samples"), fileNodeDescr = NULL, fileDataGraph = NULL, plot = TRUE, title = "", col, cutoff_graph = NULL, useMax = TRUE, tkplot = FALSE)
icaSets |
List of |
labAn |
Vector of names for each icaSet, e.g the the names of the datasets on which were calculated the decompositions. |
type.corr |
Type of correlation to compute, either
|
cutoff_zval |
Either NULL or 0 (default) if all
genes are used to compute the correlation between the
components, or a threshold to compute the correlation
using the genes that have at least a scaled projection
higher than cutoff_zval. Will be used only when
|
level |
Data level of the |
fileNodeDescr |
File where node descriptions are saved (useful when the user wants to visualize the graph using Cytoscape). |
fileDataGraph |
File where graph description is saved (useful when the user wants to visualize the graph using Cytoscape). |
plot |
if |
title |
title of the graph |
col |
vector of colors indexed by elements of labAn; if missing, colors will be automatically attributed |
cutoff_graph |
the cutoff used to select pairs that will be included in the graph |
useMax |
if |
tkplot |
If TRUE, performs interactive plot with
function |
This function calls four functions:
compareAn which computes the correlations,
compareAn2graphfile which builds the graph,
nodeAttrs which builds the node description
data, and plotCorGraph which uses tkplot to
plot the graph in an interactive device.
If the user wants to see the correlation graph in
Cytoscape, he must fill the arguments
fileDataGraph and fileNodeDescr, in order
to import the graph and its node descriptions as a .txt
file in Cytoscape.
When labAn is missing, each element i of
icaSets is labeled as 'Ani'.
The user must carefully choose the data level used in the
comparison: If level='samples', the correlations
are based on the mixing matrices of the ICA
decompositions (of dimension samples x components).
'A' will be typically chosen when the ICA
decompositions were computed on the same dataset, or on
datasets that include the same samples. If
level='features' is chosen, the correlation is
calculated between the source matrices (of dimension
features x components) of the ICA decompositions.
'S' will be typically used when the ICA
decompositions share common features (e.g same
microarrays). If level='genes', the correlations
are calculated on the attributes 'SByGene' which
store the projections of the annotated features.
'SByGene' will be typically chosen when ICA were
computed on datasets from different technologies, for
which comparison is possible only after annotation into a
common ID, like genes.
cutoff_zval is only used when level is one
of c('features','genes'), in order to restrict the
correlation to the contributing features or genes.
When cutoff_zval is specified, for each pair of
components, genes or features that are included in the
circle of center 0 and radius cutoff_zval are
excluded from the computation of the correlation.
It must be taken into account by the user that if cutoff_zval is different from NULL or zero, the computation will be much slowler since each pair of component is treated individually.
Edges of the graph are built based on the correlation values between the components. Absolute values of correlations are used since components have no direction.
If useMax is TRUE each component will be
linked to only one component of each other IcaSet that
corresponds to the most correlated component among all
components of the same IcaSet. If cutoff_graph is
specified, only correlations exceeding this value are
taken into account to build the graph. For example, if
cutoff is 1, only relationships between components
that correspond to a correlation value higher than 1 will
be included. Absolute correlation values are used since
the components have no direction.
The contents of the returned list are
dataGraph data.frame that
describes the correlation graph,
nodeAttrs data.frame that
describes the node of the graph
graph the graph as an igraph-object,
graphid the id of the graph
plotted using tkplot.
A list consisting of
a data.frame defining the correlation graph
a data.frame describing the node of the graph,
the graph as an object of class
igraph,
the id of the graph
plotted with tkplot
.
Anne Biton
compareAn2graphfile,
compareAn, cor2An,
plotCorGraph
dat1 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat1) <- paste("g", 1:1000, sep="") colnames(dat1) <- paste("s", 1:10, sep="") dat2 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat2) <- paste("g", 1:1000, sep="") colnames(dat2) <- paste("s", 1:10, sep="") ## run ICA resJade1 <- runICA(X=dat1, nbComp=3, method = "JADE") resJade2 <- runICA(X=dat2, nbComp=3, method = "JADE") ## build params params <- buildMineICAParams(resPath="toy/") ## build IcaSet objects icaSettoy1 <- buildIcaSet(params=params, A=data.frame(resJade1$A), S=data.frame(resJade1$S), dat=dat1, alreadyAnnot=TRUE)$icaSet icaSettoy2 <- buildIcaSet(params=params, A=data.frame(resJade2$A), S=data.frame(resJade2$S), dat=dat2, alreadyAnnot=TRUE)$icaSet ## compare IcaSet objects ## use tkplot=TRUE to get an interactive graph rescomp <- runCompareIcaSets(icaSets=list(icaSettoy1, icaSettoy2), labAn=c("toy1","toy2"), type.corr="pearson", level="genes", tkplot=FALSE) ## Not run: ## load the microarray-based gene expression datasets ## of breast tumors library(breastCancerMAINZ) library(breastCancerVDX) data(mainz) data(vdx) ## Define a function used to build two examples of IcaSet objects ## and annotate the probe sets into gene Symbols treat <- function(es, annot="hgu133a.db") { es <- selectFeatures_IQR(es,10000) exprs(es) <- t(apply(exprs(es),1,scale,scale=FALSE)) colnames(exprs(es)) <- sampleNames(es) resJade <- runICA(X=exprs(es), nbComp=10, method = "JADE", maxit=10000) resBuild <- buildIcaSet(params=buildMineICAParams(), A=data.frame(resJade$A), S=data.frame(resJade$S), dat=exprs(es), pData=pData(es), refSamples=character(0), annotation=annot, typeID= typeIDmainz, chipManu = "affymetrix", mart=mart) icaSet <- resBuild$icaSet } ## Build the two IcaSet objects icaSetMainz <- treat(mainz) icaSetVdx <- treat(vdx) ## compare the IcaSets runCompareIcaSets(icaSets=list(icaSetMainz, icaSetVdx), labAn=c("Mainz","Vdx"), type.corr="pearson", level="genes") ## End(Not run)dat1 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat1) <- paste("g", 1:1000, sep="") colnames(dat1) <- paste("s", 1:10, sep="") dat2 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000)) rownames(dat2) <- paste("g", 1:1000, sep="") colnames(dat2) <- paste("s", 1:10, sep="") ## run ICA resJade1 <- runICA(X=dat1, nbComp=3, method = "JADE") resJade2 <- runICA(X=dat2, nbComp=3, method = "JADE") ## build params params <- buildMineICAParams(resPath="toy/") ## build IcaSet objects icaSettoy1 <- buildIcaSet(params=params, A=data.frame(resJade1$A), S=data.frame(resJade1$S), dat=dat1, alreadyAnnot=TRUE)$icaSet icaSettoy2 <- buildIcaSet(params=params, A=data.frame(resJade2$A), S=data.frame(resJade2$S), dat=dat2, alreadyAnnot=TRUE)$icaSet ## compare IcaSet objects ## use tkplot=TRUE to get an interactive graph rescomp <- runCompareIcaSets(icaSets=list(icaSettoy1, icaSettoy2), labAn=c("toy1","toy2"), type.corr="pearson", level="genes", tkplot=FALSE) ## Not run: ## load the microarray-based gene expression datasets ## of breast tumors library(breastCancerMAINZ) library(breastCancerVDX) data(mainz) data(vdx) ## Define a function used to build two examples of IcaSet objects ## and annotate the probe sets into gene Symbols treat <- function(es, annot="hgu133a.db") { es <- selectFeatures_IQR(es,10000) exprs(es) <- t(apply(exprs(es),1,scale,scale=FALSE)) colnames(exprs(es)) <- sampleNames(es) resJade <- runICA(X=exprs(es), nbComp=10, method = "JADE", maxit=10000) resBuild <- buildIcaSet(params=buildMineICAParams(), A=data.frame(resJade$A), S=data.frame(resJade$S), dat=exprs(es), pData=pData(es), refSamples=character(0), annotation=annot, typeID= typeIDmainz, chipManu = "affymetrix", mart=mart) icaSet <- resBuild$icaSet } ## Build the two IcaSet objects icaSetMainz <- treat(mainz) icaSetVdx <- treat(vdx) ## compare the IcaSets runCompareIcaSets(icaSets=list(icaSetMainz, icaSetVdx), labAn=c("Mainz","Vdx"), type.corr="pearson", level="genes") ## End(Not run)
This function tests the enrichment of the components of
an IcaSet object using package
GOstats through function hyperGTest.
runEnrich(icaSet, params, dbs = c("KEGG", "GO"), ontos = c("BP", "CC", "MF"), cond = TRUE, hgCutoff = params["pvalCutoff"])runEnrich(icaSet, params, dbs = c("KEGG", "GO"), ontos = c("BP", "CC", "MF"), cond = TRUE, hgCutoff = params["pvalCutoff"])
icaSet |
An object of class
|
params |
An object of class
|
dbs |
The database to use, default is
|
ontos |
A string specifying the GO ontology to use.
Must be one of |
cond |
A logical indicating whether the calculation
should condition on the GO structure, see
|
hgCutoff |
The threshold p-value for statistical
significance, default is |
An annotation package should be available in
annotation(icaSet) to provide the contents of the
gene sets. If none corresponds to the technology you deal
with, please choose the org.*.eg.db package according to
the organism (for example org.Hs.eg.db for Homo sapiens).
By default, if annotation(icaSet) is empty and
organism is one of
c("Human","HomoSapiens","Mouse","Mus Musculus"),
then either org.Hs.eg.db or org.Mm.eg.db is
used.
Use of GOstats requires the input IDs to be Entrez
Gene, this function will therefore annotate either the
feature names or the gene names into Entrez Gene ID using
either the annotation package (annotation(icaSet))
or biomaRt.
Three types of enrichment tests are computed for each component: the threshold is first used to select gene based on their absolute projections, then positive and negative projections are treated individually.
For each database db (each ontology if db
is "GO"), this function writes an HTML file containing
the outputs of the enrichment tests computed through the
function hyperGTest. The
corresponding files are located in
resPath(icaSet)/GOstatsEnrichAnalysis/byDb/. The
results obtained for each database/ontology are then
merged into an array for each component, this array is
written as an HTML file in the directory
resPath(icaSet)/GOstatsEnrichmentAnalysis/ (this
directory is first deleted if it already exists). This
file is the one the user should look at.
The outputs of hyperGTest that
are given in each table are:
the database, the gene set ID, and the gene Set name
probability of observing the number of genes annotated for the gene set among the selected gene list, knowing the total number of annotated genes among the universe
,
expected number of genes in the selected gene list to be found at each tested category term/gene set,
odds ratio for each category term tested which is an indicator of the level of enrichment of genes within the list as against the universe,
number of genes in the selected gene list that are annotated for the gene set,
number of genes from the universe annotated for the gene set.
NULL
Anne Biton
buildIcaSet,
useMart,
hyperGTest,
GOHyperGParams,
hypergeoAn,
mergeGostatsResults
## Not run: # Load examples of IcaSet object data(icaSetCarbayo) ## Define parameters # Use threshold 3 to select contributing genes on which enrichment analysis will be applied # Results of enrichment analysis will be written in path 'resPath(params)/GOstatsEnrichAnalysis' params <- buildMineICAParams(resPath="carbayo/", selCutoff=3) ## Run enrichment analysis on the first two components contained in the icaSet object 'icaSetCarbayo' runEnrich(params=params,icaSet=icaSetCarbayo[,,1:2],dbs="GO", ontos="BP") ## End(Not run)## Not run: # Load examples of IcaSet object data(icaSetCarbayo) ## Define parameters # Use threshold 3 to select contributing genes on which enrichment analysis will be applied # Results of enrichment analysis will be written in path 'resPath(params)/GOstatsEnrichAnalysis' params <- buildMineICAParams(resPath="carbayo/", selCutoff=3) ## Run enrichment analysis on the first two components contained in the icaSet object 'icaSetCarbayo' runEnrich(params=params,icaSet=icaSetCarbayo[,,1:2],dbs="GO", ontos="BP") ## End(Not run)
This function performs ICA decomposition of a matrix
using functions fastICA and
JADE.
runICA(method = c("fastICA", "JADE"), X, nbComp, alg.type = c("deflation", "parallel"), fun = c("logcosh", "exp"), maxit = 500, tol = 10^-6, ...)runICA(method = c("fastICA", "JADE"), X, nbComp, alg.type = c("deflation", "parallel"), fun = c("logcosh", "exp"), maxit = 500, tol = 10^-6, ...)
method |
The ICA method to use, either "JADE" (the default) or "fastICA". |
X |
A data matrix with n rows representing observations (e.g genes) and p columns representing variables (e.g samples). |
nbComp |
The number of components to be extracted. |
alg.type |
If |
fun |
The functional form of the G function used in
the approximation to neg-entropy (see 'details' of the
help of function |
maxit |
The maximum number of iterations to perform. |
tol |
A positive scalar giving the tolerance at which the un-mixing matrix is considered to have converged. |
... |
Additional parameters for |
See details of the functions
fastICA and
JADE.
A list, see outputs of fastICA and
JADE. This list includes at least
three elements:
the estimated mixing matrix
the estimated source matrix
, itemWthe estimated unmixing matrix
Anne Biton
set.seed(2004); M <- matrix(rnorm(5000*6,sd=0.3),ncol=10) M[1:10,1:3] <- M[1:10,1:3] + 2 M[1:100,1:3] <- M[1:100,1:3] +1 resJade <- runICA(X=M, nbComp=2, method = "JADE", maxit=10000)set.seed(2004); M <- matrix(rnorm(5000*6,sd=0.3),ncol=10) M[1:10,1:3] <- M[1:10,1:3] + 2 M[1:100,1:3] <- M[1:100,1:3] +1 resJade <- runICA(X=M, nbComp=2, method = "JADE", maxit=10000)
This function selects elements whose absolute scaled values exceed a given threshold.
selectContrib(object, cutoff, level, ...)selectContrib(object, cutoff, level, ...)
object |
Either an |
cutoff |
The threshold according to which the elements will be selected. Must be either of length 1 and the same treshold is applied to all components, or of length equal to the number of components in order to use a specific threshold for each component. |
level |
The level of the selection: either |
... |
... |
Each vector is first scaled and then only elements with
an absolute scaled value higher than cutoff are
kept.
A list of projections restricted to the elements that are
higher than cutoff.
Anne Biton
## Not run: ## load an example of icaSet data(icaSetCarbayo) ##### ========= #### When arg 'object' is an IcaSet object ##### ========= ## select contributing genes selectContrib(object=icaSetCarbayo, cutoff=3, level="genes") ## select contributing features selectContrib(object=icaSetCarbayo, cutoff=3, level="features") ##### ========= #### When arg 'object' is a list ##### ========= c1 <- rnorm(100); names(c1) <- 100:199 c2 <- rnorm(100); names(c2) <- 1:99 selectContrib(object=list(c1,c2), cutoff= 0.5) ## select contributing features contribFlist <- selectContrib(Slist(icaSetCarbayo), 3) ## select contributing genes contribGlist <- selectContrib(SlistByGene(icaSetCarbayo), 3) ## End(Not run)## Not run: ## load an example of icaSet data(icaSetCarbayo) ##### ========= #### When arg 'object' is an IcaSet object ##### ========= ## select contributing genes selectContrib(object=icaSetCarbayo, cutoff=3, level="genes") ## select contributing features selectContrib(object=icaSetCarbayo, cutoff=3, level="features") ##### ========= #### When arg 'object' is a list ##### ========= c1 <- rnorm(100); names(c1) <- 100:199 c2 <- rnorm(100); names(c2) <- 1:99 selectContrib(object=list(c1,c2), cutoff= 0.5) ## select contributing features contribFlist <- selectContrib(Slist(icaSetCarbayo), 3) ## select contributing genes contribGlist <- selectContrib(SlistByGene(icaSetCarbayo), 3) ## End(Not run)
This function selects the features having the largest Inter Quartile Range (IQR).
selectFeatures_IQR(data, nb)selectFeatures_IQR(data, nb)
data |
Measured data of dimension features x samples (e.g, gene expression data) |
nb |
The number of features to be selected |
A subset of data restricted to the features having
the nb highest IQR value
Pierre Gestraud
dat <- matrix(rnorm(10000),ncol=10,nrow=1000) rownames(dat) <- 1:1000 selectFeatures_IQR(data=dat, nb=500)dat <- matrix(rnorm(10000),ncol=10,nrow=1000) rownames(dat) <- 1:1000 selectFeatures_IQR(data=dat, nb=500)
This function selects a gene per component.
selectWitnessGenes(icaSet, params, level = c("genes", "features"), maxNbOcc = 1, selectionByComp = NULL)selectWitnessGenes(icaSet, params, level = c("genes", "features"), maxNbOcc = 1, selectionByComp = NULL)
icaSet |
An object of class |
params |
An object of class
|
level |
The attribute of |
maxNbOcc |
The maximum number of components where
the genes can have an absolute projection value higher
than |
selectionByComp |
The list of components already restricted to the contributing genes |
Selects as feature/gene witness, for each component, the
first gene whose absolute projection is greater than a
given threshold in at the most maxNbOcc
components. These witnesses can then be used as
representatives of the expression behavior of the
contributing genes of the components.
When a feature/gene respecting the given constraints is
not found, maxNbOcc is incremented of one until a
gene is found.
This function returns a vector of IDs.
Anne Biton
## load an example of IcaSet data(icaSetCarbayo) ## define parameters: features or genes are considered to be contributor # when their absolute projection value exceeds a threshold of 4. params <- buildMineICAParams(resPath="carbayo/", selCutoff=4) ## selection, as gene witnesses, of the genes whose absolute projection is greater than 4 # in at the most one component. I.e, a gene is selected as a gene witness of a component # if he has a large projection on this component only. selectWitnessGenes(icaSet=icaSetCarbayo, params=params, level="genes", maxNbOcc=1) ## selection, as gene witnesses, of the genes whose absolute projection is greater than 4 # in at the most two components. # I.e, a gene is selected as a gene witness of a given component if he has a large projection # in this component and at the most another. selectWitnessGenes(icaSet=icaSetCarbayo, params=params, level="genes", maxNbOcc=2)## load an example of IcaSet data(icaSetCarbayo) ## define parameters: features or genes are considered to be contributor # when their absolute projection value exceeds a threshold of 4. params <- buildMineICAParams(resPath="carbayo/", selCutoff=4) ## selection, as gene witnesses, of the genes whose absolute projection is greater than 4 # in at the most one component. I.e, a gene is selected as a gene witness of a component # if he has a large projection on this component only. selectWitnessGenes(icaSet=icaSetCarbayo, params=params, level="genes", maxNbOcc=1) ## selection, as gene witnesses, of the genes whose absolute projection is greater than 4 # in at the most two components. # I.e, a gene is selected as a gene witness of a given component if he has a large projection # in this component and at the most another. selectWitnessGenes(icaSet=icaSetCarbayo, params=params, level="genes", maxNbOcc=2)
IcaSet object
as a list.These generic functions retrieve, from an IcaSet object,
the feature and gene projections contained in
the attribute S and SByGene as
a list where feature and gene IDs are preserved.
Slist(object) SlistByGene(object)Slist(object) SlistByGene(object)
object |
Object of class |
Slist and SlistByGene return a list whose length equals the number of
components contained in the IcaSet object. Each element of this
list contains a vector of feature or gene projections
indexed by the feature or gene IDs.
Anne Biton
class-IcaSet
biomaRt.This function annotates IDs (typically gene IDs) provided by the user and returns an html file with their description.
writeGenes(data, filename = NULL, mart = useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl"), typeId = "hgnc_symbol", typeRetrieved = NULL, sortBy = NULL, sortAbs = TRUE, colAnnot = NULL, decreasing = TRUE, highlight = NULL, caption = "")writeGenes(data, filename = NULL, mart = useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl"), typeId = "hgnc_symbol", typeRetrieved = NULL, sortBy = NULL, sortAbs = TRUE, colAnnot = NULL, decreasing = TRUE, highlight = NULL, caption = "")
data |
Either a data.frame whose rownames or one of its columns contain the IDs to be annotated, or a vector of IDs. |
filename |
The name of the HTML file where gene annotations are written. |
mart |
Output of function |
typeId |
The type of IDs available in |
typeRetrieved |
The descriptors uses to annotate the
features of |
sortBy |
Name of a column of |
sortAbs |
If TRUE absolute value of column
|
colAnnot |
The column containing the IDs to be
annotated, if NULL or missing and argument |
decreasing |
If TRUE, the output is sorted by
decreasing values of the |
highlight |
IDs to be displayed in colour red in the returned table |
caption |
A title for the HTML table |
"hgnc_symbol", "ensembl_gene_id", "description",
"chromosome_name", "start_position", "end_position",
"band", and "strand", are automatically added to
the list of fields available in argument
typeRetrieved queried on biomaRt. The web-links to
www.genecards.org and www.proteinatlas.org are
automatically added in the columns of the output
respectively corresponding to hgnc_symbol and
ensembl_gene_id.
This function returns a data.frame which contains annotations of the input data.
Anne Biton
getBM,
listFilters,
listAttributes,
useMart
if (interactive()) { ## define the database to be used mart <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl") ### Describe: ## a set of hgnc symbols with default descriptions (typeRetrieved=NULL) genes <- c("TOP2A","E2F3","E2F1","CDK1","CDC20","MKI67") writeGenes(data=genes, filename="foo", mart=mart, typeId = "hgnc_symbol") ## a data.frame indexed by hngc symbols, sort output according to column "values", add a title to the HTML output datagenes <- data.frame(values=rnorm(6),row.names = genes) writeGenes(data=datagenes, filename="foo", sortBy = "values", caption = "Description of some proliferation genes.") ## a set of Entrez Gene IDs with default descriptions genes <- c("7153","1871","1869","983","991","4288") writeGenes(data=genes, filename="foo", mart=mart, typeId = "entrezgene") } ## Not run: ## add the GO category the genes belong to ## search in listAttributes(mart)[,1] which filter correspond to the Gene Ontology -> "go_id" writeGenes(data=genes, filename="foo", mart=mart, typeId = "entrezgene", typeRetrieved = "go_id") ## End(Not run)if (interactive()) { ## define the database to be used mart <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl") ### Describe: ## a set of hgnc symbols with default descriptions (typeRetrieved=NULL) genes <- c("TOP2A","E2F3","E2F1","CDK1","CDC20","MKI67") writeGenes(data=genes, filename="foo", mart=mart, typeId = "hgnc_symbol") ## a data.frame indexed by hngc symbols, sort output according to column "values", add a title to the HTML output datagenes <- data.frame(values=rnorm(6),row.names = genes) writeGenes(data=datagenes, filename="foo", sortBy = "values", caption = "Description of some proliferation genes.") ## a set of Entrez Gene IDs with default descriptions genes <- c("7153","1871","1869","983","991","4288") writeGenes(data=genes, filename="foo", mart=mart, typeId = "entrezgene") } ## Not run: ## add the GO category the genes belong to ## search in listAttributes(mart)[,1] which filter correspond to the Gene Ontology -> "go_id" writeGenes(data=genes, filename="foo", mart=mart, typeId = "entrezgene", typeRetrieved = "go_id") ## End(Not run)
This function writes in an html file the description of the features, or genes, that contribute to each component. It also writes an html file containing, for each feature or gene, its projection value on every component.
writeProjByComp(icaSet, params, mart = useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl"), typeRetrieved = NULL, addNbOcc = TRUE, selectionByComp = NULL, level = c("features", "genes"), typeId, selCutoffWrite=2.5)writeProjByComp(icaSet, params, mart = useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl"), typeRetrieved = NULL, addNbOcc = TRUE, selectionByComp = NULL, level = c("features", "genes"), typeId, selCutoffWrite=2.5)
icaSet |
An object of class |
params |
An object of class |
mart |
An output of function |
typeRetrieved |
The annotations biomaRt is queried about. They
describe the feature or gene IDs of the argument |
addNbOcc |
If TRUE, the number of components the features/genes
contribute to is added to the output. A gene/feature is considered
as a contributor of a component if its absolute scaled projection
value is higher than |
selectionByComp |
A list containing the feature/gene projections on each component, already restricted to the ones considered as contributors. |
level |
The data level of |
typeId |
The type of ID the features or the genes of
|
selCutoffWrite |
The cutoff applied to the absolute projection values to select the features/genes that will be annotated using package |
One file is created by component, each file is named by the
index of the components (indComp(icaSet)) and located in the
path genePath(params).
In case you are interested in writing the description of features and their annotations, please remember to modify codegenesPath(params), or the previous files will be overwritten.
The genes are ranked according to their absolute projection values.
This function also writes an html file named "genes2comp"
providing, for each feature or gene, the number of components it
contributes to (according to the threshold cutoffSel(params)),
and its projection value on all the components.
The projection values are scaled.
See function writeGenes for details.
This function returns a list of two elements:
a list with the output of
writeGenes for each component
a data.frame storing the projection values of each feature/gene (row) across all the components (columns).
Anne Biton
writeGenes, getBM, listFilters, listAttributes, useMart, selectContrib, nbOccInComp
## Not run: ## load IcaSet object ## We will use 'icaSetCarbayo', whose features are hgu133a probe sets ## and feature annotations are Gene Symbols. data(icaSetCarbayo) ## define database to be used by biomaRt mart <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl") ## define the parameters of the analysis params <- buildMineICAParams(resPath="~/resMineICACarbayo/", selCutoff=0) ## Make sure the elements "_biomaRt" of attribute 'typeID' are defined typeID(icaSetCarbayo) ### Query biomaRt and write gene descriptions in HTML files ### The files will be located in the directory 'genesPath(params)' ## 1. Write description of genes res <- writeProjByComp(icaSet=icaSetCarbayo, params=params, mart=mart, level="genes") #, typeId="hgnc_symbol") ## 2. Write description of features # change attribute 'genesPath' of params to preserve the gene descriptions genesPath(params) <- paste(resPath(params),"comp2features/",sep="") res <- writeProjByComp(icaSet=icaSetCarbayo, params=params, mart=mart, level="features") #, typeId="affy_hg_u133a") ## End(Not run)## Not run: ## load IcaSet object ## We will use 'icaSetCarbayo', whose features are hgu133a probe sets ## and feature annotations are Gene Symbols. data(icaSetCarbayo) ## define database to be used by biomaRt mart <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl") ## define the parameters of the analysis params <- buildMineICAParams(resPath="~/resMineICACarbayo/", selCutoff=0) ## Make sure the elements "_biomaRt" of attribute 'typeID' are defined typeID(icaSetCarbayo) ### Query biomaRt and write gene descriptions in HTML files ### The files will be located in the directory 'genesPath(params)' ## 1. Write description of genes res <- writeProjByComp(icaSet=icaSetCarbayo, params=params, mart=mart, level="genes") #, typeId="hgnc_symbol") ## 2. Write description of features # change attribute 'genesPath' of params to preserve the gene descriptions genesPath(params) <- paste(resPath(params),"comp2features/",sep="") res <- writeProjByComp(icaSet=icaSetCarbayo, params=params, mart=mart, level="features") #, typeId="affy_hg_u133a") ## End(Not run)
Writes the gene projection values of each component in a '.rnk' file for GSEA.
writeRnkFiles(icaSet, abs = TRUE, path)writeRnkFiles(icaSet, abs = TRUE, path)
icaSet |
An object of class |
abs |
If TRUE (default) the absolute projection values are used. |
path |
The path that will contain the rnk files. |
The .rnk format requires two columns, the first
containing the gene IDs, the second containing the
projection values. The genes are ordered by projection
values. The files are named "index-of-component_abs.rnk"
if abs=TRUE, or "index-of-component.rnk" if
abs=FALSE.
NULL
Anne