Package 'MultiDataSet' reference manual

Title:	Implementation of MultiDataSet and ResultSet
Description:	Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages.
Authors:	Carlos Ruiz-Arenas [aut, cre], Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut]
Maintainer:	Xavier Escrib<c3><a0> Montagut <[email protected]>
License:	file LICENSE
Version:	1.35.0
Built:	2024-11-19 04:01:13 UTC
Source:	https://github.com/bioc/MultiDataSet

Method to add an `eSet` to `MultiDataSet`.

Description

This method adds or overwrites a slot of a MultiDataSet with the content of the given eSet.

Usage

add_eset(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  sample.tables = NULL,
  feature.tables = NULL,
  warnings = TRUE,
  overwrite = FALSE,
  GRanges
)
add_eset(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  sample.tables = NULL,
  feature.tables = NULL,
  warnings = TRUE,
  overwrite = FALSE,
  GRanges
)

Arguments

`object`	`MultiDataSet` that will be filled.
`set`	Object derived from `eSet` to be used to fill the slot.
`dataset.type`	Character with the type of data of the omic set (e.g. expression, methylation...)
`dataset.name`	Character with the specific name for this set (NULL by default). It is useful when there are several sets of the same type (e.g. multiple expression assays)
`sample.tables`	Character with the names of the slots with sample data besides phenoData.
`feature.tables`	Character with the names of the slots with feature data besides featureData.
`warnings`	Logical to indicate if warnings will be displayed.
`overwrite`	Logical to indicate if the set stored in the slot will be overwritten.
`GRanges`	`GenomicRanges` to be included in rowRanges slot.

Value

A new MultiDataSet with a slot filled.

Examples

multi <- createMultiDataSet()
eset <- new("ExpressionSet", exprs = matrix(runif(10), 5))
multi <- add_eset(multi, eset, "exampledata", GRanges = NA)
multi <- createMultiDataSet()
eset <- new("ExpressionSet", exprs = matrix(runif(10), 5))
multi <- add_eset(multi, eset, "exampledata", GRanges = NA)

Method to add an expression microarray dataset to `MultiDataSet`.

Description

This method adds or overwrites the slot "expression" of an MultiDataSet with the content of the given ExpressionSet. The fData of the ExpressionSet must contain the columns chromosome, start and end.

Usage

add_genexp(object, gexpSet, ...)
add_genexp(object, gexpSet, ...)

Arguments

`object`	`MultiDataSet` that will be filled.
`gexpSet`	`ExpressionSet` to be used to fill the slot.
`...`	Arguments to be passed to `add_eset`.

Value

A new MultiDataSet with the slot "expression" filled.

Examples

multi <- createMultiDataSet()
eset <- new("ExpressionSet", exprs = matrix(runif(4), 2))
fData(eset) <- data.frame(chromosome = c("chr1", "chr2"), start = c(12414, 1234321),
 end = c(121241, 124124114), stringsAsFactors = FALSE)
multi <- add_genexp(multi, eset)
multi <- createMultiDataSet()
eset <- new("ExpressionSet", exprs = matrix(runif(4), 2))
fData(eset) <- data.frame(chromosome = c("chr1", "chr2"), start = c(12414, 1234321),
 end = c(121241, 124124114), stringsAsFactors = FALSE)
multi <- add_genexp(multi, eset)

Method to add a slot of methylation to `MultiDataSet`.

Description

This method adds or overwrites the slot "methylation" of an MultiDataSet with the content of the given MethylationSet or RatioSet. The fData of the input object must contain the columns chromosome and position.

Usage

add_methy(object, methySet, ...)
add_methy(object, methySet, ...)

Arguments

`object`	`MultiDataSet` that will be filled.
`methySet`	`MethylationSet` or `RatioSet` to be used to fill the slot.
`...`	Further arguments to be passed to `add_eset`.

Value

A new MultiDataSet with the slot "methylation" filled.

Examples

if (require(brgedata)){
 multi <- createMultiDataSet()
 multi <- add_methy(multi, brge_methy[1:100, ])
}
if (require(brgedata)){
 multi <- createMultiDataSet()
 multi <- add_methy(multi, brge_methy[1:100, ])
}

Method to add an expression RNA seq dataset to `MultiDataSet`.

Description

This method adds or overwrites the slot "rnaseq" of an MultiDataSet with the content of the given ExpressionSet. The fData of the ExpressionSet must contain the columns chromosome, start and end.

Usage

add_rnaseq(object, rnaSet, ...)
add_rnaseq(object, rnaSet, ...)

Arguments

`object`	`MultiDataSet` that will be filled.
`rnaSet`	`ExpressionSet` to be used to fill the slot.
`...`	Arguments to be passed to `add_eset`.

Value

A new MultiDataSet with the slot "rnaseq" filled.

Examples

multi <- createMultiDataSet()
eset <- new("ExpressionSet", exprs = matrix(runif(4), 2))
fData(eset) <- data.frame(chromosome = c("chr1", "chr2"), start = c(12414, 1234321),
 end = c(121241, 12122414), stringsAsFactors = FALSE)
multi <- add_genexp(multi, eset)
multi <- createMultiDataSet()
eset <- new("ExpressionSet", exprs = matrix(runif(4), 2))
fData(eset) <- data.frame(chromosome = c("chr1", "chr2"), start = c(12414, 1234321),
 end = c(121241, 12122414), stringsAsFactors = FALSE)
multi <- add_genexp(multi, eset)

Method to add a `RangedSummarizedExperiment` to `MultiDataSet`.

Description

This method adds or overwrites a slot of a MultiDataSet with the content of the given RangedSummarizedExperiment.

Usage

add_rse(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  sample.tables = NULL,
  feature.tables = NULL,
  warnings = TRUE,
  overwrite = FALSE
)
add_rse(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  sample.tables = NULL,
  feature.tables = NULL,
  warnings = TRUE,
  overwrite = FALSE
)

Arguments

`object`	`MultiDataSet` that will be filled.
`set`	Object derived from `RangedSummarizedExperiment` to be used to fill the slot.
`dataset.type`	Character with the type of data of the omic set (e.g. expression, methylation...)
`dataset.name`	Character with the specific name for this set (NULL by default). It is useful when there are several sets of the same type (e.g. multiple expression assays)
`sample.tables`	Character with the names of the slots with sample data besides colData.
`feature.tables`	Character with the names of the slots with feature data besides rowData.
`warnings`	Logical to indicate if warnings will be displayed.
`overwrite`	Logical to indicate if the set stored in the slot will be overwritten.

Value

A new MultiDataSet with a slot filled.

Examples

if (require(GenomicRanges) & require(SummarizedExperiment)){
multi <- createMultiDataSet()
counts <- matrix(runif(200 * 6, 1, 1e4), 200)
rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
                     IRanges(floor(runif(200, 1e5, 1e6)), width=100),
                     strand=sample(c("+", "-"), 200, TRUE),
                     feature_id=sprintf("ID%03d", 1:200))
colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
                    row.names=LETTERS[1:6], id = LETTERS[1:6])
names(rowRanges) <- 1:200
rse <- SummarizedExperiment(assays=SimpleList(counts=counts),
                            rowRanges=rowRanges, colData=colData)
multi <- add_rse(multi, rse, "rseEx")
}
if (require(GenomicRanges) & require(SummarizedExperiment)){
multi <- createMultiDataSet()
counts <- matrix(runif(200 * 6, 1, 1e4), 200)
rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
                     IRanges(floor(runif(200, 1e5, 1e6)), width=100),
                     strand=sample(c("+", "-"), 200, TRUE),
                     feature_id=sprintf("ID%03d", 1:200))
colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
                    row.names=LETTERS[1:6], id = LETTERS[1:6])
names(rowRanges) <- 1:200
rse <- SummarizedExperiment(assays=SimpleList(counts=counts),
                            rowRanges=rowRanges, colData=colData)
multi <- add_rse(multi, rse, "rseEx")
}

Method to add a `SummarizedExperiment` to `MultiDataSet`.

Description

This method adds or overwrites a slot of a MultiDataSet with the content of the given SummarizedExperiment.

Usage

add_se(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  sample.tables = NULL,
  feature.tables = NULL,
  warnings = TRUE,
  overwrite = FALSE,
  GRanges
)
add_se(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  sample.tables = NULL,
  feature.tables = NULL,
  warnings = TRUE,
  overwrite = FALSE,
  GRanges
)

Arguments

`object`	`MultiDataSet` that will be filled.
`set`	Object derived from `SummarizedExperiment` to be used to fill the slot.
`dataset.type`	Character with the type of data of the omic set (e.g. expression, methylation...)
`dataset.name`	Character with the specific name for this set (NULL by default). It is useful when there are several sets of the same type (e.g. multiple expression assays)
`sample.tables`	Character with the names of the slots with sample data besides colData.
`feature.tables`	Character with the names of the slots with feature data besides rowData.
`warnings`	Logical to indicate if warnings will be displayed.
`overwrite`	Logical to indicate if the set stored in the slot will be overwritten.
`GRanges`	`GenomicRanges` to be included in rowRanges slot.

Value

A new MultiDataSet with a slot filled.

Examples

multi <- createMultiDataSet()
se <- SummarizedExperiment::SummarizedExperiment(matrix(runif(10), 5))
multi <- add_se(multi, se, "exampledata", GRanges = NA)
multi <- createMultiDataSet()
se <- SummarizedExperiment::SummarizedExperiment(matrix(runif(10), 5))
multi <- add_se(multi, se, "exampledata", GRanges = NA)

Method to add a slot of SNPs to `MultiDataSet`.

Description

This method adds or overwrites the slot "snps" of an MultiDataSet with the content of the given SnpSet. The fData of the SnpSet must contain the columns chromosome and position.

Usage

add_snps(object, snpSet, ...)
add_snps(object, snpSet, ...)

Arguments

`object`	`MultiDataSet` that will be filled.
`snpSet`	`SnpSet` to be used to fill the slot.
`...`	Arguments to be passed to `add_eset`.

Value

A new MultiDataSet with the slot "snps" filled.

Examples

multi <- createMultiDataSet()
geno <- matrix(c(3,1,2,1), ncol = 2)
colnames(geno) <- c("VAL0156", "VAL0372")
rownames(geno) <- c("rs3115860", "SNP1-1628854")
map <- AnnotatedDataFrame(data.frame(chromosome = c("chr1", "chr2"), position = c(12414, 1234321),
     stringsAsFactors = FALSE))
rownames(map) <- rownames(geno)
snpSet <- new("SnpSet", call = geno, featureData = map)
pheno <- data.frame(id = c("VAL0156", "VAL0372"))
rownames(pheno) <- c("VAL0156", "VAL0372")
pData(snpSet) <- pheno
multi <- add_snps(multi, snpSet)
multi <- createMultiDataSet()
geno <- matrix(c(3,1,2,1), ncol = 2)
colnames(geno) <- c("VAL0156", "VAL0372")
rownames(geno) <- c("rs3115860", "SNP1-1628854")
map <- AnnotatedDataFrame(data.frame(chromosome = c("chr1", "chr2"), position = c(12414, 1234321),
     stringsAsFactors = FALSE))
rownames(map) <- rownames(geno)
snpSet <- new("SnpSet", call = geno, featureData = map)
pheno <- data.frame(id = c("VAL0156", "VAL0372"))
rownames(pheno) <- c("VAL0156", "VAL0372")
pData(snpSet) <- pheno
multi <- add_snps(multi, snpSet)

Method to add a `matrix` to `MultiDataSet`.

Description

This method adds or overwrites a slot of a MultiDataSet with the content of the given matrix.

Usage

add_table(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  warnings = TRUE,
  overwrite = FALSE
)
add_table(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  warnings = TRUE,
  overwrite = FALSE
)

Arguments

`object`	`MultiDataSet` that will be filled.
`set`	`matrix` used to fill the slot.
`dataset.type`	Character with the type of data
`dataset.name`	Character with the specific name for this set (NULL by default). It is useful when there are several sets of the same type.
`warnings`	Logical to indicate if warnings will be displayed.
`overwrite`	Logical to indicate if the set stored in the slot will be overwritten.

Value

A new MultiDataSet with a slot filled.

Examples

multi <- createMultiDataSet()
mat <- matrix(runif(12), nrow = 3)
colnames(mat) <- paste0("S", 1:4)
rownames(mat) <- paste0("F", 1:3)
multi <- add_table(multi, mat, "exampledata")
multi <- createMultiDataSet()
mat <- matrix(runif(12), nrow = 3)
colnames(mat) <- paste0("S", 1:4)
rownames(mat) <- paste0("F", 1:3)
multi <- add_table(multi, mat, "exampledata")

Convert chr numbers to chr strings

Description

Given a vector of number representing the chromosomes, convert them to string (e.g 1 to chr1). 23 is consider chrX, 24 is chrY, 25 is chrXY (probes shared between chromosomes X and Y) and 26 is chrMT.

Usage

chrNumToChar(vector)
chrNumToChar(vector)

Arguments

vector

The vector with the chromosome numbers

Value

A vector with the chromosomes in string format.

Examples

chromosomes <- c(1, 3, 4, 23, 15)
stringChrs <- chrNumToChar(chromosomes)
stringChrs
chromosomes <- c(1, 3, 4, 23, 15)
stringChrs <- chrNumToChar(chromosomes)
stringChrs

Get the name of the ids common to all datasets

Description

Get the name of the ids common to all datasets

Usage

commonIds(object)
commonIds(object)

Arguments

object

MultiDataSet that will be filtered.

Value

Character vector with the common ids.

Examples

 
multi <- createMultiDataSet()
eset <- new("ExpressionSet", exprs = matrix(runif(9), ncol = 3))
fData(eset) <- data.frame(chromosome = c("chr1", "chr1", "chr1"), 
                          start = c(1, 5, 10),end = c(4, 6, 14), 
                          stringsAsFactors = FALSE)
sampleNames(eset) <- c("S1", "S2", "S3")
pData(eset) <- data.frame(id = c("S1", "S2", "S3"))
rownames(pData(eset)) <- c("S1", "S2", "S3")
multi <- add_genexp(multi, eset, dataset.name = "g1")
eset <- new("ExpressionSet", exprs = matrix(runif(8), ncol = 2))
fData(eset) <- data.frame(chromosome = c("chr1", "chr1", "chr1", "chr1"), 
                          start = c(1, 14, 25, 104),end = c(11, 16, 28, 115),
                          stringsAsFactors = FALSE)
sampleNames(eset) <- c("S1", "G2")
pData(eset) <- data.frame(id = c("S1", "G2"))
rownames(pData(eset)) <- c("S1", "G2")

multi <- add_genexp(multi, eset, dataset.name="g2")
commonIds(multi)
multi <- createMultiDataSet()
eset <- new("ExpressionSet", exprs = matrix(runif(9), ncol = 3))
fData(eset) <- data.frame(chromosome = c("chr1", "chr1", "chr1"), 
                          start = c(1, 5, 10),end = c(4, 6, 14), 
                          stringsAsFactors = FALSE)
sampleNames(eset) <- c("S1", "S2", "S3")
pData(eset) <- data.frame(id = c("S1", "S2", "S3"))
rownames(pData(eset)) <- c("S1", "S2", "S3")
multi <- add_genexp(multi, eset, dataset.name = "g1")
eset <- new("ExpressionSet", exprs = matrix(runif(8), ncol = 2))
fData(eset) <- data.frame(chromosome = c("chr1", "chr1", "chr1", "chr1"), 
                          start = c(1, 14, 25, 104),end = c(11, 16, 28, 115),
                          stringsAsFactors = FALSE)
sampleNames(eset) <- c("S1", "G2")
pData(eset) <- data.frame(id = c("S1", "G2"))
rownames(pData(eset)) <- c("S1", "G2")

multi <- add_genexp(multi, eset, dataset.name="g2")
commonIds(multi)

Method to select samples that are present in all datasets.

Description

This method subsets the datasets to only contain the samples that are in all datasets. All sets will have the samples in the same order, taking into account that there can be duplicates.

Usage

commonSamples(object, unify.names = FALSE)
commonSamples(object, unify.names = FALSE)

Arguments

`object`	`MultiDataSet` that will be filtered.
`unify.names`	Logical indicating if sample names of the sets should be unified.

Details

If unify.names is TRUE, the sample names of the sets will be unified using the id column of phenodata. This option is only possible when there are no duplicated ids.

Value

A new MultiDataSet with only the common samples.

Examples

 
multi <- createMultiDataSet()
eset <- new("ExpressionSet", exprs = matrix(runif(9), ncol = 3))
fData(eset) <- data.frame(chromosome = c("chr1", "chr1", "chr1"), 
                          start = c(1, 5, 10),end = c(4, 6, 14), 
                          stringsAsFactors = FALSE)
sampleNames(eset) <- c("S1", "S2", "S3")
pData(eset) <- data.frame(id = c("S1", "S2", "S3"))
rownames(pData(eset)) <- c("S1", "S2", "S3")
multi <- add_genexp(multi, eset, dataset.name = "g1")
eset <- new("ExpressionSet", exprs = matrix(runif(8), ncol = 2))
fData(eset) <- data.frame(chromosome = c("chr1", "chr1", "chr1", "chr1"), 
                          start = c(1, 14, 25, 104),end = c(11, 16, 28, 115),
                          stringsAsFactors = FALSE)
sampleNames(eset) <- c("S1", "G2")
pData(eset) <- data.frame(id = c("S1", "G2"))
rownames(pData(eset)) <- c("S1", "G2")

multi <- add_genexp(multi, eset, dataset.name="g2")
commonSamples(multi)
multi <- createMultiDataSet()
eset <- new("ExpressionSet", exprs = matrix(runif(9), ncol = 3))
fData(eset) <- data.frame(chromosome = c("chr1", "chr1", "chr1"), 
                          start = c(1, 5, 10),end = c(4, 6, 14), 
                          stringsAsFactors = FALSE)
sampleNames(eset) <- c("S1", "S2", "S3")
pData(eset) <- data.frame(id = c("S1", "S2", "S3"))
rownames(pData(eset)) <- c("S1", "S2", "S3")
multi <- add_genexp(multi, eset, dataset.name = "g1")
eset <- new("ExpressionSet", exprs = matrix(runif(8), ncol = 2))
fData(eset) <- data.frame(chromosome = c("chr1", "chr1", "chr1", "chr1"), 
                          start = c(1, 14, 25, 104),end = c(11, 16, 28, 115),
                          stringsAsFactors = FALSE)
sampleNames(eset) <- c("S1", "G2")
pData(eset) <- data.frame(id = c("S1", "G2"))
rownames(pData(eset)) <- c("S1", "G2")

multi <- add_genexp(multi, eset, dataset.name="g2")
commonSamples(multi)

Method to extrat feature result from a ResultSet

Description

Homologous methods from limma, getAssociation resturns a data.frame with the logFC and PValue per featrue for the selcted coef and for given result (rid).

Usage

getAssociation(object, rid = 1, coef = 2, contrast = NULL, fNames = NULL, ...)
getAssociation(object, rid = 1, coef = 2, contrast = NULL, fNames = NULL, ...)

Arguments

`object`	A `ResultSet` object.
`rid`	The name or index of the result to be extracted.
`coef`	(default `2`) Index of the coefficient to be extracted.
`contrast`	(default `1`) When `code` corresponds to a multicategorical variable, contasr selects the comparison.
`fNames`	(default `c("chromosome", "start", "end", "genesymbol")`) Corresponds to the columns selected from `fData` that will be incorporated into the resulting `data.frame`.
`...`	Further arguments passed to topTable

Value

A data.frame with the result of the association study, including P-Value and Fold Change.

Examples

data(rset)
getAssociation(rset, rid=1, fNames = c("chromosome", "position"))
data(rset)
getAssociation(rset, rid=1, fNames = c("chromosome", "position"))

Lambda Calculation for a vector of P-Values

Description

Implementation of Clayton's lambda score for a vector of P-Values

Usage

lambdaClayton(x, trim = 0.5)
lambdaClayton(x, trim = 0.5)

Arguments

`x`	Vector of P-Value
`trim`	(default `0.5`)

Value

A lambda value, indicating the inflation/deflation of the analysis.

Author(s)

Juan R. Gonzalez

Examples

lambdaClayton(runif(30))
lambdaClayton(runif(30))

Convert a `MultiAssayExperiment` to a `MultiDataSet`

Description

This function creates a MultiDataSet using the data of a MultiAssayExperiment.

Usage

mae2mds(MAE, warnings = TRUE)
mae2mds(MAE, warnings = TRUE)

Arguments

`MAE`	a `MultiAssayExperiment`
`warnings`	Logical to indicate if warnings will be displayed.

Value

MultiDataSet with the of the incoming MultiAssayExperiment.

Convert a `MultiDataSet` to a `MultiAssayExperiment`

Description

This function creates a MultiAssayExperiment using the data of a MultiDataSet.

Usage

mds2mae(MDS)
mds2mae(MDS)

Arguments

MDS

a MultiDataSet

Value

MultiAssayExperiment with the of the incoming MultiDataSet.

MultiDataSet: Implementation of the BRGE's basic classes

Description

Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and MethylationSet. MultiDataSet is designed for integrating multi omics data sets and MethylationSet to contain normalized methylation data. MultiDataSet for integrating multi omics data sets

MultiDataSet instances

Description

The class MultiDataSet is a superior class to store multiple datasets in form of triplets (assayData-phenoData-featureData). The datasets can be eSet or SummarizedExperiment derived or matrices.

Usage

## S4 method for signature 'MultiDataSet,eSet'
add_eset(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  sample.tables = "protocolData",
  feature.tables = NULL,
  warnings = TRUE,
  overwrite = FALSE,
  GRanges
)

## S4 method for signature 'MultiDataSet,ExpressionSet'
add_genexp(object, gexpSet, ...)

## S4 method for signature 'MultiDataSet,ExpressionSet'
add_rnaseq(object, rnaSet, ...)

## S4 method for signature 'MultiDataSet,GenomicRatioSet'
add_methy(object, methySet, ...)

## S4 method for signature 'MultiDataSet,RangedSummarizedExperiment'
add_rse(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  sample.tables = NULL,
  feature.tables = "elementMetadata",
  warnings = TRUE,
  overwrite = FALSE
)

## S4 method for signature 'MultiDataSet,SummarizedExperiment'
add_se(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  sample.tables = NULL,
  feature.tables = "elementMetadata",
  warnings = TRUE,
  overwrite = FALSE,
  GRanges
)

## S4 method for signature 'MultiDataSet,SnpSet'
add_snps(object, snpSet, ...)

## S4 method for signature 'MultiDataSet,matrix'
add_table(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  warnings = TRUE,
  overwrite = FALSE
)

## S4 method for signature 'MultiDataSet'
as.list(x)

## S4 method for signature 'MultiDataSet'
commonIds(object)

## S4 method for signature 'MultiDataSet'
commonSamples(object, unify.names = FALSE)

createMultiDataSet()

## S4 method for signature 'MultiDataSet'
dims(x)

## S4 method for signature 'MultiDataSet'
w_iclusterplus(object, commonSamples = TRUE, ...)

## S4 method for signature 'MultiDataSet'
length(x)

## S4 method for signature 'MultiDataSet'
w_mcia(object, ...)

## S4 method for signature 'MultiDataSet'
names(x)

## S4 method for signature 'MultiDataSet'
ncol(x)

## S4 method for signature 'MultiDataSet'
nrow(x)

## S4 method for signature 'MultiDataSet'
rowRangesElements(object)

## S4 method for signature 'MultiDataSet'
sampleNames(object)

## S4 method for signature 'MultiDataSet'
assayData(object)

## S4 method for signature 'MultiDataSet'
fData(object)

## S4 method for signature 'MultiDataSet'
featureData(object)

## S4 method for signature 'MultiDataSet'
pData(object)

## S4 method for signature 'MultiDataSet'
phenoData(object)

## S4 method for signature 'MultiDataSet'
rowRanges(x)

## S4 method for signature 'MultiDataSet,ANY,ANY'
x[[i]]

## S4 method for signature 'MultiDataSet,ANY,ANY,ANY'
x[i, j, k, ..., drop = FALSE]

## S4 method for signature 'MultiDataSet'
subset(x, feat, phe, warnings = TRUE, keep = TRUE)
## S4 method for signature 'MultiDataSet,eSet'
add_eset(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  sample.tables = "protocolData",
  feature.tables = NULL,
  warnings = TRUE,
  overwrite = FALSE,
  GRanges
)

## S4 method for signature 'MultiDataSet,ExpressionSet'
add_genexp(object, gexpSet, ...)

## S4 method for signature 'MultiDataSet,ExpressionSet'
add_rnaseq(object, rnaSet, ...)

## S4 method for signature 'MultiDataSet,GenomicRatioSet'
add_methy(object, methySet, ...)

## S4 method for signature 'MultiDataSet,RangedSummarizedExperiment'
add_rse(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  sample.tables = NULL,
  feature.tables = "elementMetadata",
  warnings = TRUE,
  overwrite = FALSE
)

## S4 method for signature 'MultiDataSet,SummarizedExperiment'
add_se(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  sample.tables = NULL,
  feature.tables = "elementMetadata",
  warnings = TRUE,
  overwrite = FALSE,
  GRanges
)

## S4 method for signature 'MultiDataSet,SnpSet'
add_snps(object, snpSet, ...)

## S4 method for signature 'MultiDataSet,matrix'
add_table(
  object,
  set,
  dataset.type,
  dataset.name = NULL,
  warnings = TRUE,
  overwrite = FALSE
)

## S4 method for signature 'MultiDataSet'
as.list(x)

## S4 method for signature 'MultiDataSet'
commonIds(object)

## S4 method for signature 'MultiDataSet'
commonSamples(object, unify.names = FALSE)

createMultiDataSet()

## S4 method for signature 'MultiDataSet'
dims(x)

## S4 method for signature 'MultiDataSet'
w_iclusterplus(object, commonSamples = TRUE, ...)

## S4 method for signature 'MultiDataSet'
length(x)

## S4 method for signature 'MultiDataSet'
w_mcia(object, ...)

## S4 method for signature 'MultiDataSet'
names(x)

## S4 method for signature 'MultiDataSet'
ncol(x)

## S4 method for signature 'MultiDataSet'
nrow(x)

## S4 method for signature 'MultiDataSet'
rowRangesElements(object)

## S4 method for signature 'MultiDataSet'
sampleNames(object)

## S4 method for signature 'MultiDataSet'
assayData(object)

## S4 method for signature 'MultiDataSet'
fData(object)

## S4 method for signature 'MultiDataSet'
featureData(object)

## S4 method for signature 'MultiDataSet'
pData(object)

## S4 method for signature 'MultiDataSet'
phenoData(object)

## S4 method for signature 'MultiDataSet'
rowRanges(x)

## S4 method for signature 'MultiDataSet,ANY,ANY'
x[[i]]

## S4 method for signature 'MultiDataSet,ANY,ANY,ANY'
x[i, j, k, ..., drop = FALSE]

## S4 method for signature 'MultiDataSet'
subset(x, feat, phe, warnings = TRUE, keep = TRUE)

Arguments

`object`	`MultiDataSet`
`set`	Object derived from `eSet` to be used to fill the slot.
`dataset.type`	Character with the type of data of the omic set (e.g. expression, methylation...)
`dataset.name`	Character with the specific name for this set (NULL by default). It is useful when there
`sample.tables`	Character with the names of the slots with sample data besides phenoData.
`feature.tables`	Character with the names of the slots with feature data besides featureData.
`warnings`	Logical to indicate if warnings will be displayed.
`overwrite`	Logical to indicate if the set stored in the slot will be overwritten.
`GRanges`	`GenomicRanges` to be included in rowRanges slot.
`gexpSet`	`ExpressionSet` to be used to fill the slot.
`...`	Further arguments passed to add_rse or add_se
`rnaSet`	`ExpressionSet` to be used to fill the slot.
`methySet`	`GenomicRatioSet` to be used to fill the slot.
`snpSet`	`SnpSet` to be used to fill the slot.
`x`	`MultiDataSet`
`unify.names`	Logical indicating if sample names of the sets should be unified.
`commonSamples`	Logical to indicate if common samples are selected
`i`	Character corresponding to selected sample names. They should match the id column of phenoData.
`j`	Character with the name of the selected tables.
`k`	`GenomicRange` used to filter the features.
`drop`	If TRUE, sets with no samples or features will be discarded
`feat`	Logical expression indicating features to keep
`phe`	Logical expression indicating the phenotype of the samples to keep
`keep`	If FALSE, sets where the expression cannot be evaluated will be discarded.

Details

The names of the three lists (assayData, phenoData and featureData) must be the same.

Value

MultiDataSet

Methods (by generic)

add_eset: Method to add an eSet to MultiDataSet.
add_genexp: Method to add a slot of expression to MultiDataSet.
add_rnaseq: Method to add a slot of (RNASeq) expression to MultiDataSet.
add_methy: Method to add a slot of methylation to MultiDataSet from a GenomicRatioSet.
add_rse: Method to add a RangedSummarizedExperiment to MultiDataSet.
add_se: Method to add a SummarizedExperiment to MultiDataSet.
add_snps: Method to add a slot of SNPs to MultiDataSet.
add_table: Method to add a matrix to MultiDataSet.
as.list: Returns a list with the first matrix of each dataset.
commonIds: Get the name of the ids common to all datasets
commonSamples: Get a MultiDataSet only with the samples present in all the tables
dims: Returns the dimensions of the sets
w_iclusterplus: Apply iClusterPlus clustering method to a MultiDataSet object
length: Returns the number of sets into the object.
w_mcia: Apply mcia integration method to a MultiDataSet object
names: Get the names of the slots.
ncol: Get number of samples of each set
nrow: Get number of features of each set
rowRangesElements: Get the name of the datasets that have rowRanges
sampleNames: Get sample names
assayData: Retrieve all assay data blocks.
fData: Retrieve information on features.
featureData: Retrieve information on features.
pData: Retrieve information on experimental phenotypes
phenoData: Retrieve information on experimental phenotypes
rowRanges: Retrieve information on feature ranges.
[[: Get a set from a slot
[: Subset a MultiDataSet
subset: Filter a subset using feature ids or phenotypes

Slots

assayData: List of assayData elements.
phenoData: List of AnnotatedDataFrame containing the phenoData of each assayData.
featureData: List of AnnotatedDataFrame containing the featureData of each assayData.
rowRanges: List of GenomicRanges containing the rowRanges of each assayData.
extraData: List of other slots of the original object.
return_method: List of functions used to create the original object.

Examples

createMultiDataSet()
createMultiDataSet()

Method to get the options sued to create the ResultSet

Description

Method that returns a list with the options used to create the ResultSet.

Usage

opt(object)
opt(object)

Arguments

object

A ResultSet object.

Value

A list with the options used to create the ResultSet.

Examples

data(rset)
opt(rset)
data(rset)
opt(rset)

Function to draw a QQ Plot from a vector of numbers

Description

Function to draw a QQ Plot from a vector of numbers

Usage

qq_plot(values, show.lambda = TRUE)
qq_plot(values, show.lambda = TRUE)

Arguments

`values`	Numeric vector of P.Values
`show.lambda`	(default: `TRUE`) If `TRUE` shows lambda score for the given model.

Value

An object obtained from ggplot.

Examples

data(rset)
rst <- getAssociation(rset, rid = 1, fNames = NULL)
qq_plot(rst$P.Value)
data(rset)
rst <- getAssociation(rset, rid = 1, fNames = NULL)
qq_plot(rst$P.Value)

Class ResultSet

Description

Class ResultSet used to encapsulate results from MEAL and omicrexposome.

Usage

## S4 method for signature 'ResultSet'
fData(object)

## S4 method for signature 'ResultSet'
getAssociation(object, rid = 1, coef = 2, contrast = NULL, fNames = NULL, ...)

## S4 method for signature 'ResultSet'
length(x)

## S4 method for signature 'ResultSet'
names(x)

## S4 method for signature 'ResultSet'
opt(object)

## S4 method for signature 'ResultSet,ANY'
plot(
  x,
  y,
  rid = 1,
  coef = 2,
  contrast = NULL,
  type,
  tFC = 2,
  tPV = -log10(0.001),
  show.labels = TRUE,
  show.effect = FALSE,
  show.lambda = TRUE,
  fNames = c("chromosome", "start"),
  subset,
  highlight,
  ...
)

## S4 method for signature 'ResultSet'
varLabels(object)

create_resultset(fOrigin, lResults, fData, lOptions = list())
## S4 method for signature 'ResultSet'
fData(object)

## S4 method for signature 'ResultSet'
getAssociation(object, rid = 1, coef = 2, contrast = NULL, fNames = NULL, ...)

## S4 method for signature 'ResultSet'
length(x)

## S4 method for signature 'ResultSet'
names(x)

## S4 method for signature 'ResultSet'
opt(object)

## S4 method for signature 'ResultSet,ANY'
plot(
  x,
  y,
  rid = 1,
  coef = 2,
  contrast = NULL,
  type,
  tFC = 2,
  tPV = -log10(0.001),
  show.labels = TRUE,
  show.effect = FALSE,
  show.lambda = TRUE,
  fNames = c("chromosome", "start"),
  subset,
  highlight,
  ...
)

## S4 method for signature 'ResultSet'
varLabels(object)

create_resultset(fOrigin, lResults, fData, lOptions = list())

Arguments

`object`	A `ResultSet` object.
`rid`	Name or index of the internal result to be used
`coef`	Coefficient to be returne, usually 2
`contrast`	Numeric matrix with the contrasts used to perform the analyses
`fNames`	Character vector with the names of the fData columns that will be added to the results data.frame.
`...`	Further arguments passed to topTable
`x`	A `ResultSet` object.
`y`	-
`type`	Type of plot to be drawn
`tFC`	Threshold for log FC of effect
`tPV`	Threshold for P-Value
`show.labels`	(default `TRUE`) If set to `TRUE`, features are labelled.
`show.effect`	(default: `TRUE`). Used in volcano plot. If `TRUE`, effect is shown as FC instead of logFC.
`show.lambda`	(default: `TRUE`) If `TRUE` shows lambda score for the given model.
`subset`	GenomicRanges used to zoom a region in Manhattan plot
`highlight`	GenomicRanges used to highlight a region in Manhattan plot
`fOrigin`	Chracter with the function used to run the analysis.
`lResults`	List with the results
`fData`	List with the feature data.
`lOptions`	List with additional options

Value

An object of class ResultSet

Methods (by generic)

fData: Returns data.frame with feature's data.
getAssociation: Getter to obtain the raw data.frame from association and integration analysis.
length: Returns the amoung of analyses stored in the ResultSet.
names: Returns the names of the omics data used to create the ResultSet.
opt: Returns a list with the options used to create the ResultSet
plot: Allows to plot a series of plots (QQ plot, Manhattan plot and Volcano plot) depending on the results stored in the ResultSet.
varLabels: Returns the names of the variables of the models used in a ResultSet.

Slots

fun_origin: Character containing the function that creates the object.
results: List containing the results of the association/integration.
fData: List containing the feature-data of the original objects.
options: list of options used to create the ResultSet.

Examples

create_resultset("hello", list(), list(), list())
create_resultset("hello", list(), list(), list())

Get the name of the datasets that have rowRanges

Description

Get the name of the datasets that have rowRanges

Usage

rowRangesElements(object)
rowRangesElements(object)

Arguments

object

MultiDataSet

Value

Character vector with the slots that have rowRanges.

Examples

multi <- createMultiDataSet()
eset <- new("ExpressionSet", exprs = matrix(runif(10), 5))
eset2 <- new("ExpressionSet", exprs = matrix(runif(8), ncol = 2))
fData(eset2) <- data.frame(chromosome = c("chr1", "chr1", "chr1", "chr1"), 
                          start = c(1, 14, 25, 104),end = c(11, 16, 28, 115),
                          stringsAsFactors = FALSE)
multi <- add_eset(multi, eset, "exampledata", GRanges = NA)
multi <- add_genexp(multi, eset2)
rowRangesElements(multi)
multi <- createMultiDataSet()
eset <- new("ExpressionSet", exprs = matrix(runif(10), 5))
eset2 <- new("ExpressionSet", exprs = matrix(runif(8), ncol = 2))
fData(eset2) <- data.frame(chromosome = c("chr1", "chr1", "chr1", "chr1"), 
                          start = c(1, 14, 25, 104),end = c(11, 16, 28, 115),
                          stringsAsFactors = FALSE)
multi <- add_eset(multi, eset, "exampledata", GRanges = NA)
multi <- add_genexp(multi, eset2)
rowRangesElements(multi)

Example `ResultSet`

Description

Example ResultSet used in the functions examples and in the tests. The script used to generate it can be found in inst/scripts.

Usage

rset
rset

Format

ResultSet

Function to draw a Volcano Plot

Description

Function that takes two numeric vectors (P-Value and fold change) and draws a volcano plot using ggplot2

Usage

volcano_plot(
  pval,
  fc,
  names,
  size = 2,
  tFC = 2,
  tPV = -log10(0.001),
  show.labels = TRUE,
  show.effect = FALSE
)
volcano_plot(
  pval,
  fc,
  names,
  size = 2,
  tFC = 2,
  tPV = -log10(0.001),
  show.labels = TRUE,
  show.effect = FALSE
)

Arguments

`pval`	numeric vector of P.Values
`fc`	numeric vector of fold change
`names`	character vector with the feature's names.
`size`	(default `2`) Sice of the labels in case they are placed.
`tFC`	(default `2`) fold change threshold. It can be set to `NULL` to not filter.
`tPV`	(default `-log10(0.001)`) P-Value threshold. It can be set to `NULL` to not filter.
`show.labels`	(default `TRUE`) If set to `TRUE`, features are labelled.
`show.effect`	(default `FALSE`) If set to `TRUE`, the X-axis will should `2^logFC` instead to the default `logFC`.

Value

A ggplot object

Examples

data(rset)
w1 <- getAssociation(rset, rid = 1, fNames = NULL)
volcano_plot(w1$P.Value, w1$logFC, rownames(w1))
data(rset)
w1 <- getAssociation(rset, rid = 1, fNames = NULL)
volcano_plot(w1$P.Value, w1$logFC, rownames(w1))

Apply iClusterPlus clustering method to a MultiDataSet object

Description

Method iClusterPlus is applied on a MultiDataSet object after getting the common samples along all the contained datasets.

Usage

w_iclusterplus(object, commonSamples = TRUE, ...)
w_iclusterplus(object, commonSamples = TRUE, ...)

Arguments

`object`	`MultiDataSet`
`commonSamples`	Logical to indicate if common samples are selected
`...`	Arguments passed to function iClusterPlus

Value

A list of results from iClusterPlus

Note

Argument type for iClusterPlus is filled within the method.

Apply mcia integration method to a MultiDataSet object

Description

Method mcia is applied on a MultiDataSet object after getting the common samples along all the contained datasets.

Usage

w_mcia(object, ...)
w_mcia(object, ...)

Arguments

`object`	`MultiDataSet`
`...`	Arguments passed to function mcia

Value

A list of results from mcia

Package 'MultiDataSet'

Help Index

Method to add an eSet to MultiDataSet.

Description

Usage

Arguments

Value

See Also

Examples

Method to add an expression microarray dataset to MultiDataSet.

Description

Usage

Arguments

Value

Examples

Method to add a slot of methylation to MultiDataSet.

Description

Usage

Arguments

Value

Examples

Method to add an expression RNA seq dataset to MultiDataSet.

Description

Usage

Arguments

Value

Examples

Method to add a RangedSummarizedExperiment to MultiDataSet.

Description

Usage

Arguments

Value

Examples

Method to add a SummarizedExperiment to MultiDataSet.

Description

Usage

Arguments

Value

Examples

Method to add a slot of SNPs to MultiDataSet.

Description

Usage

Arguments

Value

Examples

Method to add a matrix to MultiDataSet.

Description

Usage

Arguments

Value

Examples

Convert chr numbers to chr strings

Description

Usage

Arguments

Value

Examples

Get the name of the ids common to all datasets

Description

Usage

Arguments

Value

Examples

Method to select samples that are present in all datasets.

Description

Usage

Arguments

Details

Value

Examples

Method to extrat feature result from a ResultSet

Description

Usage

Arguments

Value

Examples

Lambda Calculation for a vector of P-Values

Description

Usage

Arguments

Method to add an `eSet` to `MultiDataSet`.

Method to add an expression microarray dataset to `MultiDataSet`.

Method to add a slot of methylation to `MultiDataSet`.

Method to add an expression RNA seq dataset to `MultiDataSet`.

Method to add a `RangedSummarizedExperiment` to `MultiDataSet`.

Method to add a `SummarizedExperiment` to `MultiDataSet`.

Method to add a slot of SNPs to `MultiDataSet`.

Method to add a `matrix` to `MultiDataSet`.

Convert a `MultiAssayExperiment` to a `MultiDataSet`

Convert a `MultiDataSet` to a `MultiAssayExperiment`

Example `ResultSet`