| Title: | Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation. |
|---|---|
| Description: | Tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided. |
| Authors: | Evarist Planet |
| Maintainer: | Evarist Planet <[email protected]> |
| License: | GPL (>=2) |
| Version: | 1.53.0 |
| Built: | 2024-09-22 05:04:23 UTC |
| Source: | https://github.com/bioc/phenoTest |
Test correlation between gene expression and phenotype.
| Package: | phenoTest |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2010-04-28 |
| License: | What license is it under? |
| LazyLoad: | yes |
Evarist Planet Maintainer: Evarist Planet <[email protected]>
Summarizes the univariate relationships between genes in one or more
signatures and several phenotype variables, as summarized in
epheno objects (which can be created with the
ExpressionPhenoTest function).
By default barplotSignifSignatures performs a binomial test
(binom.test from package stats) for each signature
to see if the number of up up regulated and down regulated genes is
different enough to be statistically different.
When a reference gene set is provided we test if the proportions of up
and down regulated genes of each gene set is different from the
proportions in the reference gene set. This has been done with a
chi-square test.
When a reference gene set is provided and parameter
testUpDown is TRUE (by default its FALSE)
the number of genes corresponding to up and down regulated are
compared with those of the reference gene set separately.
barplotSignatures(x, signatures, referenceSignature, alpha=.05, p.adjust.method='none', ylab, cex.text=1, ...) barplotSignifSignatures(x, signatures, referenceSignature, testUpDown=FALSE, simulate.p.value = FALSE, B = 10^4, p.adjust.method='none', alpha=.05, ylab, ylim=ylim, cex.text=1, ...)barplotSignatures(x, signatures, referenceSignature, alpha=.05, p.adjust.method='none', ylab, cex.text=1, ...) barplotSignifSignatures(x, signatures, referenceSignature, testUpDown=FALSE, simulate.p.value = FALSE, B = 10^4, p.adjust.method='none', alpha=.05, ylab, ylim=ylim, cex.text=1, ...)
x |
|
signatures |
List with each element corresponding to a
signature. The gene names in each signature must match those in
|
referenceSignature |
If specified, the average fold change in each
signature is compared to the average fold change in the signature
|
testUpDown |
If set to |
cex.text |
Character expansion for the text indicating the P-values. Ignored if referenceSignature is missing. |
alpha |
Confidence levels for barplot error bars. |
p.adjust.method |
P-value adjustment method, passed on to
|
simulate.p.value |
A logical indicating whether chi-square p-values
should be computed by Monte Carlo simulation (passed on to |
B |
Integer specifying the number of replicates in the Monte Carlo
simulation (passed on to |
ylab |
y-axis labels |
ylim |
y-axis limits |
... |
Other arguments to be passed on to boxplot. |
When a single signature is provided as input, a single plot assessing the association of that signature with all phenotype variables is created. If several signatures are provided, one separate plot is created for each phenotype variable.
Evarist Planet
#create epheno data(epheno) #construct two signatures sign1 <- sample(featureNames(epheno))[1:20] sign2 <- sample(featureNames(epheno))[1:15] mySignature <- list(sign1,sign2) names(mySignature) <- c('My first signature','My preferred signature') #plot barplotSignifSignatures(epheno[,'Relapse'],mySignature,alpha=0.05)#create epheno data(epheno) #construct two signatures sign1 <- sample(featureNames(epheno))[1:20] sign2 <- sample(featureNames(epheno))[1:15] mySignature <- list(sign1,sign2) names(mySignature) <- c('My first signature','My preferred signature') #plot barplotSignifSignatures(epheno[,'Relapse'],mySignature,alpha=0.05)
Methods for function barplotSignatures in Package
‘phenoTest’. For more information read the function's manual.
signature(x = "epheno", signatures = "character")Method for an epheno object and one signature stored in an object
of class character.
signature(x = "epheno", signatures = "GeneSetCollection")Method for an epheno object and several signatures stored in an
object of class GeneSetCollection.
signature(x = "epheno", signatures = "GeneSet")Method for an epheno object and one signature stored in an object
of class GeneSet.
signature(x = "epheno", signatures = "list")Method for an epheno object and several signatures stored in an
object of class list.
Methods for function barplotSignifSignatures in Package
‘phenoTest’. For more information read the function's manual.
signature(x = "epheno", signatures = "character")Method for an epheno object and one signature stored in an object
of class character.
signature(x = "epheno", signatures = "list")Method for an epheno object and several signatures stored in an
object of class list.
signature(x = "epheno", signatures = "GeneSet")Method for an epheno object and one signature stored in an object
of class GeneSet.
signature(x = "epheno", signatures = "GeneSetCollection")Method for an epheno object and several signatures stored in an
object of class GeneSetCollection.
Test the associations between clusters that each sample belongs to (based on gene expression) and each phenotype.
ClusterPhenoTest(x,cluster,vars2test,B=10^4,p.adjust.method='none')ClusterPhenoTest(x,cluster,vars2test,B=10^4,p.adjust.method='none')
x |
ExpressionSet with phenotype information stored in |
cluster |
variable of class |
vars2test |
list with components 'continuous', 'categorical',
'ordinal' and 'survival' indicating which phenotype variables should
be tested. 'continuous', 'categorical' and 'ordinal' must be character
vectors, 'survival' a matrix with columns named 'time' and
'event'. The names must match names in |
B |
An integer specifying the number of replicates used in the
chi-square Monte Carlo test (passed on to |
p.adjust.method |
Method for P-value adjustment, passed on to
|
Test association between the provided clusters and each phenotype.
For variables in vars2test\$continuous and vars2test\$ordinal a
Kruskal-Wallis Rank Sum test is used; for vars2test\$categorical a
chi-square test (with exact p-value if simulate.p.value is set to
TRUE); for var2test\$survival a Cox proportional hazards
likelihood-ratio test.
David Rossell
#load data data(eset) eset #construct vars2test survival <- matrix(c("Relapse","Months2Relapse"),ncol=2,byrow=TRUE) colnames(survival) <- c('event','time') #add positive to have more than one category pData(eset)[1:20,'lymph.node.status'] <- 'positive' vars2test <- list(survival=survival,categorical='lymph.node.status') vars2test #first half of the samples will be one cluster and the rest the other cluster cluster <- c(rep('Cluster1',floor(ncol(eset)/2)),rep('Cluster2',ncol(eset)-floor(ncol(eset)/2))) #test association ClusterPhenoTest(eset,cluster,vars2test=vars2test)#load data data(eset) eset #construct vars2test survival <- matrix(c("Relapse","Months2Relapse"),ncol=2,byrow=TRUE) colnames(survival) <- c('event','time') #add positive to have more than one category pData(eset)[1:20,'lymph.node.status'] <- 'positive' vars2test <- list(survival=survival,categorical='lymph.node.status') vars2test #first half of the samples will be one cluster and the rest the other cluster cluster <- c(rep('Cluster1',floor(ncol(eset)/2)),rep('Cluster2',ncol(eset)-floor(ncol(eset)/2))) #test association ClusterPhenoTest(eset,cluster,vars2test=vars2test)
Object obatined with ExpressionPhenoTest function.
data(epheno)data(epheno)
The format is: Formal class 'epheno' [package "phenoTest"] with 8 slots ..@ p.adjust.method : chr "none" ..@ assayData :<environment: 0x1050d5a78> ..@ phenoData :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots .. .. ..@ varMetadata :'data.frame': 5 obs. of 1 variable: .. .. .. ..$ labelDescription: chr [1:5] NA NA NA NA ... .. .. ..@ data :'data.frame': 12 obs. of 5 variables: .. .. .. ..$ phenoName : Factor w/ 3 levels "lymph.node.status",..: 3 3 3 3 3 1 1 1 2 3 ... .. .. .. ..$ phenoClass: Factor w/ 3 levels "categorical",..: 2 2 2 2 2 1 1 1 3 2 ... .. .. .. ..$ phenoType : Factor w/ 3 levels "mean","pval",..: 1 1 1 3 3 1 1 3 3 2 ... .. .. .. ..$ meanLabel : Factor w/ 5 levels "[45.2,49.2)",..: 1 2 3 NA NA 4 5 NA NA NA ... .. .. .. ..$ survTime : Factor w/ 1 level "Months2Relapse": NA NA NA NA NA NA NA NA 1 NA ... .. .. ..@ dimLabels : chr [1:2] "sampleNames" "sampleColumns" .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. .. .. ..@ .Data:List of 1 .. .. .. .. .. ..$ : int [1:3] 1 1 0 ..@ featureData :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots .. .. ..@ varMetadata :'data.frame': 0 obs. of 1 variable: .. .. .. ..$ labelDescription: chr(0) .. .. ..@ data :'data.frame': 1000 obs. of 0 variables .. .. ..@ dimLabels : chr [1:2] "featureNames" "featureColumns" .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. .. .. ..@ .Data:List of 1 .. .. .. .. .. ..$ : int [1:3] 1 1 0 ..@ experimentData :Formal class 'MIAME' [package "Biobase"] with 13 slots .. .. ..@ name : chr "" .. .. ..@ lab : chr "" .. .. ..@ contact : chr "" .. .. ..@ title : chr "" .. .. ..@ abstract : chr "" .. .. ..@ url : chr "" .. .. ..@ pubMedIds : chr "" .. .. ..@ samples : list() .. .. ..@ hybridizations : list() .. .. ..@ normControls : list() .. .. ..@ preprocessing : list() .. .. ..@ other : list() .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. .. .. ..@ .Data:List of 1 .. .. .. .. .. ..$ : int [1:3] 1 0 0 ..@ annotation : chr "hgu133a" ..@ protocolData :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots .. .. ..@ varMetadata :'data.frame': 0 obs. of 1 variable: .. .. .. ..$ labelDescription: chr(0) .. .. ..@ data :'data.frame': 12 obs. of 0 variables .. .. ..@ dimLabels : chr [1:2] "sampleNames" "sampleColumns" .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. .. .. ..@ .Data:List of 1 .. .. .. .. .. ..$ : int [1:3] 1 1 0 ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. ..@ .Data:List of 4 .. .. .. ..$ : int [1:3] 2 12 0 .. .. .. ..$ : int [1:3] 2 10 0 .. .. .. ..$ : int [1:3] 1 3 0 .. .. .. ..$ : int [1:3] 1 0 0
data(epheno) ## maybe str(epheno) ; plot(epheno) ...data(epheno) ## maybe str(epheno) ; plot(epheno) ...
Object obtained with the ExpressionPhenoTest function. Contains FC, HR and pvals from testing expression values of each gene against phenotypic variables.
Objects can be created by calls of the form new("epheno",
assayData, phenoData, featureData, exprs, ...).
p.adjust.method:Object of class "character"
containing the multiple testing adjustment method used (if one was
used).
approach:Object of class "character"
containing 'frequentist' or 'bayesian' depending on the user's
selecion.
assayData:Object of class "AssayData" that is
inherited from the ExpressionSet object used to create the
epheno object.
phenoData:Object of class "AnnotatedDataFrame"
that contains information about the variables stored in the
experimentData slot such as their class (continuous, categorical,
etc) or type (mean, summaryDif, pval, etc).
featureData:Object of class "AnnotatedDataFrame" that is
inherited from the ExpressionSet object used to create the
epheno object.
experimentData:Object of class "MIAME" that is
inherited from the ExpressionSet object used to create the
epheno object.
annotation:Object of class "character" that is
inherited from the ExpressionSet object used to create the
epheno object.
protocolData:Object of class "AnnotatedDataFrame" that is
inherited from the ExpressionSet object used to create the
epheno object.
.__classVersion__:Object of class "Versions" that is
inherited from the ExpressionSet object used to create the
epheno object.
Class "ExpressionSet", directly.
Class "eSet", by class "ExpressionSet", distance 2.
Class "VersionedBiobase", by class "ExpressionSet", distance 3.
Class "Versioned", by class "ExpressionSet", distance 4.
signature(x = "epheno", i = "ANY", j = "ANY"):
inherited from the ExpressionSet class.
signature(x = "epheno"): inherited from the
ExpressionSet class.
signature(x = "epheno"): ...
signature(x = "epheno"): getter for the fold
changes.
signature(x = "epheno"): getter for the hazard
ratios.
signature(x = "epheno"): getter for the means.
signature(x = "epheno"): getter for the
pvalues or posterior probabilities.
signature(x = "epheno"): getter for the
pvalues.
signature(x = "epheno"): getter for the
posterior probabilities.
signature(x = "epheno"): getter that
returns hazard ratios, fold changes and pvalues.
signature(x = "epheno", signatures =
"list"): Used to compute GSEA. Please read the gseaSignatures
manual.
signature(x = "epheno"): getter for the log of
fold changes and hazard ratios.
signature(x = "epheno"): getter for
the p value adjustment method that has been used.
signature(x = "epheno"): Returns the class
off all variables.
signature(x = "epheno"): Returns the names
of the tested phenotypes.
signature(object = "epheno"): Shows a brief
overview of the object.
Evarist Planet
showClass("epheno")showClass("epheno")
Creates html files and plots using an epheno object, which stores the association between a list of variables and gene expression.
epheno2html(x, epheno, outputdir, prefix = "", genelimit = 50, categories = 3, withPlots = TRUE, mc.cores = 1)epheno2html(x, epheno, outputdir, prefix = "", genelimit = 50, categories = 3, withPlots = TRUE, mc.cores = 1)
x |
An object of class |
epheno |
an object produced by ExpressionPhenoTest. this object will contain univariate association between a list of phenotype variables and gene expression as weel as p-values. |
outputdir |
where to place files. |
prefix |
will be used to add a text to the beginning of the files that will be created. |
genelimit |
maximum number of genes on the list. |
categories |
Number of categories used for continuous variables. It has to be the same as the one used for ExpressionPhenoTest. |
withPlots |
when FALSE no plots will be produced. Makes the process faster. |
mc.cores |
number of cores that will be used to run the process. |
Evarist Planet
#Example on building homology tables for human. #mart <- useMart("ensembl", "hsapiens_gene_ensembl") #homol.symbol <- getLDS(attributes = c("entrezgene"), # mart = mart, attributesL = c("external_gene_id"), # martL = mart, filters = "entrezgene", values = entrezid) #mart <- useMart("ensembl", "hsapiens_gene_ensembl") #homol.genename <- getLDS(attributes = c("entrezgene"), # mart = mart, attributesL = c("description"), martL = mart, # filters = "entrezgene", values = entrezid)#Example on building homology tables for human. #mart <- useMart("ensembl", "hsapiens_gene_ensembl") #homol.symbol <- getLDS(attributes = c("entrezgene"), # mart = mart, attributesL = c("external_gene_id"), # martL = mart, filters = "entrezgene", values = entrezid) #mart <- useMart("ensembl", "hsapiens_gene_ensembl") #homol.genename <- getLDS(attributes = c("entrezgene"), # mart = mart, attributesL = c("description"), martL = mart, # filters = "entrezgene", values = entrezid)
Example data of class ExpressionSet.
data(eset)data(eset)
The format is: Formal class 'ExpressionSet' [package "Biobase"] with 7 slots ..@ assayData :<environment: 0x1050d9390> ..@ phenoData :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots .. .. ..@ varMetadata :'data.frame': 7 obs. of 1 variable: .. .. .. ..$ labelDescription: chr [1:7] NA NA NA NA ... .. .. ..@ data :'data.frame': 286 obs. of 7 variables: .. .. .. ..$ PID : int [1:286] 3 5 6 7 8 9 11 14 15 17 ... .. .. .. ..$ GEOaccession : Factor w/ 286 levels "GSM36777","GSM36778",..: 17 20 21 22 24 25 58 59 60 61 ... .. .. .. ..$ lymph.node.status: chr [1:286] "negative" "negative" "negative" "negative" ... .. .. .. ..$ Months2Relapse : int [1:286] 101 118 9 106 37 125 109 14 99 137 ... .. .. .. ..$ Relapse : int [1:286] 0 0 1 0 1 0 0 1 0 0 ... .. .. .. ..$ ER.Status : num [1:286] 0 1 0 0 0 1 1 0 1 1 ... .. .. .. ..$ BrainRelapse : int [1:286] 0 0 0 0 0 0 0 0 0 0 ... .. .. ..@ dimLabels : chr [1:2] "sampleNames" "sampleColumns" .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. .. .. ..@ .Data:List of 1 .. .. .. .. .. ..$ : int [1:3] 1 1 0 ..@ featureData :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots .. .. ..@ varMetadata :'data.frame': 16 obs. of 3 variables: .. .. .. ..$ Column : chr [1:16] "ID" "GB_ACC" "SPOT_ID" "Species Scientific Name" ... .. .. .. ..$ Description : Factor w/ 15 levels "","A gene symbol, when one is available (from UniGene).",..: 3 5 15 13 12 1 11 1 10 14 ... .. .. .. ..$ labelDescription: chr [1:16] NA NA NA NA ... .. .. ..@ data :'data.frame': 1000 obs. of 16 variables: .. .. .. ..$ ID : Factor w/ 22284 levels "1007_s_at","1053_at",..: 1 2 3 4 5 6 7 8 9 10 ... .. .. .. ..$ GB_ACC : Factor w/ 21129 levels "AF052179","AF061832",..: 93 30 95 97 25 24 96 99 28 20 ... .. .. .. ..$ SPOT_ID : chr [1:1000] NA NA NA NA ... .. .. .. ..$ Species.Scientific.Name : Factor w/ 2 levels "Homo sapiens",..: 1 1 1 1 1 1 1 1 1 1 ... .. .. .. ..$ Annotation.Date : Factor w/ 2 levels "Jul 11, 2007",..: 1 1 1 1 1 1 1 1 1 1 ... .. .. .. ..$ Sequence.Type : Factor w/ 4 levels "Consensus sequence",..: 2 2 2 2 2 2 2 2 2 2 ... .. .. .. ..$ Sequence.Source : Factor w/ 3 levels "Affymetrix Proprietary Database",..: 1 2 1 2 1 2 1 1 2 1 ... .. .. .. ..$ Target.Description : Factor w/ 21363 levels "Consensus includes gb:AI656011 /FEA=EST /DB_XREF=gi:4739990 /DB_XREF=est:tt42e08.x1 /CLONE=IMAGE:2243462 /UG=Hs.116875 KIAA0156"| __truncated__,..: 16 13 18 20 8 7 19 22 11 4 ... .. .. .. ..$ Representative.Public.ID : Factor w/ 21197 levels "AF052179","AF061832",..: 93 30 95 97 25 24 96 99 28 20 ... .. .. .. ..$ Gene.Title : Factor w/ 14208 levels "ADP-ribosylation factor 1",..: 35 66 46 60 44 97 96 64 26 33 ... .. .. .. ..$ Gene.Symbol : Factor w/ 13293 levels "ABCF1","ARF1",..: 20 59 40 53 33 96 94 58 15 18 ... .. .. .. ..$ ENTREZ_GENE_ID : chr [1:1000] "780" "5982" "3310" "7849" ... .. .. .. ..$ RefSeq.Transcript.ID : Factor w/ 13074 levels "NM_000409","NM_000661 /// NM_001024921",..: 37 45 41 52 1 50 49 82 47 4 ... .. .. .. ..$ Gene.Ontology.Biological.Process: Factor w/ 7245 levels "","0000074 // regulation of progression through cell cycle // traceable author statement /// 0006139 // nucleobase, nucleoside, nu"| __truncated__,..: 61 22 78 32 79 60 14 63 72 20 ... .. .. .. ..$ Gene.Ontology.Cellular.Component: Factor w/ 4148 levels "","0000502 // proteasome complex (sensu Eukaryota) // traceable author statement /// 0005634 // nucleus // inferred from electroni"| __truncated__,..: 72 45 1 44 1 1 42 71 6 68 ... .. .. .. ..$ Gene.Ontology.Molecular.Function: Factor w/ 7314 levels "","0000049 // tRNA binding // non-traceable author statement /// 0000166 // nucleotide binding // inferred from electronic annotat"| __truncated__,..: 23 26 27 40 81 18 39 71 74 69 ... .. .. ..@ dimLabels : chr [1:2] "featureNames" "featureColumns" .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. .. .. ..@ .Data:List of 1 .. .. .. .. .. ..$ : int [1:3] 1 1 0 ..@ experimentData :Formal class 'MIAME' [package "Biobase"] with 13 slots .. .. ..@ name : chr "" .. .. ..@ lab : chr "" .. .. ..@ contact : chr "" .. .. ..@ title : chr "" .. .. ..@ abstract : chr "" .. .. ..@ url : chr "" .. .. ..@ pubMedIds : chr "" .. .. ..@ samples : list() .. .. ..@ hybridizations : list() .. .. ..@ normControls : list() .. .. ..@ preprocessing : list() .. .. ..@ other : list() .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. .. .. ..@ .Data:List of 1 .. .. .. .. .. ..$ : int [1:3] 1 0 0 ..@ annotation : chr "hgu133a" ..@ protocolData :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots .. .. ..@ varMetadata :'data.frame': 0 obs. of 1 variable: .. .. .. ..$ labelDescription: chr(0) .. .. ..@ data :'data.frame': 286 obs. of 0 variables .. .. ..@ dimLabels : chr [1:2] "sampleNames" "sampleColumns" .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. .. .. ..@ .Data:List of 1 .. .. .. .. .. ..$ : int [1:3] 1 1 0 ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. ..@ .Data:List of 4 .. .. .. ..$ : int [1:3] 2 12 0 .. .. .. ..$ : int [1:3] 2 10 0 .. .. .. ..$ : int [1:3] 1 3 0 .. .. .. ..$ : int [1:3] 1 0 0
Has been obtained from GEO (GSE2034). Only first 1000 probesets where stored (the rest has been removed).
data(eset) ## maybe str(eset) ; plot(eset) ...data(eset) ## maybe str(eset) ; plot(eset) ...
Example data of class ExpressionSet with one probeset per gene.
data(eset.genelevel)data(eset.genelevel)
The format is: Formal class 'ExpressionSet' [package "Biobase"] with 7 slots ..@ assayData :<environment: 0x1050d9390> ..@ phenoData :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots .. .. ..@ varMetadata :'data.frame': 7 obs. of 1 variable: .. .. .. ..$ labelDescription: chr [1:7] NA NA NA NA ... .. .. ..@ data :'data.frame': 286 obs. of 7 variables: .. .. .. ..$ PID : int [1:286] 3 5 6 7 8 9 11 14 15 17 ... .. .. .. ..$ GEOaccession : Factor w/ 286 levels "GSM36777","GSM36778",..: 17 20 21 22 24 25 58 59 60 61 ... .. .. .. ..$ lymph.node.status: chr [1:286] "negative" "negative" "negative" "negative" ... .. .. .. ..$ Months2Relapse : int [1:286] 101 118 9 106 37 125 109 14 99 137 ... .. .. .. ..$ Relapse : int [1:286] 0 0 1 0 1 0 0 1 0 0 ... .. .. .. ..$ ER.Status : num [1:286] 0 1 0 0 0 1 1 0 1 1 ... .. .. .. ..$ BrainRelapse : int [1:286] 0 0 0 0 0 0 0 0 0 0 ... .. .. ..@ dimLabels : chr [1:2] "sampleNames" "sampleColumns" .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. .. .. ..@ .Data:List of 1 .. .. .. .. .. ..$ : int [1:3] 1 1 0 ..@ featureData :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots .. .. ..@ varMetadata :'data.frame': 16 obs. of 3 variables: .. .. .. ..$ Column : chr [1:16] "ID" "GB_ACC" "SPOT_ID" "Species Scientific Name" ... .. .. .. ..$ Description : Factor w/ 15 levels "","A gene symbol, when one is available (from UniGene).",..: 3 5 15 13 12 1 11 1 10 14 ... .. .. .. ..$ labelDescription: chr [1:16] NA NA NA NA ... .. .. ..@ data :'data.frame': 1000 obs. of 16 variables: .. .. .. ..$ ID : Factor w/ 22284 levels "1007_s_at","1053_at",..: 1 2 3 4 5 6 7 8 9 10 ... .. .. .. ..$ GB_ACC : Factor w/ 21129 levels "AF052179","AF061832",..: 93 30 95 97 25 24 96 99 28 20 ... .. .. .. ..$ SPOT_ID : chr [1:1000] NA NA NA NA ... .. .. .. ..$ Species.Scientific.Name : Factor w/ 2 levels "Homo sapiens",..: 1 1 1 1 1 1 1 1 1 1 ... .. .. .. ..$ Annotation.Date : Factor w/ 2 levels "Jul 11, 2007",..: 1 1 1 1 1 1 1 1 1 1 ... .. .. .. ..$ Sequence.Type : Factor w/ 4 levels "Consensus sequence",..: 2 2 2 2 2 2 2 2 2 2 ... .. .. .. ..$ Sequence.Source : Factor w/ 3 levels "Affymetrix Proprietary Database",..: 1 2 1 2 1 2 1 1 2 1 ... .. .. .. ..$ Target.Description : Factor w/ 21363 levels "Consensus includes gb:AI656011 /FEA=EST /DB_XREF=gi:4739990 /DB_XREF=est:tt42e08.x1 /CLONE=IMAGE:2243462 /UG=Hs.116875 KIAA0156"| __truncated__,..: 16 13 18 20 8 7 19 22 11 4 ... .. .. .. ..$ Representative.Public.ID : Factor w/ 21197 levels "AF052179","AF061832",..: 93 30 95 97 25 24 96 99 28 20 ... .. .. .. ..$ Gene.Title : Factor w/ 14208 levels "ADP-ribosylation factor 1",..: 35 66 46 60 44 97 96 64 26 33 ... .. .. .. ..$ Gene.Symbol : Factor w/ 13293 levels "ABCF1","ARF1",..: 20 59 40 53 33 96 94 58 15 18 ... .. .. .. ..$ ENTREZ_GENE_ID : chr [1:1000] "780" "5982" "3310" "7849" ... .. .. .. ..$ RefSeq.Transcript.ID : Factor w/ 13074 levels "NM_000409","NM_000661 /// NM_001024921",..: 37 45 41 52 1 50 49 82 47 4 ... .. .. .. ..$ Gene.Ontology.Biological.Process: Factor w/ 7245 levels "","0000074 // regulation of progression through cell cycle // traceable author statement /// 0006139 // nucleobase, nucleoside, nu"| __truncated__,..: 61 22 78 32 79 60 14 63 72 20 ... .. .. .. ..$ Gene.Ontology.Cellular.Component: Factor w/ 4148 levels "","0000502 // proteasome complex (sensu Eukaryota) // traceable author statement /// 0005634 // nucleus // inferred from electroni"| __truncated__,..: 72 45 1 44 1 1 42 71 6 68 ... .. .. .. ..$ Gene.Ontology.Molecular.Function: Factor w/ 7314 levels "","0000049 // tRNA binding // non-traceable author statement /// 0000166 // nucleotide binding // inferred from electronic annotat"| __truncated__,..: 23 26 27 40 81 18 39 71 74 69 ... .. .. ..@ dimLabels : chr [1:2] "featureNames" "featureColumns" .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. .. .. ..@ .Data:List of 1 .. .. .. .. .. ..$ : int [1:3] 1 1 0 ..@ experimentData :Formal class 'MIAME' [package "Biobase"] with 13 slots .. .. ..@ name : chr "" .. .. ..@ lab : chr "" .. .. ..@ contact : chr "" .. .. ..@ title : chr "" .. .. ..@ abstract : chr "" .. .. ..@ url : chr "" .. .. ..@ pubMedIds : chr "" .. .. ..@ samples : list() .. .. ..@ hybridizations : list() .. .. ..@ normControls : list() .. .. ..@ preprocessing : list() .. .. ..@ other : list() .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. .. .. ..@ .Data:List of 1 .. .. .. .. .. ..$ : int [1:3] 1 0 0 ..@ annotation : chr "hgu133a" ..@ protocolData :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots .. .. ..@ varMetadata :'data.frame': 0 obs. of 1 variable: .. .. .. ..$ labelDescription: chr(0) .. .. ..@ data :'data.frame': 286 obs. of 0 variables .. .. ..@ dimLabels : chr [1:2] "sampleNames" "sampleColumns" .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. .. .. ..@ .Data:List of 1 .. .. .. .. .. ..$ : int [1:3] 1 1 0 ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slots .. .. ..@ .Data:List of 4 .. .. .. ..$ : int [1:3] 2 12 0 .. .. .. ..$ : int [1:3] 2 10 0 .. .. .. ..$ : int [1:3] 1 3 0 .. .. .. ..$ : int [1:3] 1 0 0
Has been obtained from GEO (GSE2034). Only first 1000 probesets where stored (the rest has been removed). After that the expressionSet was filtered to keep only one probeset per gene. We used the nsFilter function from package genefilter to accomplish this task.
data(eset.genelevel) ## maybe str(eset.genelevel) ; plot(eset.genelevel) ...data(eset.genelevel) ## maybe str(eset.genelevel) ; plot(eset.genelevel) ...
Only one probeset per gene will be kept and entrezid will be used as
gene identifier.
nsFilter from package genefilter is used to select the
probeset.
The selected probeset is the one with higher interquartilic range.
eset2genelevel(x)eset2genelevel(x)
x |
an object of class |
Evarist Planet
genefilter::nsFilter
#data(eset) #library(hgu133a.db) #x <- eset2genelevel(eset) #x #head(featureNames(x))#data(eset) #library(hgu133a.db) #x <- eset2genelevel(eset) #x #head(featureNames(x))
Saves object as comma-separated text file (CSV), using write.csv.
export2CSV(x, file, row.names=FALSE, ...)export2CSV(x, file, row.names=FALSE, ...)
x |
object to be exported. Currently methods for objects of class epheno (produced with ExpressionPhenoTest function) are implemented. |
file |
Name of the file where the results are to be saved |
row.names |
Passed on to |
... |
Other arguments to be passed on to |
Methods for function export2CSV in Package 'phenoTest'
signature(x = "epheno")Exports summary differences (fold changes, hazard ratios), p-values and gene annotation (when available) to a CSV (comma separated value) file
Tests univariate association between a list of phenotype variables and gene expression.
ExpressionPhenoTest(x, vars2test, adjustVars, p.adjust.method='BH',continuousCategories=3,mc.cores,approach='frequentist')ExpressionPhenoTest(x, vars2test, adjustVars, p.adjust.method='BH',continuousCategories=3,mc.cores,approach='frequentist')
x |
ExpressionSet containing expression levels in |
vars2test |
list with components 'continuous', 'categorical', 'ordinal' and 'survival' indicating which phenotype variables should be tested. 'continuous', 'categorical' and 'ordinal' must be character vectors, 'survival' a matrix with columns named 'time' and 'event'. The names must match names in names(pData(x)). |
adjustVars |
variables that will be used as adjustment variables
when fitting linear models and/or cox models. This variables have to
exist in |
p.adjust.method |
method for p-value adjustment, passed on to
|
continuousCategories |
number of categories used for continuous variables. |
mc.cores |
the number of cores to use, i.e. how many processes will be spawned (at most). |
approach |
this can be either 'frequentist' or 'bayesian'. With frequentist pvalues will be computed. With 'bayesian' posterior probabilities will be computed. |
If approach is 'frequentist': -The effect of both continuous, categorical and ordinal phenotype variables on gene expression levels are tested via lmFit. -For ordinal variables a single coefficient is used to test its effect on gene expression (trend test), which is then used to obtain a P-value (means for each category are reported in the output). -Gene expression effects on survival are tested via Cox proportional hazards model, as implemented in function 'coxph'.
If approach is bayesian posterior probabilities are computed comparing the BIC of a model with the variable of interest as explanatory variable against the BIC of the same model without the variable of interest as explanatory variable.
The output is an epheno object, which basically extends an
ExpressionSet object. The means, fold changes, standarized hazard
ratios and pvalues are stored in the experimentData slot which is
accessible with the exprs method. Information about the kind of
information of each variable can be found in the phenoData slot
which is accessible with the pData method.
There are several methods that can be used to access the information
stored in an epheno object. For more information please type one
of the following: getFc(x), getHr(x), getMeans(x), getSignif,
getPvals(x), getPostProbs, getSummaryDif(x), logFcHr(x),
p.adjust.method(x), phenoClass(x), phenoNames(x).
David Rossell
Kass R.E. and Wasserman L. A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion. Journal of the American Statistical Association, 90, pp. 928-934.
#load eset data(eset) eset #prepare vars2test survival <- matrix(c("Relapse","Months2Relapse"),ncol=2,byrow=TRUE) colnames(survival) <- c('event','time') vars2test <- list(survival=survival) #run ExpressionPhenoTest epheno <- ExpressionPhenoTest(eset,vars2test,p.adjust.method='none') epheno#load eset data(eset) eset #prepare vars2test survival <- matrix(c("Relapse","Months2Relapse"),ncol=2,byrow=TRUE) colnames(survival) <- c('event','time') vars2test <- list(survival=survival) #run ExpressionPhenoTest epheno <- ExpressionPhenoTest(eset,vars2test,p.adjust.method='none') epheno
Given enrichment scores between two groups of samples and the chromosomical positions of those enrichment scores this function finds areas where the enrichemnt is bigger/lower than expected if the positions where assigned at random. Plots of the regions and positions of the enriched regions are provided.
findCopyNumber(x, minGenes = 15, B = 100, p.adjust.method = "BH", pvalcutoff = 0.05, exprScorecutoff = NA, mc.cores = 1, useAllPerm = F, genome = "hg19", chrLengths, sampleGenome = TRUE, useOneChr = FALSE, useIntegrate = TRUE,plot=TRUE,minGenesPerChr=100)findCopyNumber(x, minGenes = 15, B = 100, p.adjust.method = "BH", pvalcutoff = 0.05, exprScorecutoff = NA, mc.cores = 1, useAllPerm = F, genome = "hg19", chrLengths, sampleGenome = TRUE, useOneChr = FALSE, useIntegrate = TRUE,plot=TRUE,minGenesPerChr=100)
x |
An object of class |
minGenes |
Minimum number of genes in a row that have to be enriched to mark the region as enriched. Has to be bigger than 2. |
B |
Number of permuations that will be computed to calculate pvalues.
If |
p.adjust.method |
P value adjustment method to be used. p.adjust.methods provides a list of available methods. |
pvalcutoff |
All genes with an adjusted p value lower than this parameter will be considered enriched. |
exprScorecutoff |
Genes with a smoothed score that is not bigger (lower if the given number is negative) than the specified value will not be considered significant. |
mc.cores |
Number of cores to be used in the computation. If |
useAllPerm |
If FALSE for each gene only permutations of genes that are in an area with similar density (similar number of genes close to them) are used to compute pvalues. If TRUE all permutations are used for each gene. We recommend to use the option FALSE after having observed that the enrichment can depend on the number of genes that are in the area. We recommend to use the option TRUE if the positions of the enrichment
score are equidistant. Take into account that this option is much slower
and needs less permutations, therefore a smaller See details for more info. |
genome |
Genome that will be used to draw cytobands. |
chrLengths |
An object of class |
sampleGenome |
If positions are sampled over the hole genome (across chromosomes) or within each chromosome. This is TRUE by default. |
useOneChr |
Use only one chromosome to build the distribution under the null hypothesis that genes/probes are not enriched. By default this is FALSE. The chromosome that is used is chosen as follows: after removing small chromosomes we select the one closest to the median quadratic distance to 0. Setting this parameter to TRUE decreases processing time. |
useIntegrate |
If we want to use |
plot |
If FALSE the function will make no plots. |
minGenesPerChr |
Chromosomes with less than |
Enrichemnt scores can be either log fold changes, log hazard ratios, log variabiliy ratios or any other score.
Within each chromosome a smoothed score for each gene is obtained via generalized additive models, the smoothing parameter for each chromosome being chosen via cross-validation. The obtained smoothing parameter of each chromosome will be used in permutations.
We assessed statistical significance by permuting the positions thrue
the hole genome.
If useAllPerm is FALSE for each gene the permutations of genes
that are in an area with similar density (distance to tenth gene) are
used to compute pvalues. We observed that genes with similar densities
tend to have similar smoothed scores.
If we set 1000 permutations (B=1000) scores are permuted thrue
the hole genome 10 times (1000/100). For each smoothed scored the
permutations of the 100 smoothed scores with most similar density
(distance to tenth gene) are used. Therefore each smoothed score will be
compared to 1000 smoothed scores obtained from permutations.
If scores are at the same distance in the genome from each other (for
instance when we have a score every fixed certain bases) the option
useAllPerm=TRUE is recommended. In this case every smoothed score
is compared to all smoothed scores obtained via permutations.
In this case having 20,000 genes and setting the paramter B=10
would mean that the scores are permuted 10 times times thrue the hole
genome, obtaining 200,000 permuted smoothed scores. Each observed smoothed
score will be tested against the distribution of the 200,000 permuted
smoothed scores.
Only regions with as many genes as told in minGenes being
statistically significant (pvalue lower than parameter
pvalcutoff) after adjusting pvalues with the method specified in
p.adjust.method will be selected as enriched.
If exprScorecutoff is different form NA, a gene to be
statistically significant will need (aditionally to the pvalue cutoff)
to have a smoothed score bigger (lower if exprScorecutoff is
negative) than the specified value.
Plots all chromomes and marks the enriched regions.
Also returns a data.frame containing the positions of the
enriched regions. This output can be passed by to the genesInArea
function to obtain the names of the genes that are in each region.
Evarist Planet
getEsPositions, genesInArea
data(epheno) phenoNames(epheno) mypos <- getEsPositions(epheno,'Relapse') mypos$chr <- '1' #we set all probes to chr one for illustration purposes #(we want a minimum number of probes per chromosome) head(mypos) set.seed(1) regions <- findCopyNumber(mypos,B=10,plot=FALSE) head(regions)data(epheno) phenoNames(epheno) mypos <- getEsPositions(epheno,'Relapse') mypos$chr <- '1' #we set all probes to chr one for illustration purposes #(we want a minimum number of probes per chromosome) head(mypos) set.seed(1) regions <- findCopyNumber(mypos,B=10,plot=FALSE) head(regions)
Combine the output of getEsPositions and findCopyNumber to see which genes are in the enriched areas.
Given areas of enrichment (obtained with findCopyNumber) and a
set of genes or probes and their positions in the genome (obtained with
getEsPositions) the function tells which genes fall in each
area.
genesInArea(x, regions)genesInArea(x, regions)
x |
An object of class |
regions |
This is usually the output of |
Evarist Planet
getEsPositions, findCopyNumber
data(epheno) phenoNames(epheno) mypos <- getEsPositions(epheno,'Relapse') head(mypos) #regions <- findCopyNumber(mypos) #head(regions) #genes <- genesInArea(mypos,regions) #head(genes)data(epheno) phenoNames(epheno) mypos <- getEsPositions(epheno,'Relapse') head(mypos) #regions <- findCopyNumber(mypos) #head(regions) #genes <- genesInArea(mypos,regions) #head(genes)
getEs returns ES (enrichment scores) getEsSim returns simulated ES (needed to compute pvals), getNes returns NES (normalized enrichment scores) and getFcHr returns the fold changes or hazard used to compute the ES, simulated ES and NES.
getEs(x) getEsSim(x) getNes(x) getFcHr(x)getEs(x) getEsSim(x) getNes(x) getFcHr(x)
x |
an |
Evarist Planet
Given an object of class epheno obtain the gene positions on the genome.
getEsPositions(epheno, phenoName, organism = "human", logEs = T, center = FALSE)getEsPositions(epheno, phenoName, organism = "human", logEs = T, center = FALSE)
epheno |
An object of class |
phenoName |
The phenotype that we want to use. Has to be in |
organism |
Has to be 'human' or 'mouse'. The default is 'human'. |
logEs |
If the values have to be log scaled. |
center |
If the values have to be genome centered. If TRUE the genome average will be substracted to every value. |
The output will usually be passed to findCopyNumber.
An object of class data.frame will be returned containing 3
variables: es (enichment score for fold change or hazard ratio), chr
(chromosome), pos (position in Mb).
epheno's featureNames will be used as row names.
Evarist Planet
data(epheno) phenoNames(epheno) mypos <- getEsPositions(epheno,'Relapse') head(mypos)data(epheno) phenoNames(epheno) mypos <- getEsPositions(epheno,'Relapse') head(mypos)
getFc gets the fold changes. getHr gets the hazard
ratios. getMeans gets the means. getPvals gets the p
values. getPostProbs get the posterior
probabilities. getSignif gets the pvalues or the posterior
probabilities depending on the approach (frequentist or bayesian) that
was used when the epheno object was created. getSummaryDif gets
fold changes and hazard ratios. lofFcHr gets the fold changes and
hazard ratios after log scaling. p.adjust.method gets the p value
adjustment method that was used when creating the
object. phenoClass returns a data.frame telling the class
(ordinal, continuous, categorical or survival) of each
phenotype. phenoNames gets the phenotype names. approach
gets the approach that was used (either frequentist or bayesian).
getFc(x) getHr(x) getMeans(x) getSignif(x) getPvals(x) getPostProbs(x) getSummaryDif(x) logFcHr(x) p.adjust.method(x) phenoClass(x) phenoNames(x) approach(x)getFc(x) getHr(x) getMeans(x) getSignif(x) getPvals(x) getPostProbs(x) getSummaryDif(x) logFcHr(x) p.adjust.method(x) phenoClass(x) phenoNames(x) approach(x)
x |
|
Evarist Planet
Returns an object containing the names of the variables that were tested when the epheno object was created. Will return an object of class list. Variables of the same type (categorical, survival, etc) will be in the same slot of the list. The slot names are the types of the variables.
Evarist Planet
data(epheno) getVars2test(epheno)data(epheno) getVars2test(epheno)
Methods for function getVars2test in Package ‘phenoTest’. For
more information read the funcion's manual.
signature(x = "epheno")Method for an object of class epheno.
Computes the enrichment scores and simulated enrichment scores for
each variable and signature.
An important parameter of the function is logScale. Its default
value is TRUE which means that by default the provided scores (i.e. fold
changes, hazard ratios) will be log scaled. Remember to change this
parameter to FALSE if your scores are already log scaled.
The getEs, getEsSim, getFc, getHr and
getFcHr methods can be used to acces each subobject. For more
information please visit the man pages of each method.
It also computes the NES (normalized enrichment score), p values and fdr
(false discovery rate) for all variables and signatures.
For an overview of the output use the summary method.
In case of providing gene sets which have more than 10 distinct lengths an approximation of the calculation of the enrichment score simulations (ESM) will be computed. The value of the ESM only depends on the length of the gene set. Therefore we compute the ESM over a grid of possible gene set lengths which are representative of the lengths of the provided gene sets. Then we fit a generalized additive model model with cubic splines to predict the NES value based on the length of every gene set. This provides a much faster approach that can be very useful when we need to run the software over a huge number of gene sets.
gsea(x,gsets,logScale=TRUE, absVals=FALSE, averageRepeats=FALSE, B=1000, mc.cores=1, test="perm",p.adjust.method="none", pval.comp.method="original",pval.smooth.tail=TRUE,minGenes=10, maxGenes=500,center=FALSE)gsea(x,gsets,logScale=TRUE, absVals=FALSE, averageRepeats=FALSE, B=1000, mc.cores=1, test="perm",p.adjust.method="none", pval.comp.method="original",pval.smooth.tail=TRUE,minGenes=10, maxGenes=500,center=FALSE)
x |
|
gsets |
character or list object containing the names of the genes that belong to each signature. |
logScale |
if values should be log scaled. |
absVals |
if TRUE fold changes and hazard ratios that are negative will be turned into positive before starting the process. This is useful when genes can go in both directions. |
averageRepeats |
if x is of class numeric and has repeated names (several measures for some indivdual names) we can average the measures of the same names. |
B |
number of simulations to perform. |
mc.cores |
number of processors to use. |
test |
the test that will be used. 'perm' stands for the permutation based method, 'wilcox' stands for the wilcoxon test (this is the fastest one) and 'ttperm' stands for permutation t test. |
p.adjust.method |
p adjustment method to be used. Common options
are 'BH', 'BY', 'bonferroni' or 'none'. All available options and
their explanations can be found on the |
pval.comp.method |
the p value computation method. Has to be one of 'signed' or 'original'. The default one is 'original'. See details for more information. |
pval.smooth.tail |
if we want to estimate the tail of the ditribution where the pvalues will be generated. |
minGenes |
gene sets with less than minGenes genes will be removed from the analysis. |
maxGenes |
gene sets with more than maxGenes genes will be removed from the analysis. |
center |
if we want to center scores (fold changes or hazard ratios). The following is will be done: x = x-mean(x). |
The following preprocessing was done on the provided scores (i.e. fold
changes, hazard ratios) to avoid errors during the enrichment score
computation:
-When having two scores with the same name its average was used.
-Zeros were removed.
-Scores without names (which can not be in any signature) removed.
-Non complete cases (i.e. NAs, NaNs) were removed.
ES score was calculated for each signature and variable (see
references). If parameter test is 'perm' the signature was
permutted and the ES score was recalculated (this happened B times for
each variable, 1000 by default).
If test is 'wilcox' a wilcoxon test in which we test the fact
that the average value of the genes that do belong to our signtaure is
different from the average value of the genes that do not belong to our
signature will be performed.
If test is 'ttperm' a permutation t-test will be used.
Take into account that the final plot will be different when 'wilcox' is used.
The simulated enrichment scores and the calculated one are used to find the p value.
P value calculation depends on the parameter
pval.comp.method. The default value is 'original'. In 'original'
we are simply computing the proportion of anbolute simulated ES which
are larger than the observed absolute ES. In 'signed' we are computing
the proportion of simulated ES which are larger than the observed ES (in
case of having positive enrichment score) and the proportion of
simulated ES which are smaller than the observed ES (in case of having
negative enrichment score).
Evarist Planet
Aravind Subramanian, (October 25, 2005) Gene Set Enrichment Analysis. www.pnas.org/cgi/doi/10.1073/pnas.0506580102
C.A. Tsai and J.J. Chen. Kernel estimation for adjusted p-values in multiple testing. Computational Statistics & Data Analysis http://econpapers.repec.org/article/eeecsdana/v_3a51_3ay_3a2007_3ai_3a8_3ap_3a3885-3897.htm
gsea.go
#load epheno object data(epheno) epheno #we construct two signatures sign1 <- sample(featureNames(epheno))[1:20] sign2 <- sample(featureNames(epheno))[50:75] mySignature <- list(sign1,sign2) names(mySignature) <- c('My first signature','My preferred signature') #run gsea functions gseaData <- gsea(x=epheno,gsets=mySignature,B=100,mc.cores=1) my.summary <- summary(gseaData) my.summary #plot(gseaData)#load epheno object data(epheno) epheno #we construct two signatures sign1 <- sample(featureNames(epheno))[1:20] sign2 <- sample(featureNames(epheno))[50:75] mySignature <- list(sign1,sign2) names(mySignature) <- c('My first signature','My preferred signature') #run gsea functions gseaData <- gsea(x=epheno,gsets=mySignature,B=100,mc.cores=1) my.summary <- summary(gseaData) my.summary #plot(gseaData)
gsea.selGsets to select a subgroup of gene sets from an object of
class gseaData.
gsea.selVars to select a subgroup of variables from an object of
class gseaData.
gsea.selGsets(x, selGsets) gsea.selVars(x, selVars)gsea.selGsets(x, selGsets) gsea.selVars(x, selVars)
x |
an object of class |
selGsets |
names of the gene sets that we want to keep. |
selVars |
names of the variables that we want to keep. |
Returns an object of class gseaData.
Evarist Planet
Exports gseaData objects to html files with plots and links to online databases.
gsea2html(gseaData, epheno, variable, title = "", path, file, digits = 3, plotEs = FALSE, limit=100)gsea2html(gseaData, epheno, variable, title = "", path, file, digits = 3, plotEs = FALSE, limit=100)
gseaData |
an object of class |
epheno |
the object of class |
variable |
varible that we are interested in. |
title |
title that will be shown on top of the table. |
path |
directory where we want to store the html files. |
file |
filename. |
digits |
Number of decimal digits that will be shown on the table. |
plotEs |
if this is TRUE enrichment score plots will be plotted instead of normalized enrichment score plots. |
limit |
maximum number of gene sets that will be exported. |
This function produces a browseable version of the table that we can
obtain with summary(gseaData).
We will obtain one plot per NES (or ES) and we will be able to see which
genes belong to each gene set and the values they have in the
epheno object.
Evarist Planet
#WITH PROBESET AS IDENTIFIER data(eset) data(epheno) set.seed(777) sign1 <- sample(featureNames(eset))[1:20] sign2 <- sample(featureNames(eset))[1:50] mySignature <- list(sign1,sign2) names(mySignature) <- c('My first signature','Another signature') mySignature mygsea <- gsea(x=epheno[,1],gsets=mySignature,B=100,p.adjust='BH') summary(mygsea) #following line has been commented to prevent the creation of files #gsea2html(gseaData=mygsea,epheno=epheno,variable=phenoNames(epheno)[1],title='My test',path='~/Desktop',file='myGSEA.html') #WITH ENTREZID AS IDENTIFIER data(eset.genelevel) eset.genelevel set.seed(777) sign1 <- sample(featureNames(eset.genelevel))[1:20] sign2 <- sample(featureNames(eset.genelevel))[1:50] mySignature.genelevel <- list(sign1,sign2) names(mySignature.genelevel) <- c('My first signature','Another signature') mySignature.genelevel epheno.genelevel <- ExpressionPhenoTest(eset.genelevel,vars2test=list(categorical='lymph.node.status')) mygsea.genelevel <- gsea(x=epheno.genelevel,gsets=mySignature.genelevel,B=100,p.adjust='BH') summary(mygsea.genelevel) #following line has been commented to prevent the creation of files #gsea2html(gseaData=mygsea.genelevel,epheno=epheno.genelevel,variable=phenoNames(epheno.genelevel),title='My test (at genelevel)',path='~/Desktop',file='myGSEA_genelevel.html')#WITH PROBESET AS IDENTIFIER data(eset) data(epheno) set.seed(777) sign1 <- sample(featureNames(eset))[1:20] sign2 <- sample(featureNames(eset))[1:50] mySignature <- list(sign1,sign2) names(mySignature) <- c('My first signature','Another signature') mySignature mygsea <- gsea(x=epheno[,1],gsets=mySignature,B=100,p.adjust='BH') summary(mygsea) #following line has been commented to prevent the creation of files #gsea2html(gseaData=mygsea,epheno=epheno,variable=phenoNames(epheno)[1],title='My test',path='~/Desktop',file='myGSEA.html') #WITH ENTREZID AS IDENTIFIER data(eset.genelevel) eset.genelevel set.seed(777) sign1 <- sample(featureNames(eset.genelevel))[1:20] sign2 <- sample(featureNames(eset.genelevel))[1:50] mySignature.genelevel <- list(sign1,sign2) names(mySignature.genelevel) <- c('My first signature','Another signature') mySignature.genelevel epheno.genelevel <- ExpressionPhenoTest(eset.genelevel,vars2test=list(categorical='lymph.node.status')) mygsea.genelevel <- gsea(x=epheno.genelevel,gsets=mySignature.genelevel,B=100,p.adjust='BH') summary(mygsea.genelevel) #following line has been commented to prevent the creation of files #gsea2html(gseaData=mygsea.genelevel,epheno=epheno.genelevel,variable=phenoNames(epheno.genelevel),title='My test (at genelevel)',path='~/Desktop',file='myGSEA_genelevel.html')
This class is an ES (enrichment score) and ES.sim (simulated enrichment score) container that will be used in the GSEA (Gene Set Enrichment Analysis) process. There is one container for every gene signature.
Objects can be created by calls of the form new("gseaData", ...).
.Data:Object of class "list" .
gseaSignaturesSign:Object of class
"gseaSignaturesSign" or "gseaSignaturesVar".
gseaSignificanceSign:Object of class
"gseaSignificanceSign" or "gseaSignificanceVar".
Class "list", from data part.
Class "vector", by class "list", distance 2.
Class "AssayData", by class "list", distance 2.
signature(x = "gseaData"): Returns the
enrichment scores.
signature(x = "gseaData"): Returns
the simulated enrichemt scores (the ones obtained after
permutations).
signature(x = "gseaData"): Returns
the fold change and/or the hazard ratio that were used to compute
the enrichment scores.
Evarist Planet
showClass("gseaSignaturesSign")showClass("gseaSignaturesSign")
This function has been deprecated. You could better use gsea
instead.
This function computes the first step in the process of obtaining a
GSEA-like plot.
It computes the enrichment scores and simulated enrichment scores for
each variable and signature. The output will usually be used as input
for the gseaSignificance function.
An important parameter of the function is logScale. Its default
value is TRUE which means that by default the provided scores (i.e. fold
changes, hazard ratios) will be log scaled. Remember to change this
parameter to FALSE if your scores are already log scaled.
The getEs, getEsSim, getFc, getHr and
getFcHr methods can be used to acces each subobject. For more
information please visit the man pages of each method.
gseaSignatures(x,gsets,logScale=TRUE,absVals=FALSE,averageRepeats=FALSE, B=1000,mc.cores=1,test='perm', minGenes=10,maxGenes=500,center=FALSE)gseaSignatures(x,gsets,logScale=TRUE,absVals=FALSE,averageRepeats=FALSE, B=1000,mc.cores=1,test='perm', minGenes=10,maxGenes=500,center=FALSE)
x |
|
gsets |
character or list object containing the names of the genes that belong to each signature. |
logScale |
if values should be log scaled. |
absVals |
if TRUE fold changes and hazard ratios that are negative will be turned into positive before starting the process. This is useful when genes can go in both directions. |
averageRepeats |
if x is of class numeric and has repeated names (several measures for some indivdual names) we can average the measures of the same names. |
B |
number of simulations to perform. |
mc.cores |
number of processors to use. |
test |
the test that will be used. 'perm' stands for the permutation based method, 'wilcox' stands for the wilcoxon test (this is the fastest one) and 'ttperm' stands for permutation t test. |
minGenes |
gene sets with less than minGenes genes will be removed from the analysis. |
maxGenes |
gene sets with more than maxGenes genes will be removed from the analysis. |
center |
if we want to center scores (fold changes or hazard ratios). The following is will be done: x = x-mean(x). |
The following preprocessing was done on the provided scores (i.e. fold changes, hazard
ratios) to avoid errors during the enrichment score computation:
-When having two scores with the same name its average was used.
-Zeros were removed.
-Scores without names (which can not be in any signature) removed.
-Non complete cases (i.e. NAs, NaNs) were removed.
ES score was calculated for each signature and variable (see
references). If parameter test is 'perm' the signature was
permutted and the ES score was recalculated (this happened B times for
each variable, 1000 by default).
If test is 'wilcox' a wilcoxon test in which we test the fact
that the average value of the genes that do belong to our signtaure is
different from the average value of the genes that do not belong to our
signature will be performed.
If test is 'ttperm' a permutation t-test will be used.
Take into account that the final plot will be different when 'wilcox' is used.
Evarist Planet
Aravind Subramanian, (October 25, 2005) Gene Set Enrichment Analysis. www.pnas.org/cgi/doi/10.1073/pnas.0506580102
#load epheno object data(epheno) epheno #we construct two signatures sign1 <- sample(featureNames(epheno))[1:20] sign2 <- sample(featureNames(epheno))[50:75] mySignature <- list(sign1,sign2) names(mySignature) <- c('My first signature','My preferred signature') #run gsea functions #my.gseaSignatures <- gseaSignatures(x=epheno,signatures=mySignature,B=100,mc.cores=1) #my.gseaSignificance <- gseaSignificance(my.gseaSignatures) #my.summary <- summary(my.gseaSignificance) #my.summary #plot(my.gseaSignatures,my.gseaSignificance)#load epheno object data(epheno) epheno #we construct two signatures sign1 <- sample(featureNames(epheno))[1:20] sign2 <- sample(featureNames(epheno))[50:75] mySignature <- list(sign1,sign2) names(mySignature) <- c('My first signature','My preferred signature') #run gsea functions #my.gseaSignatures <- gseaSignatures(x=epheno,signatures=mySignature,B=100,mc.cores=1) #my.gseaSignificance <- gseaSignificance(my.gseaSignatures) #my.summary <- summary(my.gseaSignificance) #my.summary #plot(my.gseaSignatures,my.gseaSignificance)
This object contains de ES (enrichment scores) and simulated ES that will be used in the GSEA (Gene Set Enrichment Analysis) process.
Objects can be created by calls of the form new("gseaSignatures", ...).
.Data:Object of class "list" .
es:Object of class "numeric" Contains the
observed enrichment scores. The ones that were compted from the
data without permuting anything.
es.sim:Object of class "numeric" Contains the
enrichment score that were obtained after permutations.
signature:Object of class "numeric" The subset
of genes we are interested in.
Class "list", from data part.
Class "vector", by class "list", distance 2.
Class "AssayData", by class "list", distance 2.
No methods defined with class "gseaSignatures" in the signature.
Evarist Planet
showClass("gseaSignatures")showClass("gseaSignatures")
Methods for function gseaSignatures in Package ‘phenoTest’. For
more information read the function's manual.
signature(x = "ANY", signatures = "character")Method for signature of class character.
signature(x = "ANY", signatures = "GeneSet")Method for signature of class character.
signature(x = "epheno", signatures = "list")Method for an epheno object and several signatures stored in an
object of class list.
signature(x = "matrix", signatures = "GeneSetCollection")Method for an matrix object and several signatures stored in an
object of class GeneSetCollection.
signature(x = "epheno", signatures = "GeneSetCollection")Method for an epheno object and several signatures stored in an
object of class GeneSetCollection.
signature(x = "numeric", signatures = "GeneSetCollection")Method for an numeric object and several signatures stored in an
object of class GeneSetCollection.
signature(x = "matrix", signatures = "list")Method for an matrix object and several signatures stored in an
object of class list.
signature(x = "numeric", signatures = "list")Method for an numeric object and several signatures stored in an
object of class list.
This class is an ES (enrichment score) and ES.sim (simulated enrichment score) container that will be used in the GSEA (Gene Set Enrichment Analysis) process. There is one container for every gene signature.
Objects can be created by calls of the form new("gseaSignaturesSign", ...).
.Data:Object of class "list" .
gseaSignatures:Object of class
"gseaSignatures" This is the object that will contain the
ES and ES.sim.
es.sim.gam:Object of class "matrix" enrichment
scores computed with the gam method.
fc.hr:Object of class "character" fold change
or hazard ratio used to compute the enrichment scores.
s:Object of class "logical" The subset of
genes we are interested in.
test:Object of class "character" The
statistical test that will be used.
Class "list", from data part.
Class "vector", by class "list", distance 2.
Class "AssayData", by class "list", distance 2.
signature(x = "gseaSignaturesSign"): Returns the
enrichment scores.
signature(x = "gseaSignaturesSign"): Returns
the simulated enrichemt scores (the ones obtained after
permutations).
signature(x = "gseaSignaturesSign"): Returns
the fold change and/or the hazard ratio that were used to compute
the enrichment scores.
signature(x = "gseaSignaturesSign"):
This is the next step in the process of performing GSEA. This
funtion will test if the gene sets are enriched.
Evarist Planet
showClass("gseaSignaturesSign")showClass("gseaSignaturesSign")
This class is an ES (enrichment score) and ES.sim (simulated enrichment
score) container that will be used in the GSEA (Gene Set Enrichment
Analysis) process.
There is one container for every phenotype. Every one of this containers
(of class gseaSignaturesSign) is a container itsef and has the
enrichment scores of all signatures.
GseaSignaturesVar contains one element per phenotype (phenotypic
variable). Every one of this elements is of class
gseaSignaturesSign and contains one element per signature.
Objects can be created by calls of the form new("gseaSignaturesVar", ...).
.Data:Object of class "list".
gseaSignatures:Object of class
"gseaSignaturesSign" This object contains the enrichment
scores and other elements that will be used in the GSEA process.
Class "list", from data part.
Class "vector", by class "list", distance 2.
Class "AssayData", by class "list", distance 2.
signature(x = "gseaSignaturesVar"): Returns the
enrichment scores.
signature(x = "gseaSignaturesVar"): Returns
the simulated enrichemt scores (the ones obtained after
permutations).
signature(x = "gseaSignaturesVar"): Returns
the fold change and/or the hazard ratio that were used to compute
the enrichment scores.
signature(x = "gseaSignaturesVar"):
This is the next step in the process of performing GSEA. This
funtion will test if the gene sets are enriched.
Evarist Planet
showClass("gseaSignaturesVar")showClass("gseaSignaturesVar")
This function has been deprecated. You could better use gsea
instead.
This function performs the second step in the process of obtaining a
GSEA-like plot.
It computes the NES (normalized enrichment score), p values and fdr
(false discovery rate) for all variables and signatures. A
gseaSignaturesSign or gseaSignaturesVar object will
be needed as input (these objects can be obtained with the
gseaSignatures function).
For an overview of the output use the summary method.
The next step after using the gseaSignificance function would be using
the plot method.
gseaSignificance(x,p.adjust.method='none',pval.comp.method='original',pval.smooth.tail=TRUE)gseaSignificance(x,p.adjust.method='none',pval.comp.method='original',pval.smooth.tail=TRUE)
x |
|
p.adjust.method |
p adjustment method to be used. Common options
are 'BH', 'BY', 'bonferroni' or 'none'. All available options and
their explanations can be found on the |
pval.comp.method |
the p value computation method. Has to be one of 'signed' or 'original'. The default one is 'original'. See details for more information. |
pval.smooth.tail |
if we want to estimate the tail of the ditribution where the pvalues will be generated. |
The simulated enrichment scores and the calculated one are used to find the p value.
P value calculation depends on the parameter
pval.comp.method. The default value is 'original'. In 'original'
we are simply computing the proportion of anbolute simulated ES which
are larger than the observed absolute ES. In 'signed' we are computing
the proportion of simulated ES which are larger than the observed ES (in
case of having positive enrichment score) and the proportion of
simulated ES which are smaller than the observed ES (in case of having
negative enrichment score).
Evarist Planet
Aravind Subramanian, (October 25, 2005) Gene Set Enrichment Analysis. www.pnas.org/cgi/doi/10.1073/pnas.0506580102
C.A. Tsai and J.J. Chen. Kernel estimation for adjusted p-values in multiple testing. Computational Statistics & Data Analysis http://econpapers.repec.org/article/eeecsdana/v_3a51_3ay_3a2007_3ai_3a8_3ap_3a3885-3897.htm
#for examples see the help file of gseaSigntaures: ?gseaSignatures#for examples see the help file of gseaSigntaures: ?gseaSignatures
This object contains the results of the test of enrichment that was performed on each gene set. There is one container for every gene signature.
Objects can be created by calls of the form new("gseaSignificanceSign", ...).
.Data:Object of class "list" .
gseaSignificance:Object of class "matrix"
Contains the statistics. Use the summary method to access
this information.
p.adjust.method:Object of class "character"
The p-value adjustment method that was used.
Class "list", from data part.
Class "vector", by class "list", distance 2.
Class "AssayData", by class "list", distance 2.
No methods defined with class "gseaSignificanceSign" in the signature.
Evarist Planet
showClass("gseaSignificanceSign")showClass("gseaSignificanceSign")
This object contains the results of the test of enrichment
that was performed on each gene set and phenotype.
There is one container for every phenotype. Every one of this containers
(of class gseaSignificanceSign) is a container itsef and has the
results of the tests for all signatures.
GseaSignificanceVar contains one element per phenotype (phenotypic
variable). Every one of this elements is of class
gseaSignificanceSign and contains one element per signature.
Objects can be created by calls of the form new("gseaSignificanceVar", ...).
.Data:Object of class "list" .
gseaSignificance:Object of class
"gseaSignificanceSign" This object contains the results of
the tests.
Class "list", from data part.
Class "vector", by class "list", distance 2.
Class "AssayData", by class "list", distance 2.
No methods defined with class "gseaSignificanceVar" in the signature.
Evarist Planet
showClass("gseaSignificanceVar")showClass("gseaSignificanceVar")
Show the associations between clusters that each sample belongs to and each phenotype in a heatmap and/or a Kaplan-Meier plot.
heatmapPhenoTest(x, signatures, vars2test, probes2genes = FALSE, filterVar, filteralpha = 0.05, distCol = "pearson", nClust = 2, distRow = "cor", p.adjust.method = "none", simulate.p.value = FALSE, B = 10^5, linkage = "average", equalize = FALSE, center = TRUE, col, survCol, heat.kaplan="both", ...)heatmapPhenoTest(x, signatures, vars2test, probes2genes = FALSE, filterVar, filteralpha = 0.05, distCol = "pearson", nClust = 2, distRow = "cor", p.adjust.method = "none", simulate.p.value = FALSE, B = 10^5, linkage = "average", equalize = FALSE, center = TRUE, col, survCol, heat.kaplan="both", ...)
x |
ExpressionSet with phenotype information stored in |
signatures |
Either character vector or list of character vectors
with gene sets to be used to draw heatmaps (gene names should match
those in |
vars2test |
list with components 'continuous', 'categorical',
'ordinal' and 'survival' indicating which phenotype variables should
be tested. 'continuous', 'categorical' and 'ordinal' must be character
vectors, 'survival' a matrix with columns named 'time' and
'event'. The names must match names in |
probes2genes |
If set to |
filterVar |
If specified, only genes with significant differences
in the variable |
filteralpha |
Significance level for the filtering based on |
distCol |
Distance metric used to cluster columns
(e.g. patients/samples). Can take any value accepted by
|
nClust |
Number of desired clusters. |
distRow |
Distance metric used to cluster rows (e.g. genes). Can
take any value accepted by |
p.adjust.method |
Method for P-value adjustment, passed on to
|
simulate.p.value |
If set to FALSE the chi-square test p-values are
computed using asymptotics, otherwise a simulation is used (see
|
B |
An integer specifying the number of replicates used in the
chi-square Monte Carlo test (passed on to |
linkage |
Linkage used for clustering. Must be either 'complete', 'average' or 'minimum'. |
equalize |
Should color codes be equalized between genes, i.e. all
genes present the same range of colors. Passed on to
|
center |
centering is done by subtracting the column means (omitting NAs). |
col |
Color scheme to be used for heatmap. Defaults to a green/red scheme designed to look nice for microarray data. |
survCol |
Colors for the Kaplan-Meier survival curves. |
heat.kaplan |
can be "heat" if we want to plot a heatmap, "kaplan" if we want to plot a kaplan-meier or "both" if we want both of them. |
... |
Other arguments for the survival plot, e.g. lty etc. |
Makes two clusters of samples based on the expression levels of the genes from the given signature and plots a heatmap and/or a Kaplan-Meier showing the association between belonging to one cluster or the other and each phenotype.
For variables in vars2test\$continuous and vars2test\$ordinal a Kruskal-Wallis Rank Sum test is
used; for vars2test\$categorical a chi-square test (with exact p-value
if simulate.p.value is set to TRUE); for var2test\$survival a Cox proportional hazards likelihood-ratio test.
David Rossell
#load data data(eset) eset #construct vars2test survival <- matrix(c("Relapse","Months2Relapse"),ncol=2,byrow=TRUE) colnames(survival) <- c('event','time') vars2test <- list(survival=survival) vars2test #construct a signature sign <- sample(featureNames(eset))[1:20] #make plot heatmapPhenoTest(eset,sign,vars2test=vars2test,heat.kaplan='heat') heatmapPhenoTest(eset,sign,vars2test=vars2test,heat.kaplan='kaplan')#load data data(eset) eset #construct vars2test survival <- matrix(c("Relapse","Months2Relapse"),ncol=2,byrow=TRUE) colnames(survival) <- c('event','time') vars2test <- list(survival=survival) vars2test #construct a signature sign <- sample(featureNames(eset))[1:20] #make plot heatmapPhenoTest(eset,sign,vars2test=vars2test,heat.kaplan='heat') heatmapPhenoTest(eset,sign,vars2test=vars2test,heat.kaplan='kaplan')
Methods for function heatmapPhenoTest in Package ‘phenoTest’. For
more information read the function's manual.
signature(x = "ExpressionSet", signatures = "character")Method for an ExpressionSet object and one signature stored in an
object of class character.
signature(x = "ExpressionSet", signatures = "list")Method for an ExpressionSet object and several signatures stored
in an object of class list.
signature(x = "ExpressionSet", signatures = "missing")Method for an ExpressionSet object and no signatures.
signature(x = "ExpressionSet", signatures = "GeneSet")Method for an ExpressionSet object and one signature stored in an
object of class GeneSet.
signature(x = "ExpressionSet", signatures = "GeneSetCollection")Method for an ExpressionSet object and several signatures stored
in an object of class GeneSetCollection.
Adjusts the p values of an epheno object. The p.adjust function will be
used. For more information read the p.adjust function's help.
pAdjust(x, method = "BH")pAdjust(x, method = "BH")
x |
an |
method |
the correction method that will be used. Se the
|
Evarist Planet
#load epheno object data(epheno) epheno #Adjust pvalue p.adjust.method(epheno) epheno <- pAdjust(epheno,method='BH') p.adjust.method(epheno)#load epheno object data(epheno) epheno #Adjust pvalue p.adjust.method(epheno) epheno <- pAdjust(epheno,method='BH') p.adjust.method(epheno)
Methods for function pAdjust in Package ‘phenoTest’. This
function adjusts the p-values of an epheno object. For more
informe read the function's manual.
signature(x = "epheno")Adjusts the pvalues of an epheno object.
Evarist Planet
Creates a Principal Components plot where we can show paired samples, and confidence intervals for the mean of every group of interest. We can also choose the component or components we want to plot.
pca(x, group, group2, pair, names, ellipse = FALSE, main = "", components = c(1, 2))pca(x, group, group2, pair, names, ellipse = FALSE, main = "", components = c(1, 2))
x |
An object of class ExpressionSet. |
group |
Variable in pData(x) that contains the groups of interest. Samples of the same group will be plotted with the same color. |
group2 |
Variable in pData(x) that contains secondary groups of interest. Sample of the same secondary group of interest will be plotted with the same symbol. |
pair |
Variable in pData(x) that contains the information about the pairs of data. Those pairs will be joined by a line. |
names |
Variable in pData(x) that contains the information about the names of the samples. |
ellipse |
If we want to plot ellipses with the 95 percent confidence intervals for every group. |
main |
A title for the plot. |
components |
Which components we want to plot. By default the first principal component will be plotted on the x axis and the second principal component will be plotted on the y axis. More than two components may be specified. If so multiple plots will be produced. |
Evarist Planet
prcomp.
data(eset) pca(x=eset, group='Relapse', names='GEOaccession') #pca(x=eset, group='Relapse', names='GEOaccession', components=1:3)data(eset) pca(x=eset, group='Relapse', names='GEOaccession') #pca(x=eset, group='Relapse', names='GEOaccession', components=1:3)
Builds a GSEA plot using a gseaData object.
gseaData object can be obtained with the gsea function.
plot.gseaData(x,selGsets,selVars,...)plot.gseaData(x,selGsets,selVars,...)
x |
this has to be of class |
selGsets |
object of class |
selVars |
object of class |
... |
Arguments to be passed to |
Evarist Planet
Aravind Subramanian, (October 25, 2005) Gene Set Enrichment Analysis. www.pnas.org/cgi/doi/10.1073/pnas.0506580102
#for examples see the help file of gseaSigntaures: ?gseaSignatures#for examples see the help file of gseaSigntaures: ?gseaSignatures
Builds a GSEA plot using a gseaSignature object (one of
gseaSignaturesSign or gseaSignaturesVar obtained with the
gseaSignatures function) and a gseaSignificance object (one of
gseaSignificanceSign or gseaSignificanceVar obtained with
the gseaSignificance function).
plot.gseaSignaturesSign(x,gseaSignificance,es.ylim,nes.ylim,es.nes="both",...)plot.gseaSignaturesSign(x,gseaSignificance,es.ylim,nes.ylim,es.nes="both",...)
x |
object of class |
gseaSignificance |
object of class |
es.ylim |
ylim values for the ES plot. |
nes.ylim |
ylim values for the NES plot. |
es.nes |
can be "es" if we want to plot enrichment score, "nes" if we want to plot normalised enrichment scores or "both"if we want to plot them both. |
... |
Arguments to be passed to |
Evarist Planet
Aravind Subramanian, (October 25, 2005) Gene Set Enrichment Analysis. www.pnas.org/cgi/doi/10.1073/pnas.0506580102
plot.gseaSignaturesSign, plot.gseaSignaturesVar
#for examples see the help file of gseaSigntaures: ?gseaSignatures#for examples see the help file of gseaSigntaures: ?gseaSignatures
Methods for function show in Package ‘methods’.
signature(object = "AnnotatedDataFrame")Will show an object of class AnnotatedDataFrame.
signature(object = "ANY")Will show an object of class ANY.
signature(object = "classRepresentation")Will show an object of class classRepresentation.
signature(object = "container")Will show an object of class container.
signature(object = "epheno")Will show an object of class epheno.
signature(object = "eSet")Will show an object of class eSet.
signature(object = "genericFunction")Will show an object of class genericFunction.
signature(object = "gseaSignaturesSign")Will show an object of class gseaSignaturesSign.
signature(object = "gseaSignaturesVar")Will show an object of class gseaSignaturesVar.
signature(object = "gseaSignificanceSign")Will show an object of class gseaSignificanceSign.
signature(object = "gseaSignificanceVar")Will show an object of class gseaSignificanceVar.
signature(object = "LargeDataObject")Will show an object of class LargeDataObject.
signature(object = "MethodDefinition")Will show an object of class MethodDefinition.
signature(object = "MethodSelectionReport")Will show an object of class MethodSelectionReport.
signature(object = "MethodWithNext")Will show an object of class MethodWithNext.
signature(object = "MIAME")Will show an object of class MIAME.
signature(object = "namedList")Will show an object of class namedList.
signature(object = "ObjectsWithPackage")Will show an object of class ObjectsWithPackage.
signature(object = "oldClass")Will show an object of class oldClass.
signature(object = "ScalarCharacter")Will show an object of class ScalarCharacter.
signature(object = "ScalarObject")Will show an object of class ScalarObject.
signature(object = "signature")Will show an object of class signature.
signature(object = "TestResults")Will show an object of class TestResults.
signature(object = "traceable")Will show an object of class traceable.
signature(object = "Versioned")Will show an object of class Versioned.
signature(object = "Versions")Will show an object of class Versions.
signature(object = "VersionsNull")Will show an object of class VersionsNull.
Builds a plot showing how hazard behaves over different levels of expression of a given gene. Confidence intervals are also provided.
smoothCoxph(time, event, x, xlim, ylim, xlab, ylab, logrisk=TRUE, ...)smoothCoxph(time, event, x, xlim, ylim, xlab, ylab, logrisk=TRUE, ...)
time |
variable where time to survival is stored. |
event |
variable where survival event is stored. |
x |
|
xlim |
|
ylim |
|
xlab |
|
ylab |
|
logrisk |
|
... |
other arguments that will be passed to plot. |
David Rossell.
#load eset data(eset) #make plot smoothCoxph(pData(eset)$Months2Relapse,pData(eset)$Relapse,exprs(eset)[25,])#load eset data(eset) #make plot smoothCoxph(pData(eset)$Months2Relapse,pData(eset)$Relapse,exprs(eset)[25,])
Builds a data.frame object that can easily be written to a csv file containing the ES, NES, pval.ES, pval.NES and FDR.
summary.gseaData(object,...)summary.gseaData(object,...)
object |
object of class |
... |
Arguments to be passed to |
Evarist Planet
Aravind Subramanian, (October 25, 2005) Gene Set Enrichment Analysis. www.pnas.org/cgi/doi/10.1073/pnas.0506580102
summary.gseaSignificanceSign, summary.gseaSignificanceVar
#for examples see the help file of gseaSigntaures: ?gsea#for examples see the help file of gseaSigntaures: ?gsea
Builds a data.frame object that can easily be written to a csv file containing the ES, NES, pval.ES, pval.NES and FDR.
summary.gseaSignificanceSign(object,...)summary.gseaSignificanceSign(object,...)
object |
object of class |
... |
Arguments to be passed to |
Evarist Planet
Aravind Subramanian, (October 25, 2005) Gene Set Enrichment Analysis. www.pnas.org/cgi/doi/10.1073/pnas.0506580102
#for examples see the help file of gseaSigntaures: ?gseaSignatures#for examples see the help file of gseaSigntaures: ?gseaSignatures
Creates an html file with links and plots from a table.
write.html(x, links, tiny.pic, tiny.pic.size = 100, title = "", file, digits = 3)write.html(x, links, tiny.pic, tiny.pic.size = 100, title = "", file, digits = 3)
x |
Object of class |
links |
Object of class |
tiny.pic |
Object of class |
tiny.pic.size |
size of the pictures if any. |
title |
Title that will be shown on top of the html file. |
file |
path and name of the file that will be created. |
digits |
number of digits that will be shown in numeric columns of x. |
Evarist Planet
write.csv, write.table, htmlpage
## ##Code has been commented to avoid the creation of files ## #(x <- data.frame(gene.symbol=c('AARS','ABCF1','ABLIM1'),value=c(2.054,30.024,5.0221),plot=rep('Open',3))) #tiny.pic <- links <- vector('list',length=ncol(x)) #links[[1]] <- paste('http://www.genecards.org/index.php?path=/Search/keyword/',x[,1]) #for (i in 1:nrow(x)) { # png(paste('~/Desktop/',x[i,1],'.png',sep='')) # plot(1:3,log(1:3)) # dev.off() #} #tiny.pic[[3]] <- links[[3]] <- paste(x[,1],'.png',sep='') #write.html(x,links=links,tiny.pic=tiny.pic,file='~/Desktop/x.html',title='My html test')## ##Code has been commented to avoid the creation of files ## #(x <- data.frame(gene.symbol=c('AARS','ABCF1','ABLIM1'),value=c(2.054,30.024,5.0221),plot=rep('Open',3))) #tiny.pic <- links <- vector('list',length=ncol(x)) #links[[1]] <- paste('http://www.genecards.org/index.php?path=/Search/keyword/',x[,1]) #for (i in 1:nrow(x)) { # png(paste('~/Desktop/',x[i,1],'.png',sep='')) # plot(1:3,log(1:3)) # dev.off() #} #tiny.pic[[3]] <- links[[3]] <- paste(x[,1],'.png',sep='') #write.html(x,links=links,tiny.pic=tiny.pic,file='~/Desktop/x.html',title='My html test')