Package 'MEAL'

Title:	Perform methylation analysis
Description:	Package to integrate methylation and expression data. It can also perform methylation or expression analysis alone. Several plotting functionalities are included as well as a new region analysis based on redundancy analysis. Effect of SNPs on a region can also be estimated.
Authors:	Carlos Ruiz-Arenas [aut, cre], Juan R. Gonzalez [aut]
Maintainer:	Xavier Escribà Montagut <[email protected]>
License:	Artistic-2.0
Version:	1.37.0
Built:	2024-10-30 08:46:39 UTC
Source:	https://github.com/bioc/MEAL

Help Index

Compute signification of RDA test

Description

Compare R2 obtained in our region of interest with the global R^2 and the R^2 of regions with the same number of probes.

Usage

computeRDAR2(
  fullMat,
  varsmodel,
  covarsmodel = NULL,
  featNum,
  R2,
  num_permutations = 1e+05 - 1
)
computeRDAR2(
  fullMat,
  varsmodel,
  covarsmodel = NULL,
  featNum,
  R2,
  num_permutations = 1e+05 - 1
)

Arguments

`fullMat`	Matrix with the whole genome expression or methylation values
`varsmodel`	Matrix with the model
`covarsmodel`	Matrix with the covariables model
`featNum`	Numeric with the number of features of the RDA model
`R2`	Numeric with the R2 of the RDA model
`num_permutations`	Numeric with the number of permutations.

Value

Numeric vector with the probability of finding a region with the same number of probes with a bigger R2 and the global R2.

Computes the correlation between methylation and expression

Description

Estimates the correlation between methylation and expression. When there are known variables that affect methylation and/or expression, their effect can be substracted using a linear model and then the residuals are used.

Usage

correlationMethExprs(
  multiset,
  meth_set_name = NULL,
  exprs_set_name = NULL,
  vars_meth = NULL,
  vars_exprs = NULL,
  sel_cpgs,
  flank = 250000,
  betas = TRUE,
  num_cores = 1,
  verbose = TRUE
)
correlationMethExprs(
  multiset,
  meth_set_name = NULL,
  exprs_set_name = NULL,
  vars_meth = NULL,
  vars_exprs = NULL,
  sel_cpgs,
  flank = 250000,
  betas = TRUE,
  num_cores = 1,
  verbose = TRUE
)

Arguments

`multiset`	`MultiDataSet` containing a `methylation` and an `expression` slots.
`meth_set_name`	Character vector with the name of the `MultiDataSet`'s slot containing methylation data.
`exprs_set_name`	Character vector with the name of the `MultiDataSet`'s slot containing expression data.
`vars_meth`	Character vector with the names of the variables that will be used to obtain the methylation residuals. By default, none is used and residuals are not computed.
`vars_exprs`	Character vector with the names of the variables that will be used to obtain the expression residuals. By default, none is used and residuals are not computed.
`sel_cpgs`	Character vector with the name of the CpGs used in the analysis. If empty, all the CpGs of the methylation set will be used.
`flank`	Numeric with the number of pair bases used to define the cpg-expression probe pairs.
`betas`	If `set` is a `GenomicRatioSet`, should beta values be used? (Default: TRUE)
`num_cores`	Numeric with the number of cores to be used.
`verbose`	Logical value. If TRUE, it writes out some messages indicating progress. If FALSE nothing should be printed.

Details

For each cpg, a range is defined by the position of the cpg plus the flank parameter (upstream and downstream). Only those expression probes that are entirely in this range will be selected. For these reason, it is required that the ExpressionSet contains a featureData with the chromosome and the starting and ending positions of the probes.

Value

Data.frame with the results of the linear regression:

cpg: Name of the cpg
exprs: Name of the expression probe
beta: coefficient of the methylation change
se: standard error of the beta
P.Value: p-value of the beta coefficient
adj.P.Val: q-value computed using B&H

Exports results data.frames to csv files.

Description

Exports results to csv files. If more than one variable is present, subfolders with the name of the variable are created. For each variable, four files will be generated: probeResults.csv, dmrCateResults.csv, bumphunterResults.csv and blockFinderResults.csv

Usage

exportResults(
  object,
  dir = "./",
  prefix = NULL,
  fNames = c("chromosome", "start")
)
exportResults(
  object,
  dir = "./",
  prefix = NULL,
  fNames = c("chromosome", "start")
)

Arguments

`object`	`ResultSet`
`dir`	Character with the path to export.
`prefix`	Character with a prefix to be added to all file names.
`fNames`	Names of the columns of `object` fData that will be added to the results data.frame.

Value

Files are saved into the given folder.

Examples

if (require(minfiData)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
methyOneVar <- runPipeline(set, variable_names = "sex")
exportResults(methyOneVar)
}
if (require(minfiData)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
methyOneVar <- runPipeline(set, variable_names = "sex")
exportResults(methyOneVar)
}

Filter the data.frame obtained from probe analysis

Description

Filter the data.frame obtained from probe analysis

Usage

filterResults(results, range, position = "position", chr = "chromosome")
filterResults(results, range, position = "position", chr = "chromosome")

Arguments

`results`	Data.frame with the results of probe analysis
`range`	`GenomicRanges` with the desired range.
`position`	Character with the name of the column containing the positions
`chr`	Character with the name of the column containing the chromosome

Value

Data.frame with the results of the probes of the range

Get all probes related to a gene

Description

Given a ResultSet and a gene name returns the results of the analysis of all the probes of the gene.

Usage

getGeneVals(
  object,
  gene,
  rid = 1,
  genecol = "genes",
  fNames = c("chromosome", "start"),
  ...
)
getGeneVals(
  object,
  gene,
  rid = 1,
  genecol = "genes",
  fNames = c("chromosome", "start"),
  ...
)

Arguments

`object`	`ResultSet`
`gene`	Character with the name of the gene
`rid`	Name of the results: "DiffMean" for mean differences, "DiffVar" for variance differences. (Default: DiffMean)
`genecol`	Character with the column of `object` fData with the gene information
`fNames`	Names of the columns of `object` fData that will be added to the results data.frame.
`...`	Further arguments passed to `getProbeResults`

Value

data.frame with the results of the analysis of the probes belonging to the gene

Examples

## Not run: 
if (require(minfiData)){
 set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
methyOneVar <- runPipeline(set, variable_names = "sex")
getGeneVals(methyOneVar, "TSPY4")
}

## End(Not run)
## Not run: 
if (require(minfiData)){
 set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
methyOneVar <- runPipeline(set, variable_names = "sex")
getGeneVals(methyOneVar, "TSPY4")
}

## End(Not run)

Obtain probe results from a `ResultSet`

Description

It computes the statistics from the MArrayLM computed with DiffMeanAnalysis or DiffVarAnalysis. This function allows to specify the contrasts and to get F-statistics for a group of variables.

Usage

getProbeResults(
  object,
  rid = "DiffMean",
  coef = 2,
  contrast = NULL,
  fNames = c("chromosome", "start"),
  robust = FALSE,
  ...
)
getProbeResults(
  object,
  rid = "DiffMean",
  coef = 2,
  contrast = NULL,
  fNames = c("chromosome", "start"),
  robust = FALSE,
  ...
)

Arguments

`object`	ResultSet
`rid`	Name of the results: "DiffMean" for mean differences, "DiffVar" for variance differences. (Default: DiffMean)
`coef`	Number of the coefficient used to compute the statistics. If a vector is supplied, F-statistics evaluating the global effect of the coefficients are computed. (Default: 2).
`contrast`	Matrix of contrasts
`fNames`	Names of the columns of `object` fData that will be added to the results data.frame.
`...`	Further arguments passed to `getAssociation`.

Value

data.frame with the probe results.

Get a summary of RDA results

Description

Get statistics from RDA result.

Usage

getRDAresults(object)
getRDAresults(object)

Arguments

object

ResultSet

Value

Numeric vector with the RDA statistics

MEAL (Methylation and Expression AnaLizer): Package for analysing methylation and expression data

Description

MEAL is a package designed to facilitate the analysis methylation and expression data. The package can analyze one dataset and can find correlations between methylation and expression data. MEAL has a vignette that explains the main functionalities of the package.

Defunct functions

Description

These functions are defunct and no longer available.

Details

Defunct functions are: multiCorrMethExprs, DAPipeline, DAProbe, DARegion, RDAset, filterSet, plotBestFeatures, preparePhenotype, createRanges, prepareMethylationSet, calculateRelevantSNPs, correlationMethSNPs, explainedVariance, normalSNP, plotLM

Defunct classes are: analysisRegionResults, analysisResults

Plot values of a feature

Description

Plot values of a feature splitted by one or two variables.

Usage

plotFeature(set, feat, variables = colnames(pheno)[1], betas = TRUE)
plotFeature(set, feat, variables = colnames(pheno)[1], betas = TRUE)

Arguments

`set`	`ExpressionSet`, `GenomicRatioSet` or `SummarizedExperiment.`
`feat`	Numeric with the index of the feature or character with its name.
`variables`	Character vector with the names of the variables to be used in the splitting. Two variables is the maximum allowed.
`betas`	If `set` is a `GenomicRatioSet`, should beta values be used? (Default: TRUE)

Value

A plot is generated on the current graphics device.

Examples

if (require(minfiData)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
 plotFeature(set, 1, variables = "Sample_Group")
 }
if (require(minfiData)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
 plotFeature(set, 1, variables = "Sample_Group")
 }

Plot RDA results

Description

Plot RDA results

Usage

plotRDA(object, pheno = data.frame(), n_feat = 5, main = "RDA plot", alpha = 1)
plotRDA(object, pheno = data.frame(), n_feat = 5, main = "RDA plot", alpha = 1)

Arguments

`object`	`ResultSet`
`pheno`	data.frame with the variables used to color the samples.
`n_feat`	Numeric with the number of cpgs to be highlighted. Default: 5.
`main`	Character with the plot title.
`alpha`	Numeric with the alpha level for colour transparance. Default: 1; no transparency.

Value

A plot is generated on the current graphics device.

Examples

if (require(minfiData)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
model <- model.matrix(~set$sex)
rda <- runRDA(set, model)
plotRDA(rda, pheno = data.frame(factor(set$sex)))
}
if (require(minfiData)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
model <- model.matrix(~set$sex)
rda <- runRDA(set, model)
plotRDA(rda, pheno = data.frame(factor(set$sex)))
}

Plot results in a genomic region

Description

Plot the results from the different analyses of a ResultSet in a specific genomic region. It can plot all the results from runPipeline.

Usage

plotRegion(
  rset,
  range,
  results = names(rset),
  genome = "hg19",
  rset2,
  tPV = 5,
  fNames = c("chromosome", "start", "end"),
  fNames2 = c("chromosome", "start", "end")
)
plotRegion(
  rset,
  range,
  results = names(rset),
  genome = "hg19",
  rset2,
  tPV = 5,
  fNames = c("chromosome", "start", "end"),
  fNames2 = c("chromosome", "start", "end")
)

Arguments

`rset`	`ResultSet`
`range`	`GenomicRanges` with the region coordinates
`results`	Character with the analyses that will be included in the plot. By default, all analyses available are included.
`genome`	String with the genome used to retrieve transcripts annotation: hg19, hg38, mm10. (Default: "hg19")
`rset2`	Additional `ResultSet`
`tPV`	Threshold for P-Value
`fNames`	Names from rset fData
`fNames2`	Names from rset2 fData

Details

This plot allows to have a quick summary of the methylation or gene expression analyses in a given region. If we use a ResultSet obtained from methylation data, transcripts annotation is obtained from archive. If we use a ResultSet obtained from gene expression data, transcripts annotation is taken from fData.

This plot can be used to plot the results of one dataset (methylation or gene expression) or to represent the association between methylation and gene expression data. If only one dataset is used, the p-values and the coefficients of DiffMean and DiffVar analyses are plotted. If we pass two ResultSets, rset should contain methylation results and a rset2 the gene expression results.

Value

Regional plot

Run blockFinder

Description

Run blockFinder

Usage

runBlockFinder(
  set,
  model,
  coefficient = 2,
  blockfinder_cutoff = 0.1,
  num_permutations = 0,
  resultSet = FALSE,
  verbose = FALSE,
  ...
)
runBlockFinder(
  set,
  model,
  coefficient = 2,
  blockfinder_cutoff = 0.1,
  num_permutations = 0,
  resultSet = FALSE,
  verbose = FALSE,
  ...
)

Arguments

`set`	`GenomicRatioSet`, `eSet` derived object or `SummarizedExperiment`
`model`	Model matrix or formula to get model matrix from `set`.
`coefficient`	Numeric with the column of model matrix used in the analysis. (Default: 2)
`blockfinder_cutoff`	Numeric with the minimum cutoff to include a probe in a block. (Default: 0.1)
`num_permutations`	Numeric with the number of permutations run to compute the blocks p-value. (Default: 0)
`resultSet`	Should results be encapsulated in a `resultSet`? (Default: TRUE)
`verbose`	Logical value. Should the function be verbose? (Default: FALSE)
`...`	Further arguments passed to `blockFinder`.

Details

This function has been deprecated and will be defunct in the new version.

Value

data.frame or resultSet with the result of blockFinder

Run bumphunter

Description

Run bumphunter

Usage

runBumphunter(
  set,
  model,
  coefficient = 2,
  bumphunter_cutoff = 0.1,
  num_permutations = 0,
  bumps_max = 30000,
  betas = TRUE,
  check_perms = FALSE,
  verbose = FALSE,
  resultSet = FALSE,
  ...
)
runBumphunter(
  set,
  model,
  coefficient = 2,
  bumphunter_cutoff = 0.1,
  num_permutations = 0,
  bumps_max = 30000,
  betas = TRUE,
  check_perms = FALSE,
  verbose = FALSE,
  resultSet = FALSE,
  ...
)

Arguments

`set`	`GenomicRatioSet`, `eSet` derived object or `SummarizedExperiment`
`model`	Model matrix or formula to get model matrix from `set`.
`coefficient`	Numeric with the column of model matrix used in the analysis. (Default: 2)
`bumphunter_cutoff`	Numeric with the minimum cutoff to include a probe in a block. (Default: 0.1)
`num_permutations`	Numeric with the number of permutations run to compute the bumps p-value. (Default: 0)
`bumps_max`	Numeric with the maximum number of bumps used in the permutation. This parameter only applies when `num_permutations` is greater than 0. (Default: 30000)
`betas`	If `set` is a `GenomicRatioSet`, should beta values be used? (Default: TRUE)
`check_perms`	Logical. Should we check that there are less bumps than `bumps_max`? This parameter only applies when `num_permutations` is greater than 0. (Default: TRUE)
`verbose`	Logical value. Should the function be verbose? (Default: FALSE)
`resultSet`	Should results be encapsulated in a `resultSet`? (Default: TRUE)
`...`	Further arguments passed to `bumphunter`.

Details

This function has been deprecated and will be defunct in the new version.

Value

data.frame or resultSet with the result of bumphunter

Run differential mean analysis

Description

Run differential mean analysis using t-moderated statistics. This function relies on lmFit from limma package.

Usage

runDiffMeanAnalysis(
  set,
  model,
  weights = NULL,
  method = "ls",
  max_iterations = 100,
  betas = TRUE,
  resultSet = TRUE,
  warnings = TRUE
)
runDiffMeanAnalysis(
  set,
  model,
  weights = NULL,
  method = "ls",
  max_iterations = 100,
  betas = TRUE,
  resultSet = TRUE,
  warnings = TRUE
)

Arguments

`set`	Matrix, `GenomicRatioSet`, `SummarizedExperiment` or `ExpressionSet`.
`model`	Model matrix or formula to get model matrix from `set`.
`weights`	weights used in the lmFit model.
`method`	String indicating the method used in the regression: "ls" or "robust". (Default: "ls")
`max_iterations`	Numeric indicating the maximum number of iterations done in the robust method.
`betas`	If `set` is a `GenomicRatioSet`, should beta values be used? (Default: TRUE)
`resultSet`	Should results be encapsulated in a `resultSet`? (Default: TRUE)
`warnings`	Should warnings be displayed? (Default:TRUE)

Value

MArrayLM or resultSet with the result of the differential mean analysis.

Examples

if (require(minfiData)){
 mvalues <- getM(MsetEx)[1:100, ]
 model <- model.matrix(~ Sample_Group, data = pData(MsetEx)) 
 res <- runDiffMeanAnalysis(mvalues, model, method = "ls")
 res
}
if (require(minfiData)){
 mvalues <- getM(MsetEx)[1:100, ]
 model <- model.matrix(~ Sample_Group, data = pData(MsetEx)) 
 res <- runDiffMeanAnalysis(mvalues, model, method = "ls")
 res
}

Run differential variance analysis

Description

Run differential variance analysis. This analysis can only be run with categorical variables. This function relies on varFit from missMethyl package.

Usage

runDiffVarAnalysis(
  set,
  model,
  coefficient = NULL,
  resultSet = TRUE,
  betas = TRUE,
  warnings = TRUE,
  ...
)
runDiffVarAnalysis(
  set,
  model,
  coefficient = NULL,
  resultSet = TRUE,
  betas = TRUE,
  warnings = TRUE,
  ...
)

Arguments

`set`	Matrix, `GenomicRatioSet`, `SummarizedExperiment` or `ExpressionSet`.
`model`	Model matrix or formula to get model matrix from `set`.
`coefficient`	Numeric with the coefficients used to make the groups. If NULL, all possible groups will be computed.
`resultSet`	Should results be encapsulated in a `resultSet`? (Default: TRUE)
`betas`	If `set` is a `GenomicRatioSet`, should beta values be used? (Default: TRUE)
`warnings`	Should warnings be displayed? (Default:TRUE)
`...`	Further arguments passed to `varFit`.

Value

MArrayLM or resultSet with the result of the differential variance analysis.

Examples

if (require(minfiData)){
 mvalues <- getM(MsetEx)[1:100, ]
 model <- model.matrix(~ Sample_Group, data = pData(MsetEx)) 
 res <- runDiffVarAnalysis(mvalues, model)
 res
}
if (require(minfiData)){
 mvalues <- getM(MsetEx)[1:100, ]
 model <- model.matrix(~ Sample_Group, data = pData(MsetEx)) 
 res <- runDiffVarAnalysis(mvalues, model)
 res
}

Run DMRcate

Description

Run DMRcate

Usage

runDMRcate(set, model, coefficient = 2, resultSet = FALSE, ...)
runDMRcate(set, model, coefficient = 2, resultSet = FALSE, ...)

Arguments

`set`	`GenomicRatioSet`, `eSet` derived object or `SummarizedExperiment`
`model`	Model matrix or formula to get model matrix from `set`.
`coefficient`	Numeric with the column of model matrix used in the analysis. (Default: 2)
`resultSet`	Should results be encapsulated in a `resultSet`? (Default: TRUE)
`...`	Further arguments passed to `cpg.annotate` or `dmrcate`.

Details

This function has been deprecated and will be defunct in the new version.

Value

data.frame or resultSet with the result of bumphunter

Perform differential methylation analysis

Description

Wrapper for analysing differential methylation and expression at region and probe level.

Usage

runPipeline(
  set,
  variable_names,
  covariable_names = NULL,
  model = NULL,
  weights = NULL,
  num_vars,
  sva = FALSE,
  betas = TRUE,
  range,
  analyses = c("DiffMean"),
  verbose = FALSE,
  warnings = TRUE,
  DiffMean_params = NULL,
  DiffVar_params = list(coefficient = 1:2),
  rda_params = NULL,
  method = "ls",
  big = FALSE
)
runPipeline(
  set,
  variable_names,
  covariable_names = NULL,
  model = NULL,
  weights = NULL,
  num_vars,
  sva = FALSE,
  betas = TRUE,
  range,
  analyses = c("DiffMean"),
  verbose = FALSE,
  warnings = TRUE,
  DiffMean_params = NULL,
  DiffVar_params = list(coefficient = 1:2),
  rda_params = NULL,
  method = "ls",
  big = FALSE
)

Arguments

`set`	`GenomicRatioSet`, `eSet` derived object or `SummarizedExperiment`
`variable_names`	Character vector with the names of the variables that will be returned as result.
`covariable_names`	Character vector with the names of the variables that will be used to adjust the model.
`model`	Model matrix or formula to get model matrix from `set`.
`weights`	weights used in the lmFit model (default NULL)
`num_vars`	Numeric with the number of variables in the matrix for which the analysis will be performed. Compulsory if equation is not null.
`sva`	Logical. Should Surrogate Variable Analysis be applied? (Default: FALSE)
`betas`	If `set` is a `GenomicRatioSet`, should beta values be used? (Default: TRUE)
`range`	`GenomicRanges` with the region used for RDA
`analyses`	Vector with the names of the analysis to be run (DiffMean and/or DiffVar).
`verbose`	Logical value. If TRUE, it writes out some messages indicating progress. If FALSE nothing should be printed.
`warnings`	Should warnings be displayed? (Default:TRUE)
`DiffMean_params`	List with other parameter passed to `runBumphunter` function.
`DiffVar_params`	List with other parameter passed to `runBumphunter` function.
`rda_params`	List with other parameter passed to `runRDA` function.
`method`	String indicating the method used in the regression: "ls" or "robust". (Default: "ls")
`big`	Logical value indicating whether SmartSVA should be instead of SVA (TRUE recommended for methylation or when having large number of samples). Default is FALSE.

Details

This function is the main wrapper of the package. First, it simplifies the the set to only contain the common samples between phenotype and features. In addition, it allows to change the class of the variables and to apply genomic models (more information on preparePhenotype). Afterwards, analysis per probe and per region are done merging the results in an AnalysisResults object.

Default linear model will contain a sum of the variables and covariables. If interactions are desired, a costum formula can be specified. In that case, variables and covariables must also be specified in order to assure the proper work of the resulting AnalysisResult. In addition, the number of variables of the model for which the calculation will be done must be specified.

Value

ResultSet object

Examples

if (require(minfiData)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
 res <- runPipeline(set, variable_names = "Sample_Group") 
 res
}
if (require(minfiData)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
 res <- runPipeline(set, variable_names = "Sample_Group") 
 res
}

Calculate RDA for a set

Description

Perform RDA calculation for a AnalysisRegionResults. Feature values will be considered the matrix X and phenotypes the matrix Y. Adjusting for covariates is done using a model matrix passed in covarsmodel.

Usage

runRDA(
  set,
  model,
  num_vars = ncol(model),
  range,
  betas = FALSE,
  resultSet = TRUE,
  num_permutations = 10000,
  ...
)
runRDA(
  set,
  model,
  num_vars = ncol(model),
  range,
  betas = FALSE,
  resultSet = TRUE,
  num_permutations = 10000,
  ...
)

Arguments

`set`	`MethylationSet`, `ExpressionSet` or `matrix`
`model`	Model matrix or formula to get model matrix from `set`.
`num_vars`	Numeric with the number of variables in the matrix for which the analysis will be performed. Compulsory if equation is not null.
`range`	`GenomicRanges` with the region used for RDA
`betas`	If `set` is a `GenomicRatioSet`, should beta values be used? (Default: TRUE)
`resultSet`	Should results be encapsulated in a `resultSet`? (Default: TRUE)
`num_permutations`	Numeric with the number of permutations run to compute the p-value. (Default: 1e4)
`...`	Further arguments passed to `rda`.

Value

Object of class rda or resultSet

Examples

if (require(minfiData)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
model <- model.matrix(~set$age)
rda <- runRDA(set, model)
rda
}
if (require(minfiData)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
model <- model.matrix(~set$age)
rda <- runRDA(set, model)
rda
}

Run different DMR detection methods

Description

Run different DMR detection methods

Usage

runRegionAnalysis(
  set,
  model,
  methods = c("blockFinder", "bumphunter", "DMRcate"),
  coefficient = 2,
  bumphunter_params = NULL,
  blockFinder_params = NULL,
  dmrcate_params = NULL,
  verbose = FALSE,
  resultSet = TRUE
)
runRegionAnalysis(
  set,
  model,
  methods = c("blockFinder", "bumphunter", "DMRcate"),
  coefficient = 2,
  bumphunter_params = NULL,
  blockFinder_params = NULL,
  dmrcate_params = NULL,
  verbose = FALSE,
  resultSet = TRUE
)

Arguments

`set`	`GenomicRatioSet`, `eSet` derived object or `SummarizedExperiment`
`model`	Model matrix representing a linear model.
`methods`	Character vector with the names of the methods used to estimate the regions. Valid names are: "blockFinder", "bumphunter" and "DMRcate".
`coefficient`	Numeric with the index of the model matrix used to perform the analysis.
`bumphunter_params`	List with other parameter passed to `runBumphunter` function.
`blockFinder_params`	List with other parameter passed to `runBlockFinder` function.
`dmrcate_params`	List with other parameter passed to `runDMRcate` function.
`verbose`	Logical value. Should the function be verbose? (Default: FALSE)
`resultSet`	Should results be encapsulated in a `resultSet`? (Default: TRUE)

Details

This function has been deprecated and will be defunct in the new version.

Value

List or resultSet with the result of the DMR detection methods.

Examples

if (require(minfiData)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
 model <- model.matrix(~Sample_Group, data = pData(MsetEx))
 res <- runRegionAnalysis(set, model) 
 res
}
if (require(minfiData)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
 model <- model.matrix(~Sample_Group, data = pData(MsetEx))
 res <- runRegionAnalysis(set, model) 
 res
}

Get the top features associated with the RDA

Description

Get a list of the features significantly associated to the first two RDA components

Usage

topRDAhits(object, tPV = 0.05)
topRDAhits(object, tPV = 0.05)

Arguments

`object`	`ResultSet`
`tPV`	numeric with the p-value threshold. Only features with a p-values below this threshold will be shown.

Value

data.frame with the features, the component, the correlation and the p-value

Examples

if (require(minfiData) & require(GenomicRanges)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
model <- model.matrix(~set$sex)
rda <- runRDA(set, model)
topRDAhits(rda)
}
if (require(minfiData) & require(GenomicRanges)){
set <- ratioConvert(mapToGenome(MsetEx[1:10,]))
model <- model.matrix(~set$sex)
rda <- runRDA(set, model)
topRDAhits(rda)
}

Package 'MEAL'

Help Index

Compute signification of RDA test

Description

Usage

Arguments

Value

Computes the correlation between methylation and expression

Description

Usage

Arguments

Details

Value

Exports results data.frames to csv files.

Description

Usage

Arguments

Value

Examples

Filter the data.frame obtained from probe analysis

Description

Usage

Arguments

Value

Get all probes related to a gene

Description

Usage

Arguments

Value

Examples

Obtain probe results from a ResultSet

Description

Usage

Arguments

Value

Get a summary of RDA results

Description

Usage

Arguments

Value

MEAL (Methylation and Expression AnaLizer): Package for analysing methylation and expression data

Description

Defunct functions

Description

Details

Plot values of a feature

Description

Usage

Arguments

Value

Examples

Plot RDA results

Description

Usage

Arguments

Value

Examples

Plot results in a genomic region

Description

Usage

Arguments

Details

Value

Run blockFinder

Description

Usage

Arguments

Details

Value

See Also

Run bumphunter

Description

Usage

Arguments

Details

Value

See Also

Run differential mean analysis

Description

Usage

Obtain probe results from a `ResultSet`