| Title: | Analyze Illumina Infinium DNA methylation arrays |
|---|---|
| Description: | Tools to analyze & visualize Illumina Infinium methylation arrays. |
| Authors: | Kasper Daniel Hansen [cre, aut], Martin Aryee [aut], Rafael A. Irizarry [aut], Andrew E. Jaffe [ctb], Jovana Maksimovic [ctb], E. Andres Houseman [ctb], Jean-Philippe Fortin [ctb], Tim Triche [ctb], Shan V. Andrews [ctb], Peter F. Hickey [ctb] |
| Maintainer: | Kasper Daniel Hansen <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.51.0 |
| Built: | 2024-07-27 05:51:24 UTC |
| Source: | https://github.com/bioc/minfi |
Tools for analyzing and visualizing Illumina methylation array data. There is special focus on the 450k array; the 27k array is not supported at the moment.
The package contains a (hopefully) useful vignette; this vignette contains a lengthy description of the package content and capabilities.
Finds blocks (large scale regions) of methylation differences for Illumina methylation arrays
blockFinder(object, design, coef = 2, what = c("Beta", "M"), cluster = NULL, cutoff = NULL, pickCutoff = FALSE, pickCutoffQ = 0.99, nullMethod = c("permutation","bootstrap"), smooth = TRUE, smoothFunction = locfitByCluster, B = ncol(permutations), permutations = NULL, verbose = TRUE, bpSpan = 2.5*10^5,...)blockFinder(object, design, coef = 2, what = c("Beta", "M"), cluster = NULL, cutoff = NULL, pickCutoff = FALSE, pickCutoffQ = 0.99, nullMethod = c("permutation","bootstrap"), smooth = TRUE, smoothFunction = locfitByCluster, B = ncol(permutations), permutations = NULL, verbose = TRUE, bpSpan = 2.5*10^5,...)
object |
An object of class GenomicRatioSet. |
design |
Design matrix with rows representing samples and columns representing covariates. Regression is applied to each row of mat. |
coef |
An integer denoting the column of the design matrix containing the covariate of interest. The hunt for bumps will be only be done for the estimate of this coefficient. |
what |
Should blockfinding be performed on M-values or Beta values? |
cluster |
The clusters of locations that are to be analyzed
together. In the case of microarrays, the clusters are many times
supplied by the manufacturer. If not available the function
|
cutoff |
A numeric value. Values of the estimate of the genomic profile above the cutoff or below the negative of the cutoff will be used as candidate regions. It is possible to give two separate values (upper and lower bounds). If one value is given, the lower bound is minus the value. |
pickCutoff |
Should a cutoff be picked automatically? |
pickCutoffQ |
The quantile used for picking the cutoff using the permutation distribution. |
nullMethod |
Method used to generate null candidate regions, must be one of ‘bootstrap’ or ‘permutation’ (defaults to ‘permutation’). However, if covariates in addition to the outcome of interest are included in the design matrix (ncol(design)>2), the ‘permutation’ approach is not recommended. See vignette and original paper for more information. |
smooth |
A logical value. If TRUE the estimated profile will be smoothed with the
smoother defined by |
smoothFunction |
A function to be used for smoothing the estimate of the genomic
profile. Two functions are provided by the package: |
B |
An integer denoting the number of resamples to use when computing
null distributions. This defaults to 0. If |
permutations |
is a matrix with columns providing indexes to be used to
scramble the data and create a null distribution. If this matrix is not supplied and |
verbose |
Should the function be verbose? |
bpSpan |
Smoothing span. Note that this defaults to a large value becuase we are searching for large scale changes. |
... |
further arguments sent to |
The approximately 170,000 open sea probes on the 450k can be used to detect
long-range changes in methylation status. These large scale changes that can range up
to several Mb have typically been identified only through whole-genome bisulfite
sequencing. blockFinder groups the averaged methylation values in open-sea
probe clusters (See cpgCollapse) into large regions in which the bumphunter
procedure is applied with a large (250KB+) smoothing window.
Note that estimating the precise boundaries of these blocks are constrained by the resolution of the array.
FIXME
cpgCollapse, and bumphunter
bumphunter in Package minfi
Estimate regions for which a genomic profile deviates from its baseline value. Originally implemented to detect differentially methylated genomic regions between two populations, but can be applied to any CpG-level coefficient of interest.
## S4 method for signature 'GenomicRatioSet' bumphunter(object, design, cluster=NULL, coef=2, cutoff=NULL, pickCutoff=FALSE, pickCutoffQ=0.99, maxGap=500, nullMethod=c("permutation","bootstrap"), smooth=FALSE, smoothFunction=locfitByCluster, useWeights=FALSE, B=ncol(permutations), permutations=NULL, verbose=TRUE, type = c("Beta","M"), ...)## S4 method for signature 'GenomicRatioSet' bumphunter(object, design, cluster=NULL, coef=2, cutoff=NULL, pickCutoff=FALSE, pickCutoffQ=0.99, maxGap=500, nullMethod=c("permutation","bootstrap"), smooth=FALSE, smoothFunction=locfitByCluster, useWeights=FALSE, B=ncol(permutations), permutations=NULL, verbose=TRUE, type = c("Beta","M"), ...)
object |
An object of class GenomicRatioSet. |
design |
Design matrix with rows representing samples and columns representing covariates. Regression is applied to each row of mat. |
cluster |
The clusters of locations that are to be analyzed
together. In the case of microarrays, the clusters are many times
supplied by the manufacturer. If not available the function
|
coef |
An integer denoting the column of the design matrix containing the covariate of interest. The hunt for bumps will be only be done for the estimate of this coefficient. |
cutoff |
A numeric value. Values of the estimate of the genomic profile above the cutoff or below the negative of the cutoff will be used as candidate regions. It is possible to give two separate values (upper and lower bounds). If one value is given, the lower bound is minus the value. |
pickCutoff |
Should bumphunter attempt to pick a cutoff using the permutation distribution? |
pickCutoffQ |
The quantile used for picking the cutoff using the permutation distribution. |
maxGap |
If cluster is not provided this maximum location gap
will be used to define cluster via the |
nullMethod |
Method used to generate null candidate regions, must be one of ‘boots trap’ or ‘permutation’ (defaults to ‘permutation’). However, if covariates in addition to the outcome of interest are included in the design matrix (ncol(design)>2), the ‘permutation’ approach is not recommended. See vignette and original paper for more information. |
smooth |
A logical value. If TRUE the estimated profile will be smoothed with the
smoother defined by |
smoothFunction |
A function to be used for smoothing the estimate of the genomic
profile. Two functions are provided by the package: |
useWeights |
A logical value. If |
B |
An integer denoting the number of resamples to use when computing
null distributions. This defaults to 0. If |
permutations |
is a matrix with columns providing indexes to be used to
scramble the data and create a null distribution when
|
verbose |
logical value. If |
type |
Should bumphunting be performed on M-values ("M") or Beta values ("Beta")? |
... |
further arguments to be passed to the smoother functions. |
See help file for bumphunter method in the
bumphunter package for for details.
An object of class bumps with the following components:
tab |
The table with candidate regions and annotation for these. |
coef |
The single loci coefficients. |
fitted |
The estimated genomic profile used to determine the regions. |
pvaluesMarginal |
marginal p-value for each genomic location. |
null |
The null distribution. |
algorithm |
details on the algorithm. |
Rafael A. Irizarry, Martin J. Aryee and Kasper D. Hansen
AE Jaffe, P Murakami, H Lee, JT Leek, MD Fallin, AP Feinberg, and RA Irizarry. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. International Journal of Epidemiology (2012) 41(1):200-209. doi:10.1093/ije/dyr238
if(require(minfiData)) { gmSet <- preprocessQuantile(MsetEx) design <- model.matrix(~ gmSet$status) bumps <- bumphunter(gmSet, design = design, B = 0, type = "Beta", cutoff = 0.25) }if(require(minfiData)) { gmSet <- preprocessQuantile(MsetEx) design <- model.matrix(~ gmSet$status) bumps <- bumphunter(gmSet, design = design, B = 0, type = "Beta", cutoff = 0.25) }
A method for combining different types of methylation arrays into a virtual array. The three generations of Illumina methylation arrays are supported: the 27k, the 450k and the EPIC arrays. Specifically, the 450k array and the EPIC array share many probes in common. This function combines data from the two different array types and outputs a data object of the user-specified type. Essentially, this new object will be like (for example) an EPIC array with many probes missing.
## S4 method for signature 'RGChannelSet,RGChannelSet' combineArrays(object1, object2, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC"), verbose = TRUE) ## S4 method for signature 'MethylSet,MethylSet' combineArrays(object1, object2, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE) ## S4 method for signature 'RatioSet,RatioSet' combineArrays(object1, object2, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE) ## S4 method for signature 'GenomicMethylSet,GenomicMethylSet' combineArrays(object1, object2, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE) ## S4 method for signature 'GenomicRatioSet,GenomicRatioSet' combineArrays(object1, object2, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE)## S4 method for signature 'RGChannelSet,RGChannelSet' combineArrays(object1, object2, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC"), verbose = TRUE) ## S4 method for signature 'MethylSet,MethylSet' combineArrays(object1, object2, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE) ## S4 method for signature 'RatioSet,RatioSet' combineArrays(object1, object2, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE) ## S4 method for signature 'GenomicMethylSet,GenomicMethylSet' combineArrays(object1, object2, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE) ## S4 method for signature 'GenomicRatioSet,GenomicRatioSet' combineArrays(object1, object2, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE)
object1 |
The first object. |
object2 |
The second object. |
outType |
The array type of the output. |
verbose |
Should the function be verbose? |
FIXME: describe the RCChannelSet combination.
The output object has the same class as the two input objects, that is
either an RGChannelSet, a MethylSet, a RatioSet, a
GemomicMethylSet or a GenomicRatioSet, with the type of
the array given by the outType argument.
Jean-Philippe Fortin and Kasper D. Hansen.
if(require(minfiData) && require(minfiDataEPIC)) { data(RGsetEx.sub) data(RGsetEPIC) rgSet <- combineArrays(RGsetEPIC, RGsetEx.sub) rgSet }if(require(minfiData) && require(minfiDataEPIC)) { data(RGsetEx.sub) data(RGsetEPIC) rgSet <- combineArrays(RGsetEPIC, RGsetEx.sub) rgSet }
Estimates A/B compartments as revealed by Hi-C by computing the first eigenvector on a binned probe correlation matrix.
compartments(object, resolution=100*1000, what = "OpenSea", chr="chr22", method = c("pearson", "spearman"), keep=TRUE)compartments(object, resolution=100*1000, what = "OpenSea", chr="chr22", method = c("pearson", "spearman"), keep=TRUE)
object |
An object of class (Genomic)MethylSet or (Genomic)RatioSet |
resolution |
An integer specifying the binning resolution |
what |
Which subset of probes should be used? |
chr |
The chromosome to be analyzed. |
method |
Method of correlation. |
keep |
Should the correlation matrix be stored or not? |
This function extracts A/B compartments from Illumina methylation microarrays. Analysis of Hi-C data has shown that the genome can be divided into two compartments (A/B compartments) that are cell-type specific and are associated with open and closed chromatin respectively. The approximately 170,000 open sea probes on the 450k array can be used to estimate these compartments by computing the first eigenvector on a binned correlation matrix. The binning resolution can be specified by resolution, and by default is set to a 100 kb. We do not recommend higher resolutions because of the low-resolution probe design of the 450k array.
an object of class GRanges containing the correlation matrix, the compartment eigenvector and the compartment labels (A or B) as metadata.
Jean-Philippe Fortin [email protected], Kasper D. Hansen [email protected]
JP Fortin and KD Hansen. Reconstructing A/B compartments as revealed by Hi-C using long-range correlations in epigenetic data. bioRxiv (2015). doi:10.1101/019000.
if (require(minfiData)) { GMset <- mapToGenome(MsetEx) ## compartments at 1MB resolution; we recommend 100kb. comps <- compartments(GMset, res = 10^6) }if (require(minfiData)) { GMset <- mapToGenome(MsetEx) ## compartments at 1MB resolution; we recommend 100kb. comps <- compartments(GMset, res = 10^6) }
Strip plots are produced for each control probe type specified.
controlStripPlot(rgSet, controls = c("BISULFITE CONVERSION I", "BISULFITE CONVERSION II"), sampNames = NULL, xlim = c(5, 17))controlStripPlot(rgSet, controls = c("BISULFITE CONVERSION I", "BISULFITE CONVERSION II"), sampNames = NULL, xlim = c(5, 17))
rgSet |
An |
controls |
A vector of control probe types to plot. |
sampNames |
Sample names to be used for labels. |
xlim |
x-axis limits. |
This function produces the control probe signal plot component of the QC report.
No return value. Plots are produced as a side-effect.
Martin Aryee [email protected].
qcReport, mdsPlot, densityPlot, densityBeanPlot
if (require(minfiData)) { names <- pData(RGsetEx)$Sample_Name controlStripPlot(RGsetEx, controls=c("BISULFITE CONVERSION I"), sampNames=names) }if (require(minfiData)) { names <- pData(RGsetEx)$Sample_Name controlStripPlot(RGsetEx, controls=c("BISULFITE CONVERSION I"), sampNames=names) }
A method for converting a type of methylation array into a array of
another type. The three generations of Illumina methylation arrays are
supported: the 27k, the 450k and the EPIC arrays. Specifically, the
450k array and the EPIC array share many probes in common. For
RGChannelSet, this function will convert an EPIC array into a
450k array (or vice-versa) by dropping probes that differ between the
two arrays. Because most of the probes on the 27k array have a different
chemistry than the 450k and EPIC probes, converting an 27k
RGChannelSet into another array is not supported. Each array can
be converted into another array at the CpG site level, that is any
MethylSet and RatioSet (or GenomicMethylSet and
GenomicRatioSet) can be converted to a 27k, 450k or EPIC array.
The output array is specified by the outType argument.
## S4 method for signature 'RGChannelSet' convertArray(object, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC"), verbose = TRUE) ## S4 method for signature 'MethylSet' convertArray(object, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE) ## S4 method for signature 'RatioSet' convertArray(object, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE) ## S4 method for signature 'GenomicMethylSet' convertArray(object, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE) ## S4 method for signature 'GenomicRatioSet' convertArray(object, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE)## S4 method for signature 'RGChannelSet' convertArray(object, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC"), verbose = TRUE) ## S4 method for signature 'MethylSet' convertArray(object, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE) ## S4 method for signature 'RatioSet' convertArray(object, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE) ## S4 method for signature 'GenomicMethylSet' convertArray(object, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE) ## S4 method for signature 'GenomicRatioSet' convertArray(object, outType = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), verbose = TRUE)
object |
The input object. |
outType |
The array type of the output. |
verbose |
Should the function be verbose? |
FIXME: describe the RGChannelSet conversion.
The output object has the same class as the input object, that is
either an RGChannelSet, a MethylSet, a RatioSet, a
GemomicMethylSet or a GenomicRatioSet, with the type of
the array given by the outType argument.
Jean-Philippe Fortin and Kasper D. Hansen.
if(require(minfiData)) { data(RGsetEx.sub) rgSet <- convertArray(RGsetEx.sub, outType = "IlluminaHumanMethylationEPIC") rgSet }if(require(minfiData)) { data(RGsetEx.sub) rgSet <- convertArray(RGsetEx.sub, outType = "IlluminaHumanMethylationEPIC") rgSet }
This function groups adjacent loci into clusters with a specified maximum gap between CpGs in the cluster, and a specified maximum cluster width. The loci within each cluster are summarized resulting in a single methylation estimate per cluster.
cpgCollapse(object, what = c("Beta", "M"), maxGap = 500, blockMaxGap = 2.5 * 10^5, maxClusterWidth = 1500, dataSummary = colMeans, na.rm = FALSE, returnBlockInfo = TRUE, islandAnno = NULL, verbose = TRUE, ...)cpgCollapse(object, what = c("Beta", "M"), maxGap = 500, blockMaxGap = 2.5 * 10^5, maxClusterWidth = 1500, dataSummary = colMeans, na.rm = FALSE, returnBlockInfo = TRUE, islandAnno = NULL, verbose = TRUE, ...)
object |
An object of class |
what |
Should operation be performed on the M-scale or Beta-scale? |
maxGap |
Maximum gap between CpGs in a cluster |
blockMaxGap |
Maximum block gap |
maxClusterWidth |
Maximum cluster width |
dataSummary |
Function used to summarize methylation across CpGs in the cluster. |
na.rm |
Should NAs be removed when summarizing? Passed on to the dataSummary function. |
returnBlockInfo |
Should the block annotation table be returned in addition to the block table? |
islandAnno |
Which Island annotation should be used. |
verbose |
Should the function be verbose? |
... |
Passed on to getMethSignal and getCN. Can be used to specify |
This function is used as the first step of block-finding. It groups adjacent loci into clusters with a default maximum gap of 500bp and a maximum cluster width of 1,500bp. The loci within each cluster are then summarized (using the mean by default) resulting in a single methylation estimate per cluster. Cluster estimates from open-sea probes are used in block-finding.
If returnBlockInfo is FALSE: a GenomicRatioSet of collapsed CpG clusters.
If returnBlockInfo is TRUE:
object |
A GenomicRatioSet of collapsed CpG clusters |
blockInfo |
A cluster annotation data frame |
Rafael Irizarry
Density ‘bean’ plots of methylation Beta values, primarily for QC.
densityBeanPlot(dat, sampGroups = NULL, sampNames = NULL, main = NULL, pal = brewer.pal(8, "Dark2"), numPositions = 10000)densityBeanPlot(dat, sampGroups = NULL, sampNames = NULL, main = NULL, pal = brewer.pal(8, "Dark2"), numPositions = 10000)
dat |
An |
sampGroups |
Optional sample group labels. See details. |
sampNames |
Optional sample names. See details. |
main |
Plot title. |
pal |
Color palette. |
numPositions |
The density calculation uses |
This function produces the density bean plot component of the QC
report. If sampGroups is specified, group-specific colors will
be used. For speed reasons the plots are produced using a random
subset of CpG positions. The number of positions used is specified by the
numPositions option.
No return value. Plots are produced as a side-effect.
Martin Aryee [email protected].
P Kampstra. Beanplot: A boxplot alternative for visual comparison of distributions. Journal of Statistical Software 28, (2008). http://www.jstatsoft.org/v28/c01
qcReport, mdsPlot, controlStripPlot, densityPlot
if (require(minfiData)) { names <- pData(RGsetEx)$Sample_Name groups <- pData(RGsetEx)$Sample_Group par(mar=c(5,6,4,2)) densityBeanPlot(RGsetEx, sampNames=names, sampGroups=groups) }if (require(minfiData)) { names <- pData(RGsetEx)$Sample_Name groups <- pData(RGsetEx)$Sample_Group par(mar=c(5,6,4,2)) densityBeanPlot(RGsetEx, sampNames=names, sampGroups=groups) }
Density plots of methylation Beta values, primarily for QC.
densityPlot(dat, sampGroups = NULL, main = "", xlab = "Beta", pal = brewer.pal(8, "Dark2"), xlim, ylim, add = TRUE, legend = TRUE, ...)densityPlot(dat, sampGroups = NULL, main = "", xlab = "Beta", pal = brewer.pal(8, "Dark2"), xlim, ylim, add = TRUE, legend = TRUE, ...)
dat |
An |
sampGroups |
Optional sample group labels. See details. |
main |
Plot title. |
xlab |
x-axis label. |
pal |
Color palette. |
xlim |
x-axis limits. |
ylim |
y-axis limits. |
add |
Start a new plot? |
legend |
Plot legend. |
... |
Additional options to be passed to the plot command. |
This function produces the density plot component of the QC report. If sampGroups is specified, group-specific colors will be used.
No return value. Plots are produced as a side-effect.
Martin Aryee [email protected].
qcReport, mdsPlot, controlStripPlot, densityBeanPlot
if (require(minfiData)) { groups <- pData(RGsetEx)$Sample_Group densityPlot(RGsetEx, sampGroups=groups) }if (require(minfiData)) { groups <- pData(RGsetEx)$Sample_Group densityPlot(RGsetEx, sampGroups=groups) }
This function identifies failed positions defined as both the methylated and unmethylated channel reporting background signal levels.
detectionP(rgSet, type = "m+u")detectionP(rgSet, type = "m+u")
rgSet |
An |
type |
How to calculate p-values. Only |
A detection p-value is returned for every genomic position in every sample. Small p-values indicate a good position. Positions with non-significant p-values (typically >0.01) should not be trusted.
The m+u method compares the total DNA signal (Methylated +
Unmethylated) for each position to the background signal level. The
background is estimated using negative control positions, assuming a
normal distribution. Calculations are performed on the original
(non-log) scale.
This function is different from the detection routine in Genome Studio.
A matrix with detection p-values.
Martin Aryee [email protected].
if (require(minfiData)) { detP <- detectionP(RGsetEx.sub) failed <- detP>0.01 colMeans(failed) # Fraction of failed positions per sample sum(rowMeans(failed)>0.5) # How many positions failed in >50% of samples? }if (require(minfiData)) { detP <- detectionP(RGsetEx.sub) failed <- detP>0.01 colMeans(failed) # Fraction of failed positions per sample sum(rowMeans(failed)>0.5) # How many positions failed in >50% of samples? }
Identify CpGs where methylation is associated with a continuous or categorical phenotype.
dmpFinder(dat, pheno, type = c("categorical", "continuous"), qCutoff = 1, shrinkVar = FALSE)dmpFinder(dat, pheno, type = c("categorical", "continuous"), qCutoff = 1, shrinkVar = FALSE)
dat |
A |
pheno |
The phenotype to be tested for association with methylation. |
type |
Is the phenotype '‘continuous’ or ‘categorical’? |
qCutoff |
DMPs with an FDR q-value greater than this will not be returned. |
shrinkVar |
Should variance shrinkage be used? See details. |
This function tests each genomic position for association between methylation and a phenotype. Continuous phenotypes are tested with linear regression, while an F-test is used for categorical phenotypes.
Variance shrinkage (shrinkVar=TRUE) is recommended when sample
sizes are small (<10). The sample variances are squeezed
by computing empirical Bayes posterior means using the limma
package.
A table with one row per CpG.
Martin Aryee [email protected].
squeezeVar and the limma package in general.
if (require(minfiData)) { grp <- pData(MsetEx)$Sample_Group MsetExSmall <- MsetEx[1:1e4,] # To speed up the example M <- getM(MsetExSmall, type = "beta", betaThreshold = 0.001) dmp <- dmpFinder(M, pheno=grp, type="categorical") sum(dmp$qval < 0.05, na.rm=TRUE) head(dmp) }if (require(minfiData)) { grp <- pData(MsetEx)$Sample_Group MsetExSmall <- MsetEx[1:1e4,] # To speed up the example M <- getM(MsetExSmall, type = "beta", betaThreshold = 0.001) dmp <- dmpFinder(M, pheno=grp, type="categorical") sum(dmp$qval < 0.05, na.rm=TRUE) head(dmp) }
Estimates the relative proportion of pure cell types within a sample. For example, given peripheral blood samples, this function will return the relative proportions of lymphocytes, monocytes, B-cells, and neutrophils.
estimateCellCounts(rgSet, compositeCellType = "Blood", processMethod = "auto", probeSelect = "auto", cellTypes = c("CD8T","CD4T", "NK","Bcell","Mono","Gran"), referencePlatform = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), returnAll = FALSE, meanPlot = FALSE, verbose = TRUE, ...)estimateCellCounts(rgSet, compositeCellType = "Blood", processMethod = "auto", probeSelect = "auto", cellTypes = c("CD8T","CD4T", "NK","Bcell","Mono","Gran"), referencePlatform = c("IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPIC", "IlluminaHumanMethylation27k"), returnAll = FALSE, meanPlot = FALSE, verbose = TRUE, ...)
rgSet |
The input |
compositeCellType |
Which composite cell type is being deconvoluted. Should be one of "Blood", "CordBlood", or "DLPFC". See details. |
processMethod |
How should the user and reference data be processed together?
Default input "auto" will use |
probeSelect |
How should probes be selected to distinguish cell types? Options include "both", which selects an equal number (50) of probes (with F-stat p-value < 1E-8) with the greatest magnitude of effect from the hyper- and hypo-methylated sides, and "any", which selects the 100 probes (with F-stat p-value < 1E-8) with the greatest magnitude of difference regardless of direction of effect. Default input "auto" will use "any" for cord blood and "both" otherwise, in line with previous versions of this function and/or our recommendations. Please see the references for more details. |
cellTypes |
Which cell types, from the reference object, should be we use for the deconvolution? See details. |
referencePlatform |
The platform for the reference dataset; if
the input |
returnAll |
Should the composition table and the normalized user supplied data be return? |
verbose |
Should the function be verbose? |
meanPlot |
Whether to plots the average DNA methylation across the cell-type discrimating probes within the mixed and sorted samples. |
... |
Passed to |
This is an implementaion of the Houseman et al (2012) regression calibration approachalgorithm
to the Illumina 450k microarray for deconvoluting heterogeneous tissue sources like blood.
For example, this function will take an RGChannelSet from a DNA methylation (DNAm)
study of blood, and return the relative proportions of CD4+ and CD8+ T-cells, natural
killer cells, monocytes, granulocytes, and b-cells in each sample.
The function currently supports cell composition estimation for blood, cord blood, and
the frontal cortex, through compositeCellType values of "Blood", "CordBlood", and
"DLPFC", respectively. Packages containing the appropriate reference data should be installed
before running the function for the first time ("FlowSorted.Blood.450k", "FlowSorted.DLPFC.450k",
"FlowSorted.CordBlood.450k"). Each tissue supports the estimation of different cell types, delimited
via the cellTypes argument. For blood, these are "Bcell", "CD4T", "CD8T", "Eos", "Gran",
"Mono", "Neu", and "NK" (though the default value for cellTypes is often sufficient).
For cord blood, these are "Bcell", "CD4T", "CD8T", "Gran", "Mono", "Neu", and "nRBC". For frontal
cortex, these are "NeuN_neg" and "NeuN_pos". See documentation of individual reference packages for
more details.
The meanPlot should be used to check for large batch effects in the data,
reducing the confidence placed in the composition estimates. This plot
depicts the average DNA methylation across the cell-type discrimating probes
in both the provided and sorted data. The means from the provided
heterogeneous samples should be within the range of the sorted samples.
If the sample means fall outside the range of the sorted means,
the cell type estimates will inflated to the closest cell type. Note that we
quantile normalize the sorted data with the provided data to reduce these
batch effects.
Matrix of composition estimates across all samples and cell types.
If returnAll=TRUE a list of a count matrix (see previous
paragraph), a composition table and the normalized user data in form
of a GenomicMethylSet.
Andrew E. Jaffe, Shan V. Andrews, E. Andres Houseman
EA Houseman, WP Accomando, DC Koestler, BC Christensen, CJ Marsit, HH Nelson, JK Wiencke and KT Kelsey. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC bioinformatics (2012) 13:86. doi:10.1186/1471-2105-13-86.
AE Jaffe and RA Irizarry. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biology (2014) 15:R31. doi:10.1186/gb-2014-15-2-r31.
KM Bakulski, JI Feinberg, SV Andrews, J Yang, S Brown, S McKenney, F Witter, J Walston, AP Feinberg, and MD Fallin. DNA methylation of cord blood cell types: Applications for mixed cell birth studies. Epigenetics (2016) 11:5. doi:10.1080/15592294.2016.1161875.
preprocessQuantile and convertArray.
## Not run: if(require(FlowSorted.Blood.450k)) { wh.WBC <- which(FlowSorted.Blood.450k$CellType == "WBC") wh.PBMC <- which(FlowSorted.Blood.450k$CellType == "PBMC") RGset <- FlowSorted.Blood.450k[, c(wh.WBC, wh.PBMC)] ## The following line is purely to work around an issue with repeated ## sampleNames and Biobase::combine() sampleNames(RGset) <- paste(RGset$CellType, c(seq(along = wh.WBC), seq(along = wh.PBMC)), sep = "_") counts <- estimateCellCounts(RGset, meanPlot = FALSE) round(counts, 2) } ## End(Not run)## Not run: if(require(FlowSorted.Blood.450k)) { wh.WBC <- which(FlowSorted.Blood.450k$CellType == "WBC") wh.PBMC <- which(FlowSorted.Blood.450k$CellType == "PBMC") RGset <- FlowSorted.Blood.450k[, c(wh.WBC, wh.PBMC)] ## The following line is purely to work around an issue with repeated ## sampleNames and Biobase::combine() sampleNames(RGset) <- paste(RGset$CellType, c(seq(along = wh.WBC), seq(along = wh.PBMC)), sep = "_") counts <- estimateCellCounts(RGset, meanPlot = FALSE) round(counts, 2) } ## End(Not run)
Methylation outliers (loci with very extreme values of the Meth or Unmeth channel) are identified and fixed (see details).
fixMethOutliers(object, K = -3, verbose = FALSE)fixMethOutliers(object, K = -3, verbose = FALSE)
object |
An object of class |
K |
The number of standard deviations away from the median when defining the outlier cutoff, see details. |
verbose |
Should the function be verbose? |
This function fixes outlying methylation calls in the Meth channel and Unmeth channel separately.
Unlike other types of arrays, all loci on a methylation array ought to measure something (apart from loci on the Y chromosome in a female sample). An outlier is a loci with a very low value in one of the two methylation channels. Typically, relatively few loci ought to be outliers.
An outlier is defined in a sample and methylation channel specific
way. First the (sample, methylation channel) values are
log2(x+0.5) transformed and then the median and mad of these
values are computed. An outlier is then defined to be any value less
than the median plus K times the mad, and these outlier values
are thresholded at the cutoff (on the original scale).
An object of the same class as object where outlier values in
the methylation channels have been thresholded.
Rafael A. Irizarry and Kasper D. Hansen
if(require(minfiData)) { MsetEx <- fixMethOutliers(MsetEx) }if(require(minfiData)) { MsetEx <- fixMethOutliers(MsetEx) }
This function finds probes in the Illumina 450k Array for which calculated beta values cluster into distinct groups separated by a defined threshold. It identifies, for these ‘gaps signals’ the number of groups, the size of these groups, and the samples in each group.
gaphunter(object, threshold=0.05, keepOutliers=FALSE, outCutoff=0.01, verbose=TRUE)gaphunter(object, threshold=0.05, keepOutliers=FALSE, outCutoff=0.01, verbose=TRUE)
object |
An object of class (Genomic)RatioSet, (Genomic)MethylSet, or matrix. If one of the first two, codegetBeta is used to calculate beta values. If a matrix, must be one of beta values. |
threshold |
The difference in consecutive, ordered beta values that defines the presence of a gap signal. Defaults to 5 percent. |
keepOutliers |
Should outlier-driven gap signals be kept in the
results? Defaults to |
outCutoff |
Value used to identify gap signals driven by outliers. Defined as the percentage of the total sample size; the sum of samples in all groups except the largest must exceed this number of samples in order for the probe to still be considered a gap signal. Defaults to 1 percent. |
verbose |
logical value. If |
The function can calculate a beta matrix or utilize a user-supplied matrix of beta values.
The function will idenfity probes with a gap in a beta signal greater than
or equal to the defined threshold. These probes constitue an additional,
dataset-specific subset of probes that merit special consideration due to their tendency
to be driven by an underlying SNP or other genetic variant. In this manner, these probes
can serve as surrogates for underlying genetic signal locally and/or in a broader
(i.e. haplotype) context. Please see our upcoming manuscript for a detailed description
of the utility of these probes.
Outlier-driven gap signals are those in which the sum of the smaller group(s) does not exceed a certain percentage of the sample size, defined by the argument outCutoff.
A list with three values,
proberesults |
A data frame listing, for each identified gap signal, the number of groups and the size of each group. |
sampleresults |
a matrix of dimemsions probes (rows) by samples (columns). Individuals are assigned numbers based onthe groups into which they cluster. Lower number groups indicate lower mean methylation values for the group. For example, individuals coded as ‘1’ will have a lower mean methylation value than those individuals coded as ‘2’. |
algorithm |
A list detailing the arguments supplied to the function. |
Shan V. Andrews [email protected].
SV Andrews, C Ladd-Acosta, AP Feinberg, KD Hansen, MD Fallin. ‘Gap hunting’ to characterize clustered probe signals in Illumina methylation array data. Epigenetics & Chromatin (2016) 9:56. doi:10.1186/s13072-016-0107-z.
if(require(minfiData)) { gapres <- gaphunter(MsetEx.sub, threshold=0.3, keepOutliers=TRUE) #Note: the threshold argument is increased from the default value in this small example #dataset with 6 people to avoid the reporting of a large amount of probes as gap signals. #In a typical EWAS setting with hundreds of samples, the default arguments should be #sufficient. }if(require(minfiData)) { gapres <- gaphunter(MsetEx.sub, threshold=0.3, keepOutliers=TRUE) #Note: the threshold argument is increased from the default value in this small example #dataset with 6 people to avoid the reporting of a large amount of probes as gap signals. #In a typical EWAS setting with hundreds of samples, the default arguments should be #sufficient. }
This class holds preprocessed data for Illumina methylation microarrays, mapped to a genomic location.
## Constructor GenomicMethylSet(gr = GRanges(), Meth = new("matrix"), Unmeth = new("matrix"), annotation = "", preprocessMethod = "", ...) ## Data extraction / Accessors ## S4 method for signature 'GenomicMethylSet' getMeth(object) ## S4 method for signature 'GenomicMethylSet' getUnmeth(object) ## S4 method for signature 'GenomicMethylSet' getBeta(object, type = "", offset = 0, betaThreshold = 0) ## S4 method for signature 'GenomicMethylSet' getM(object, type = "", ...) ## S4 method for signature 'GenomicMethylSet' getCN(object, ...) ## S4 method for signature 'GenomicMethylSet' pData(object) ## S4 method for signature 'GenomicMethylSet' sampleNames(object) ## S4 method for signature 'GenomicMethylSet' featureNames(object) ## S4 method for signature 'GenomicMethylSet' annotation(object) ## S4 method for signature 'GenomicMethylSet' preprocessMethod(object) ## S4 method for signature 'GenomicMethylSet' mapToGenome(object, ...)## Constructor GenomicMethylSet(gr = GRanges(), Meth = new("matrix"), Unmeth = new("matrix"), annotation = "", preprocessMethod = "", ...) ## Data extraction / Accessors ## S4 method for signature 'GenomicMethylSet' getMeth(object) ## S4 method for signature 'GenomicMethylSet' getUnmeth(object) ## S4 method for signature 'GenomicMethylSet' getBeta(object, type = "", offset = 0, betaThreshold = 0) ## S4 method for signature 'GenomicMethylSet' getM(object, type = "", ...) ## S4 method for signature 'GenomicMethylSet' getCN(object, ...) ## S4 method for signature 'GenomicMethylSet' pData(object) ## S4 method for signature 'GenomicMethylSet' sampleNames(object) ## S4 method for signature 'GenomicMethylSet' featureNames(object) ## S4 method for signature 'GenomicMethylSet' annotation(object) ## S4 method for signature 'GenomicMethylSet' preprocessMethod(object) ## S4 method for signature 'GenomicMethylSet' mapToGenome(object, ...)
object |
A |
gr |
A |
Meth |
A matrix of methylation values (between zero and infinity) with each row being a methylation loci and each column a sample. |
Unmeth |
See the |
annotation |
An annotation character string. |
preprocessMethod |
A preprocess method character string. |
type |
How are the values calculated? For |
offset |
Offset in the beta ratio, see detail. |
betaThreshold |
Constrains the beta values to be in the inverval
betwen |
... |
For the constructor, additional arguments to be passed to
|
For a detailed discussion of getBeta and getM see the
deatils section of MethylSet.
An object of class GenomicMethylSet for the constructor.
Instances are constructed using the GenomicMethylSet function with the
arguments outlined above.
A number of useful accessors are inherited from
RangedSummarizedExperiment.
In the following code, object is a GenomicMethylSet.
getMeth(object), getUnmeth(object)
Get the Meth or Unmeth matrix.
getBeta(object)Get Beta, see details.
getM(object)get M-values, see details.
getCN(object)get copy number values which are defined as the sum of the methylation and unmethylation channel.
getManifest(object)get the manifest associated with the object.
sampleNames(object), featureNames(object)
Get the sampleNames (colnames) or the featureNames (rownames).
preprocessMethod(object),
annotation(object)
Get the preprocess method or annotation
character.
mapToGenome(object) Since object is already
mapped to the genome, this method simply returns object
unchanged.
combine:Combines two different GenomicMethylSet,
eventually using the cbind method for SummarizedExperiment.
Kasper Daniel Hansen [email protected]
RangedSummarizedExperiment in the
SummarizedExperiment package for the basic class structure.
Objects of this class are typically created by using the function
mapToGenome on a MethylSet.
showClass("GenomicMethylSet")showClass("GenomicMethylSet")
This class holds preprocessed data for Illumina methylation microarrays, mapped to a genomic location.
## Constructor GenomicRatioSet(gr = GRanges(), Beta = NULL, M = NULL, CN = NULL, annotation = "", preprocessMethod = "", ...) ## Data extraction / Accessors ## S4 method for signature 'GenomicRatioSet' getBeta(object) ## S4 method for signature 'GenomicRatioSet' getM(object) ## S4 method for signature 'GenomicRatioSet' getCN(object) ## S4 method for signature 'GenomicRatioSet' pData(object) ## S4 method for signature 'GenomicRatioSet' sampleNames(object) ## S4 method for signature 'GenomicRatioSet' featureNames(object) ## S4 method for signature 'GenomicRatioSet' annotation(object) ## S4 method for signature 'GenomicRatioSet' preprocessMethod(object) ## S4 method for signature 'GenomicRatioSet' mapToGenome(object, ...)## Constructor GenomicRatioSet(gr = GRanges(), Beta = NULL, M = NULL, CN = NULL, annotation = "", preprocessMethod = "", ...) ## Data extraction / Accessors ## S4 method for signature 'GenomicRatioSet' getBeta(object) ## S4 method for signature 'GenomicRatioSet' getM(object) ## S4 method for signature 'GenomicRatioSet' getCN(object) ## S4 method for signature 'GenomicRatioSet' pData(object) ## S4 method for signature 'GenomicRatioSet' sampleNames(object) ## S4 method for signature 'GenomicRatioSet' featureNames(object) ## S4 method for signature 'GenomicRatioSet' annotation(object) ## S4 method for signature 'GenomicRatioSet' preprocessMethod(object) ## S4 method for signature 'GenomicRatioSet' mapToGenome(object, ...)
object |
A |
gr |
A |
Beta |
A matrix of beta values (optional, see details). |
M |
A matrix of M values (optional, see details). |
CN |
A matrix of copy number values. |
annotation |
An annotation character string. |
preprocessMethod |
A preprocess method character string. |
... |
For the constructor, additional arguments to be passed to
|
This class holds M or Beta values (or both) together
with associated genomic coordinates. It is not possible to get
Meth or Unmeth values from this object. The intention
is to use this kind of object as an analysis end point.
In case one of M or Beta is missing, the other is
computed on the fly. For example, M is computed from Beta as the
logit (base 2) of the Beta values.
An object of class GenomicRatioSet for the constructor.
Instances are constructed using the GenomicRatioSet function with the
arguments outlined above.
A number of useful accessors are inherited from
RangedSummarizedExperiment.
In the following code, object is a GenomicRatioSet.
getBeta(object)Get Beta, see details.
getM(object)get M-values, see details.
getCN(object)get copy number, see details.
getManifest(object)get the manifest associated with the object.
sampleNames(object), featureNames(object)
Get the sampleNames (colnames) or the featureNames (rownames).
preprocessMethod(object),
annotation(object)
Get the preprocess method or annotation
character.
mapToGenome(object) Since object is already
mapped to the genome, this method simply returns object
unchanged.
combine:Combines two different GenomicRatioSet,
eventually using the cbind method for SummarizedExperiment.
Kasper Daniel Hansen [email protected]
RangedSummarizedExperiment in the
SummarizedExperiment package for the basic class structure.
showClass("GenomicRatioSet")showClass("GenomicRatioSet")
These functions access provided annotation for various Illumina methylation objects.
getAnnotation(object, what = "everything", lociNames = NULL, orderByLocation = FALSE, dropNonMapping = FALSE) getLocations(object, mergeManifest = FALSE, orderByLocation = FALSE, lociNames = NULL) getAnnotationObject(object) getSnpInfo(object, snpAnno = NULL) addSnpInfo(object, snpAnno = NULL) dropLociWithSnps(object, snps = c("CpG", "SBE"), maf = 0, snpAnno = NULL) getProbeType(object, withColor = FALSE) getIslandStatus(object, islandAnno = NULL)getAnnotation(object, what = "everything", lociNames = NULL, orderByLocation = FALSE, dropNonMapping = FALSE) getLocations(object, mergeManifest = FALSE, orderByLocation = FALSE, lociNames = NULL) getAnnotationObject(object) getSnpInfo(object, snpAnno = NULL) addSnpInfo(object, snpAnno = NULL) dropLociWithSnps(object, snps = c("CpG", "SBE"), maf = 0, snpAnno = NULL) getProbeType(object, withColor = FALSE) getIslandStatus(object, islandAnno = NULL)
object |
A |
what |
Which annotation objects should be returned? |
lociNames |
Restrict the return values to these loci. |
orderByLocation |
Should the return object be ordered according to genomic location. |
dropNonMapping |
Should loci that do not have a genomic location
associated with it (by being marked as |
mergeManifest |
Should the manifest be merged into the return object? |
snpAnno |
The snp annotation you want to use; |
withColor |
Should the return object have the type I probe color labelled? |
snps |
The type of SNPs used. |
maf |
Minor allelle fraction. |
islandAnno |
Like |
getAnnotation returns requested annotation as a
DataFrame, with each row corresponding to a methylation loci.
If object is of class IlluminaHumanAnnotation no
specific ordering of the return object is imposed. If, on the other
hand, the class of object imposes some natural order on the
return object (ie. if the object is of class
[Genomic](Methyl|Ratio)Set), this order is kept in the return
object. Note that RGChannelSet does not impose a specific
ordering on the methylation loci.
getAnnotationObject returns the annotation object, as opposed
to the annotation the object contains. This is useful for printing
and examining the contents of the object.
getLocations is a convenience function which returns
Locations as a GRanges and which furthermore drops
unmapped loci. A user should not need to call this function, instead
mapToGenome should be used to get genomic coordinates and
granges to return those coordinates.
getSnpInfo is a conevnience function which gets a SNP DataFrame
containing information on which probes contains SNPs
where. addSnpInfo adds this information to the rowRanges
or granges of the object. dropLociWithSnps is a
convenience function for removing loci with SNPs based on their MAF.
To see which options are available for what, simply print the
annotation object, possibly using getAnnotationObject.
For getAnnotation, a DataFrame with the requested
information.
For getAnnotationObject, a IlluminaMethylationAnnotation
object.
For getLocations, a GRanges with the locations.
For getProbeType and getIslandStatus, a character
vector with the requested information.
For getSnpInfo, a DataFrame with the requested
information. For addSnpInfo, an object of the same class as
object but with the SNP information added to the metadata columns
of the granges of the object.
For dropLociWithSnps an object of the same kind as the input,
possibly with fewer loci.
Kasper Daniel Hansen[email protected]
IlluminaMethylationAnnotation for the basic class,
mapToGenome for a better alternative (for users) to
getLocations.
if(require(minfiData)) { table(getIslandStatus(MsetEx)) getAnnotation(MsetEx, what = "Manifest") }if(require(minfiData)) { table(getIslandStatus(MsetEx)) getAnnotation(MsetEx, what = "Manifest") }
Reading Illumina methylation array data from GEO.
getGenomicRatioSetFromGEO(GSE = NULL, path = NULL, array = "IlluminaHumanMethylation450k", annotation = .default.450k.annotation, what = c("Beta", "M"), mergeManifest = FALSE, i = 1)getGenomicRatioSetFromGEO(GSE = NULL, path = NULL, array = "IlluminaHumanMethylation450k", annotation = .default.450k.annotation, what = c("Beta", "M"), mergeManifest = FALSE, i = 1)
GSE |
The GSE ID of the dataset to be downloaded from GEO. |
path |
If data already downloaded, the path with soft files. Either |
array |
Array name. |
annotation |
The feature annotation to be used. This includes the location of features thus depends on genome build. |
what |
Are |
mergeManifest |
Should the Manifest be merged to the final object. |
i |
If the GEO download results in more than one dataset, it pickes entry |
This function downloads data from GEO using
getGEO from the GEOquery package. It then
returns a GenomicRatioSet object. Note that the rs probes
(used for genotyping) are dropped.
A GenomicRatioSet object.
Tim Triche Jr. and Rafael A. Irizarry[email protected].
If the data is already in memor you can use
makeGenomicRatioSetFromMatrix
## Not run: mset=getGenomicRatioSetFromGEO("GSE42752") ## End(Not run)## Not run: mset=getGenomicRatioSetFromGEO("GSE42752") ## End(Not run)
Utility functions operating on objects from the minfi package.
getMethSignal(object, what = c("Beta", "M"), ...)getMethSignal(object, what = c("Beta", "M"), ...)
object |
An object from the minfi package supporting either
|
what |
Which signal is returned. |
... |
Passed to the method described by argument |
A matrix.
Kasper Daniel Hansen [email protected].
if(require(minfiData)) { head(getMethSignal(MsetEx, what = "Beta")) }if(require(minfiData)) { head(getMethSignal(MsetEx, what = "Beta")) }
Estimate sample-specific quality control (QC) for methylation data.
getQC(object) addQC(object, qc) plotQC(qc, badSampleCutoff = 10.5)getQC(object) addQC(object, qc) plotQC(qc, badSampleCutoff = 10.5)
object |
An object of class |
qc |
An object as produced by |
badSampleCutoff |
The cutoff for identifying a bad sample. |
For getQC, a DataFrame with two columns: mMed and
uMed which are the chipwide medians of the Meth and Unmeth
channels.
For addQC, essentially object supplied to the function,
but with two new columns added to the pheno data slot: uMed and
mMed.
Rafael A. Irizarry and Kasper D. Hansen
minfiQC for an all-in-one function.
if(require(minfiData)){ qc <- getQC(MsetEx) MsetEx <- addQC(MsetEx, qc = qc) ## plotQC(qc) }if(require(minfiData)){ qc <- getQC(MsetEx) MsetEx <- addQC(MsetEx, qc = qc) ## plotQC(qc) }
Estimates samples sex based on methylation data.
getSex(object = NULL, cutoff = -2) addSex(object, sex = NULL) plotSex(object, id = NULL)getSex(object = NULL, cutoff = -2) addSex(object, sex = NULL) plotSex(object, id = NULL)
object |
An object of class |
cutoff |
What should the difference in log2 copynumber be between males and females. |
sex |
An optional character vector of sex (with values |
id |
Text used as plotting symbols in the |
Estimation of sex is based on the median values of measurements on the
X and Y chromosomes respectively. If yMed - xMed is
less than cutoff we predict a female, otherwise male.
For getSex, a DataFrame with columns predictedSex
(a character with values M and F), xMed and
yMed, which are the chip-wide medians of measurements on the two
sex chromosomes.
For addSex, an object of the same type as object but
with the output of getSex(object) added to the pheno data.
For plotSex, a plot of xMed vs. yMed, which are the
chip-wide medians of measurements on the two sex chromosomes, coloured by
predictedSex.
Rafael A. Irizarry, Kasper D. Hansen, Peter F. Hickey
if(require(minfiData)) { GMsetEx <- mapToGenome(MsetEx) estSex <- getSex(GMsetEx) GMsetEx <- addSex(GMsetEx, sex = estSex) }if(require(minfiData)) { GMsetEx <- mapToGenome(MsetEx) estSex <- getSex(GMsetEx) GMsetEx <- addSex(GMsetEx, sex = estSex) }
IlluminaMethylationAnnotation
This is a class for representing annotation associated with an
Illumina methylation microarray. Annotation is transient in the sense
that it may change over time, wheres the information stored in the
IlluminaMethylationManifest class only depends on the array design.
## Constructor IlluminaMethylationAnnotation(objectNames, annotation = "", defaults = "", packageName = "") ## Data extraction ## S4 method for signature 'IlluminaMethylationAnnotation' getManifest(object)## Constructor IlluminaMethylationAnnotation(objectNames, annotation = "", defaults = "", packageName = "") ## Data extraction ## S4 method for signature 'IlluminaMethylationAnnotation' getManifest(object)
object |
An object of class
|
annotation |
An annotation |
defaults |
A vector of default choices for
|
objectNames |
a |
packageName |
The name of the package this object will be contained in. |
An object of class IlluminaMethylationAnnotation.
In the following code, object is a IlluminaMethylationAnnotation.
getManifest(object)Get the manifest object associated with the array.
Kasper Daniel Hansen [email protected].
"IlluminaMethylationManifest"
This is a class for representing an Illumina methylation microarray design, ie. the physical location and the probe sequences. This information should be independent of genome build and annotation.
## Constructor IlluminaMethylationManifest(TypeI = DataFrame(), TypeII = DataFrame(), TypeControl = DataFrame(), TypeSnpI = DataFrame(), TypeSnpII = DataFrame(), annotation = "") ## Data extraction ## S4 method for signature 'IlluminaMethylationManifest' getManifest(object) ## S4 method for signature 'character' getManifest(object) getProbeInfo(object, type = c("I", "II", "Control", "I-Green", "I-Red", "SnpI", "SnpII")) getManifestInfo(object, type = c("nLoci", "locusNames")) getControlAddress(object, controlType = c("NORM_A", "NORM_C", "NORM_G", "NORM_T"), asList = FALSE)## Constructor IlluminaMethylationManifest(TypeI = DataFrame(), TypeII = DataFrame(), TypeControl = DataFrame(), TypeSnpI = DataFrame(), TypeSnpII = DataFrame(), annotation = "") ## Data extraction ## S4 method for signature 'IlluminaMethylationManifest' getManifest(object) ## S4 method for signature 'character' getManifest(object) getProbeInfo(object, type = c("I", "II", "Control", "I-Green", "I-Red", "SnpI", "SnpII")) getManifestInfo(object, type = c("nLoci", "locusNames")) getControlAddress(object, controlType = c("NORM_A", "NORM_C", "NORM_G", "NORM_T"), asList = FALSE)
object |
Either an object of class
|
TypeI |
A |
TypeII |
A |
TypeControl |
A |
TypeSnpI |
A |
TypeSnpII |
A |
annotation |
An annotation |
type |
A single character describing what kind of information
should be returned. For |
controlType |
A character vector of control types. |
asList |
If |
An object of class IlluminaMethylationManifest for the constructor.
The data slot contains the following objects: TypeI,
TypeII and TypeControl which are all of class
data.frame, describing the array design.
Methylation loci of type I are measured using two different probes, in
either the red or the green channel. The columns AddressA,
AddresB describes the physical location of the two probes on
the array (with ProbeSeqA, ProbeSeqB giving the probe
sequences), and the column Color describes which color channel
is used.
Methylation loci of type II are measured using a single probe, but with two different color channels. The methylation signal is always measured in the green channel.
In the following code, object is a IlluminaMethylationManifest.
getManifest(object)Get the manifest object.
getProbeInfo(object)Returns a DataFrame
giving the type I, type II or control probes. It is also possible
to get the type I probes measured in the Green or Red channel. This
function ensures that the return object only contains probes which
are part of the input object. In case of a RGChannelSet and
type I probes, both addresses needs to be in the object.
getManifestInfo(object)Get some information about the manifest object (the chip design).
getControlAddress(object)Get the control addresses for control probes of a certain type.
getControlTypes(object)Returns the types and the numbers of control probes of each type.
Kasper Daniel Hansen [email protected].
IlluminaMethylationAnnotation for annotation
information for the array (information depending on a specific genome
build).
if(require(IlluminaHumanMethylation450kmanifest)) { show(IlluminaHumanMethylation450kmanifest) head(getProbeInfo(IlluminaHumanMethylation450kmanifest, type = "I")) head(IlluminaHumanMethylation450kmanifest@data$TypeI) head(IlluminaHumanMethylation450kmanifest@data$TypeII) head(IlluminaHumanMethylation450kmanifest@data$TypeControl) }if(require(IlluminaHumanMethylation450kmanifest)) { show(IlluminaHumanMethylation450kmanifest) head(getProbeInfo(IlluminaHumanMethylation450kmanifest, type = "I")) head(IlluminaHumanMethylation450kmanifest@data$TypeI) head(IlluminaHumanMethylation450kmanifest@data$TypeII) head(IlluminaHumanMethylation450kmanifest@data$TypeControl) }
Utility functions for computing logit and inverse logit in base 2.
logit2(x) ilogit2(x)logit2(x) ilogit2(x)
x |
A numeric vector. |
A numeric vector.
Kasper Daniel Hansen [email protected].
logit2(c(0.25, 0.5, 0.75))logit2(c(0.25, 0.5, 0.75))
Make a GenomicRatioSet from a matrix.
makeGenomicRatioSetFromMatrix(mat, rownames = NULL, pData = NULL, array = "IlluminaHumanMethylation450k", annotation = .default.450k.annotation, mergeManifest = FALSE, what = c("Beta", "M"))makeGenomicRatioSetFromMatrix(mat, rownames = NULL, pData = NULL, array = "IlluminaHumanMethylation450k", annotation = .default.450k.annotation, mergeManifest = FALSE, what = c("Beta", "M"))
mat |
The matrix that will be converted. |
rownames |
The feature IDs associated with the rows of |
pData |
A |
array |
Array name. |
annotation |
The feature annotation to be used. This includes the location of features thus depends on genome build. |
mergeManifest |
Should the Manifest be merged to the final object. |
what |
Are |
Many 450K data is provided as csv files. This function permits you to
convert a matrix of values into the class that is used by functions such
as bumphunter and blockFinder. The rownames of mat
are used to match the 450K array features. Alternatively the rownames
can be supplied directly through rownames.
A GenomicRatioSet object.
Rafael A. Irizarry[email protected].
getGenomicRatioSetFromGEO is similar but reads data from GEO.
mat <- matrix(10,5,2) rownames(mat) <- c( "cg13869341", "cg14008030","cg12045430", "cg20826792","cg00381604") grset <- makeGenomicRatioSetFromMatrix(mat)mat <- matrix(10,5,2) rownames(mat) <- c( "cg13869341", "cg14008030","cg12045430", "cg20826792","cg00381604") grset <- makeGenomicRatioSetFromMatrix(mat)
Mapping Ilumina methylation array data to the genome using an annotation package. Depending on the genome, not all methylation loci may have a genomic position.
## S4 method for signature 'MethylSet' mapToGenome(object, mergeManifest = FALSE) ## S4 method for signature 'MethylSet' mapToGenome(object, mergeManifest = FALSE) ## S4 method for signature 'RGChannelSet' mapToGenome(object, ...)## S4 method for signature 'MethylSet' mapToGenome(object, mergeManifest = FALSE) ## S4 method for signature 'MethylSet' mapToGenome(object, mergeManifest = FALSE) ## S4 method for signature 'RGChannelSet' mapToGenome(object, ...)
object |
Either a |
mergeManifest |
Should the information in the associated manifest
package be merged into the location |
... |
Passed to the method for |
FIXME: details on the MethylSet method.
The RGChannelSet method of this function is a convenience
function: the RGChannelSet is first transformed into a
MethylSet using preprocessRaw. The resulting
MethylSet is then mapped directly to the genome.
This function silently drops loci which cannot be mapped to a genomic position, based on the associated annotation package.
An object of class GenomicMethylSet or GenomicRatioSet.
Kasper Daniel Hansen [email protected]
GenomicMethylSet for the output object and
MethylSet for the input object. Also,
getLocations obtains the genomic locations for a given object.
if (require(minfiData)) { ## MsetEx.sub is a small subset of MsetEx; ## only used for computational speed. GMsetEx.sub <- mapToGenome(MsetEx.sub) }if (require(minfiData)) { ## MsetEx.sub is a small subset of MsetEx; ## only used for computational speed. GMsetEx.sub <- mapToGenome(MsetEx.sub) }
Multi-dimensional scaling (MDS) plots showing a 2-d projection of distances between samples.
mdsPlot(dat, numPositions = 1000, sampNames = NULL, sampGroups = NULL, xlim, ylim, pch = 1, pal = brewer.pal(8, "Dark2"), legendPos = "bottomleft", legendNCol, main = NULL)mdsPlot(dat, numPositions = 1000, sampNames = NULL, sampGroups = NULL, xlim, ylim, pch = 1, pal = brewer.pal(8, "Dark2"), legendPos = "bottomleft", legendNCol, main = NULL)
dat |
An |
numPositions |
Use the |
sampNames |
Optional sample names. See details. |
sampGroups |
Optional sample group labels. See details. |
xlim |
x-axis limits. |
ylim |
y-axis limits. |
pch |
Point type. See |
pal |
Color palette. |
legendPos |
The legend position. See
|
legendNCol |
The number of columns in the legend. See
|
main |
Plot title. |
Euclidean distance is calculated between samples using the
numPositions most variable CpG positions. These distances are then
projected into a 2-d plane using classical multidimensional scaling
transformation.
No return value. Plots are produced as a side-effect.
Martin Aryee [email protected].
I Borg, P Groenen. Modern Multidimensional Scaling: theory and applications (2nd ed.) New York: Springer-Verlag (2005) pp. 207-212. ISBN 0387948457.
http://en.wikipedia.org/wiki/Multidimensional_scaling
qcReport, controlStripPlot,
densityPlot, densityBeanPlot,
par, legend
if (require(minfiData)) { names <- pData(MsetEx)$Sample_Name groups <- pData(MsetEx)$Sample_Group mdsPlot(MsetEx, sampNames=names, sampGroups=groups) }if (require(minfiData)) { names <- pData(MsetEx)$Sample_Name groups <- pData(MsetEx)$Sample_Group mdsPlot(MsetEx, sampNames=names, sampGroups=groups) }
This class holds preprocessed data for Illumina methylation microarrays.
## Constructor MethylSet(Meth = new("matrix"), Unmeth = new("matrix"), annotation = "", preprocessMethod = "", ...) ## Data extraction / Accessors ## S4 method for signature 'MethylSet' getMeth(object) ## S4 method for signature 'MethylSet' getUnmeth(object) ## S4 method for signature 'MethylSet' getBeta(object, type = "", offset = 0, betaThreshold = 0) ## S4 method for signature 'MethylSet' getM(object, type = "", ...) ## S4 method for signature 'MethylSet' getCN(object, ...) ## S4 method for signature 'MethylSet' getManifest(object) ## S4 method for signature 'MethylSet' preprocessMethod(object) ## S4 method for signature 'MethylSet' annotation(object) ## S4 method for signature 'MethylSet' pData(object) ## S4 method for signature 'MethylSet' sampleNames(object) ## S4 method for signature 'MethylSet' featureNames(object) ## Utilities dropMethylationLoci(object, dropRS = TRUE, dropCH = TRUE)## Constructor MethylSet(Meth = new("matrix"), Unmeth = new("matrix"), annotation = "", preprocessMethod = "", ...) ## Data extraction / Accessors ## S4 method for signature 'MethylSet' getMeth(object) ## S4 method for signature 'MethylSet' getUnmeth(object) ## S4 method for signature 'MethylSet' getBeta(object, type = "", offset = 0, betaThreshold = 0) ## S4 method for signature 'MethylSet' getM(object, type = "", ...) ## S4 method for signature 'MethylSet' getCN(object, ...) ## S4 method for signature 'MethylSet' getManifest(object) ## S4 method for signature 'MethylSet' preprocessMethod(object) ## S4 method for signature 'MethylSet' annotation(object) ## S4 method for signature 'MethylSet' pData(object) ## S4 method for signature 'MethylSet' sampleNames(object) ## S4 method for signature 'MethylSet' featureNames(object) ## Utilities dropMethylationLoci(object, dropRS = TRUE, dropCH = TRUE)
object |
A |
Meth |
A matrix of methylation values (between zero and infinity) with each row being a methylation loci and each column a sample. |
Unmeth |
See the |
annotation |
An annotation string, optional. |
preprocessMethod |
A |
type |
How are the values calculated? For |
offset |
Offset in the beta ratio, see detail. |
betaThreshold |
Constrains the beta values to be in the inverval
betwen |
dropRS |
Should SNP probes be dropped? |
dropCH |
Should CH probes be dropped |
... |
For the constructor, additional arguments to be passed to
|
This class inherits from eSet. Essentially the class is a representation of a
Meth matrix and a Unmeth matrix linked to a pData data frame.
In addition, an annotation and a preprocessMethod slot is present. The annotation slot describes the type of array and also which annotation package to use. The preprocessMethod slot describes the kind of preprocessing that resulted in this dataset.
A MethylSet stores meth and Unmeth. From these it is easy to compute Beta
values, defined as
The offset is chosen to avoid dividing with small values. Illumina uses a default of 100. M-values (an unfortunate bad name) are defined as
This formula has problems if either Meth or Unmeth is zero. For this reason, we can use
betaThreshold to make sure Beta is neither 0 nor 1, before taken the logit. What makes
sense for the offset and betaThreshold depends crucially on how the data was
preprocessed. Do not expect the default values to be particular good.
An object of class MethylSet for the constructor.
Instances are constructed using the MethylSet function with the
arguments outlined above.
In the following code, object is a MethylSet.
getMeth(object), getUnmeth(object)
Get the Meth or the Unmeth matrix
getBeta(object)Get Beta, see details.
getM(object)get M-values, see details.
getCN(object)get copy number values which are defined as the sum of the methylation and unmethylation channel.
getManifest(object)get the manifest associated with the object.
preprocessMethod(object)Get the preprocess method character.
In the following code, object is a MethylSet.
dropMethylationLoci(object)A unifed interface to removing methylation loci. You can drop SNP probes (probes that measure SNPs, not probes containing SNPs) or CH probes (non-CpG methylation).
combine:Combines two different MethylSet,
eventually using the combine method for eSet.
Kasper Daniel Hansen [email protected]
eSet for the basic class structure.
Objects of this class are typically created from an
RGChannelSet using preprocessRaw or
another preprocessing function.
showClass("MethylSet")showClass("MethylSet")
These functions are provided now defunct in ‘minfi’.
The following functions are now defunct (not working anymore); use the replacement indicated below:
read.450k: Use read.metharray
read.450k.sheet: Use read.metharray.sheet
read.450k.exp: Use read.metharray.exp
These functions are provided for compatibility with older versions of ‘minfi’ only, and will be defunct at the next release.
No functions are currently deprecated.
The following functions are deprecated and will be made defunct; use the replacement indicated below:
read.450k: read.metharray
read.450k.sheet: read.metharray.sheet
read.450k.exp: read.metharray.exp
This function combines a number of functions into a simple to use, one step QC step/
minfiQC(object, fixOutliers = TRUE, verbose = FALSE)minfiQC(object, fixOutliers = TRUE, verbose = FALSE)
object |
An object of class |
fixOutliers |
Should the function fix outlying observations
(using |
verbose |
Should the function be verbose? |
A number of functions are run sequentially on the object.
First outlier values are thresholded using fixMethOutliers.
Then qc is performed using getQC and then sample specific sex
is estimated using getSex.
A list with two values,
object |
The |
qc |
A |
Kasper D. Hansen
getSex, getQC, fixMethOutliers
if(require(minfiData)) { out <- minfiQC(MsetEx) ## plotQC(out$qc) ## plotSex(out$sex) }if(require(minfiData)) { out <- minfiQC(MsetEx) ## plotQC(out$qc) ## plotSex(out$sex) }
Plot the overall density distribution of beta values and the density distributions of the Infinium I and II probe types.
plotBetasByType(data, probeTypes = NULL, legendPos = "top", colors = c("black", "red", "blue"), main = "", lwd = 3, cex.legend = 1)plotBetasByType(data, probeTypes = NULL, legendPos = "top", colors = c("black", "red", "blue"), main = "", lwd = 3, cex.legend = 1)
data |
A |
probeTypes |
If |
legendPos |
The x and y co-ordinates to be used to position the
legend. They can be specified by keyword or in any way which is
accepted by |
colors |
Colors to be used for the different beta value density distributions. Must be a vector of length 3. |
main |
Plot title. |
lwd |
The line width to be used for the different beta value density distributions. |
cex.legend |
The character expansion factor for the legend text. |
The density distribution of the beta values for a single sample is
plotted. The density distributions of the Infinium I and II probes are
then plotted individually, showing how they contribute to the overall
distribution. This is useful for visualising how using
preprocessSWAN affects the data.
No return value. Plot is produced as a side-effect.
Jovana Maksimovic [email protected].
densityPlot, densityBeanPlot,
par, legend
## Not run: if (require(minfiData)) { Mset.swan <- preprocessSWAN(RGsetEx, MsetEx) par(mfrow=c(1,2)) plotBetasByType(MsetEx[,1], main="Raw") plotBetasByType(Mset.swan[,1], main="SWAN") } ## End(Not run)## Not run: if (require(minfiData)) { Mset.swan <- preprocessSWAN(RGsetEx, MsetEx) par(mfrow=c(1,2)) plotBetasByType(MsetEx[,1], main="Raw") plotBetasByType(Mset.swan[,1], main="SWAN") } ## End(Not run)
Plot single-position (single CpG) methylation values as a function of a categorical or continuous phenotype
plotCpg(dat, cpg, pheno, type = c("categorical", "continuous"), measure = c("beta", "M"), ylim = NULL, ylab = NULL, xlab = "", fitLine = TRUE, mainPrefix = NULL, mainSuffix = NULL)plotCpg(dat, cpg, pheno, type = c("categorical", "continuous"), measure = c("beta", "M"), ylim = NULL, ylab = NULL, xlab = "", fitLine = TRUE, mainPrefix = NULL, mainSuffix = NULL)
dat |
An |
cpg |
A character vector of the CpG position identifiers to be plotted. |
pheno |
A vector of phenotype values. |
type |
Is the phenotype categorical or continuous? |
measure |
Should Beta values or log-ratios (M) be plotted? |
ylim |
y-axis limits. |
ylab |
y-axis label. |
xlab |
x-axis label. |
fitLine |
Fit a least-squares best fit line when using a continuous phenotype. |
mainPrefix |
Text to prepend to the CpG name in the plot main title. |
mainSuffix |
Text to append to the CpG name in the plot main title. |
This function plots methylation values (Betas or log-ratios) at individual CpG loci as a function of a phenotype.
No return value. Plots are produced as a side-effect.
Martin Aryee [email protected].
if (require(minfiData)) { grp <- pData(MsetEx)$Sample_Group cpgs <- c("cg00050873", "cg00212031", "cg26684946", "cg00128718") par(mfrow=c(2,2)) plotCpg(MsetEx, cpg=cpgs, pheno=grp, type="categorical") }if (require(minfiData)) { grp <- pData(MsetEx)$Sample_Group cpgs <- c("cg00050873", "cg00212031", "cg26684946", "cg00128718") par(mfrow=c(2,2)) plotCpg(MsetEx, cpg=cpgs, pheno=grp, type="categorical") }
Functional normalization (FunNorm) is a between-array normalization method for the Illumina Infinium HumanMethylation450 platform. It removes unwanted variation by regressing out variability explained by the control probes present on the array.
preprocessFunnorm(rgSet, nPCs=2, sex = NULL, bgCorr = TRUE, dyeCorr = TRUE, keepCN = TRUE, ratioConvert = TRUE, verbose = TRUE)preprocessFunnorm(rgSet, nPCs=2, sex = NULL, bgCorr = TRUE, dyeCorr = TRUE, keepCN = TRUE, ratioConvert = TRUE, verbose = TRUE)
rgSet |
An object of class |
nPCs |
Number of principal components from the control probes PCA |
sex |
An optional numeric vector containing the sex of the samples. |
bgCorr |
Should the NOOB background correction be done, prior to
functional normalization (see |
dyeCorr |
Should dye normalization be done as part of the NOOB
background correction (see |
keepCN |
Should copy number estimates be kept around? Setting to
|
ratioConvert |
Should we run |
verbose |
Should the function be verbose? |
This function implements functional normalization preprocessing for
Illumina methylation microarrays. Functional normalization extends the
idea of quantile normalization by adjusting for known covariates
measuring unwanted variation. For the 450k array, the first k principal
components of the internal control probes matrix play the role of the
covariates adjusting for technical variation. The number k of principal
components can be set by the argument nPCs. By default
nPCs is set to 2, and have been shown to perform consistently
well across different datasets. This parameter should only be modified
by expert users. The normalization procedure is applied to the Meth and
Unmeth intensities separately, and to type I and type II signals
separately. For the probes on the X and Y chromosomes we normalize males
and females separately using the gender information provided in the
sex argument. For the Y chromosome, standard quantile
normalization is used due to the small number of probes, which results
in instability for functional normalization. If sex is unspecified
(NULL), a guess is made using by the getSex function using
copy number information. Note that this algorithm does not rely on any
assumption and therefore can be be applicable for cases where global
changes are expected such as in cancer-normal comparisons or tissue
differences.
an object of class GenomicRatioSet, unless
ratioConvert=FALSE in which case an object of class
GenomicMethylSet.
Jean-Philippe Fortin [email protected], Kasper D. Hansen [email protected].
JP Fortin, A Labbe, M Lemire, BW Zanke, TJ Hudson, EJ Fertig, CMT Greenwood and KD Hansen. Functional normalization of 450k methylation array data improves replication in large cancer studies. (2014) Genome Biology (2014) 15:503. doi:10.1186/s13059-014-0503-2.
RGChannelSet
as well as IlluminaMethylationManifest for the
basic classes involved in these functions.
preprocessRaw and preprocessQuantile are
other preprocessing functions. Background correction may be done using
preprocessNoob.
if (require(minfiData)) { ## RGsetEx.sub is a small subset of RGsetEx; ## only used for computational speed. Mset.sub.funnorm <- preprocessFunnorm(RGsetEx.sub) }if (require(minfiData)) { ## RGsetEx.sub is a small subset of RGsetEx; ## only used for computational speed. Mset.sub.funnorm <- preprocessFunnorm(RGsetEx.sub) }
These functions implements preprocessing for Illumina methylation microarrays as used in Genome Studio, the standard software provided by Illumina.
preprocessIllumina(rgSet, bg.correct = TRUE, normalize = c("controls", "no"), reference = 1) bgcorrect.illumina(rgSet) normalize.illumina.control(rgSet, reference = 1)preprocessIllumina(rgSet, bg.correct = TRUE, normalize = c("controls", "no"), reference = 1) bgcorrect.illumina(rgSet) normalize.illumina.control(rgSet, reference = 1)
rgSet |
An object of class |
bg.correct |
logical, should background correction be performed? |
normalize |
logical, should (control) normalization be performed? |
reference |
for control normalization, which array is the reference? |
We have reverse engineered the preprocessing methods from Genome Studio, based on the documentation.
The current implementation of control normalization is equal to what Genome Studio provides (this statement is based on comparing Genome Studio output to the output of this function), with the following caveat: this kind of normalization requires the selection of a reference array. It is unclear how Genome Studio selects the reference array, but we allow for the manual specification of this parameter.
The current implementation of background correction is roughly equal to Genome Studio. Based on examining the output of 24 arrays, we are able to exactly recreate 18 out of the 24. The remaining 6 arrays had a max discrepancy in the Red and/or Green channel of 1-4 (this is on the unlogged intensity scale, so 4 is very small).
A script for doing this comparison may be found in the scripts
directory (although it is of limited use without the data files).
preprocessIllumina returns a MethylSet, while
bgcorrect.illumina and normalize.illumina.control both
return a RGChannelSet with corrected color channels.
Kasper Daniel Hansen [email protected].
RGChannelSet and MethylSet
as well as IlluminaMethylationManifest for the
basic classes involved in these functions.
preprocessRaw is another basic preprocessing function.
if (require(minfiData)) { dat <- preprocessIllumina(RGsetEx, bg.correct=FALSE, normalize="controls") slot(name="preprocessMethod", dat)[1] }if (require(minfiData)) { dat <- preprocessIllumina(RGsetEx, bg.correct=FALSE, normalize="controls") slot(name="preprocessMethod", dat)[1] }
Noob (normal-exponential out-of-band) is a background correction method with dye-bias normalization for Illumina Infinium methylation arrays.
preprocessNoob(rgSet, offset = 15, dyeCorr = TRUE, verbose = FALSE, dyeMethod=c("single", "reference"))preprocessNoob(rgSet, offset = 15, dyeCorr = TRUE, verbose = FALSE, dyeMethod=c("single", "reference"))
rgSet |
An object of class |
offset |
An offset for the normexp background correction. |
dyeCorr |
Should dye correction be done? |
verbose |
Should the function be verbose? |
dyeMethod |
How should dye bias correction be done: use a single sample approach (ssNoob), or a reference array? |
An object of class MethylSet.
Tim Triche, Jr.
TJ Triche, DJ Weisenberger, D Van Den Berg, PW Laird and KD Siegmund Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res (2013) 41, e90. doi:10.1093/nar/gkt090.
RGChannelSet
as well as IlluminaMethylationManifest for the
basic classes involved in these functions.
preprocessRaw and preprocessQuantile are other preprocessing functions.
if (require(minfiData)) { ## RGsetEx.sub is a small subset of RGsetEx; ## only used for computational speed. MsetEx.sub.noob <- preprocessNoob(RGsetEx.sub) } ## Not run: if (require(minfiData)) { dyeMethods <- c(ssNoob="single", refNoob="reference") GRsets <- lapply(dyeMethods, function(m) preprocessNoob(RGsetEx, dyeMethod=m)) all.equal(getBeta(GRsets$refNoob), getBeta(GRsets$ssNoob)) # TRUE } ## End(Not run)if (require(minfiData)) { ## RGsetEx.sub is a small subset of RGsetEx; ## only used for computational speed. MsetEx.sub.noob <- preprocessNoob(RGsetEx.sub) } ## Not run: if (require(minfiData)) { dyeMethods <- c(ssNoob="single", refNoob="reference") GRsets <- lapply(dyeMethods, function(m) preprocessNoob(RGsetEx, dyeMethod=m)) all.equal(getBeta(GRsets$refNoob), getBeta(GRsets$ssNoob)) # TRUE } ## End(Not run)
Stratified quantile normalization for Illumina amethylation arrays.
This function implements stratified quantile normalization preprocessing for Illumina methylation microarrays. Probes are stratified by region (CpG island, shore, etc.)
preprocessQuantile(object, fixOutliers = TRUE, removeBadSamples = FALSE, badSampleCutoff = 10.5, quantileNormalize = TRUE, stratified = TRUE, mergeManifest = FALSE, sex = NULL, verbose = TRUE)preprocessQuantile(object, fixOutliers = TRUE, removeBadSamples = FALSE, badSampleCutoff = 10.5, quantileNormalize = TRUE, stratified = TRUE, mergeManifest = FALSE, sex = NULL, verbose = TRUE)
object |
An object of class |
fixOutliers |
Should low outlier Meth and Unmeth signals be fixed? |
removeBadSamples |
Should bad samples be removed? |
badSampleCutoff |
Samples with median Meth and Umneth signals below this cutoff will be labelled ‘bad’. |
quantileNormalize |
Should quantile normalization be performed? |
stratified |
Should quantile normalization be performed within genomic region strata (e.g. CpG island, shore, etc.)? |
mergeManifest |
Should the information in the associated manifest package be merged into the output object? |
sex |
Gender |
verbose |
Should the function be verbose? |
This function implements stratified quantile normalization preprocessing
for Illumina methylation microarrays. If removeBadSamples is
TRUE we calculate the median Meth and median Unmeth signal for
each sample, and remove those samples where their average falls below
badSampleCutoff. The normalization procedure is applied to the
Meth and Unmeth intensities separately. The distribution of type I and
type II signals is forced to be the same by first quantile normalizing
the type II probes across samples and then interpolating a reference
distribution to which we normalize the type I probes. Since probe types
and probe regions are confounded and we know that DNAm distributions
vary across regions we stratify the probes by region before applying
this interpolation. For the probes on the X and Y chromosomes we
normalize males and females separately using the gender information
provided in the sex argument. If gender is unspecified
(NULL), a guess is made using by the getSex function using
copy number information. Background correction is not used, but very
small intensities close to zero are thresholded using the
fixMethOutlier. Note that this algorithm relies on the
assumptions necessary for quantile normalization to be applicable and
thus is not recommended for cases where global changes are expected such
as in cancer-normal comparisons.
Note that this normalization procedure is essentially similar to one previously presented (Touleimat and Tost, 2012), but has been independently re-implemented due to the present lack of a released, supported version.
a GenomicRatioSet
A bug in the function was found to affect the Beta values of type I
probes, when stratified=TRUE (default). This is fixed in minfi
version 1.19.7 and 1.18.4 and greater.
Rafael A. Irizarry
N Touleimat and J Tost. Complete pipeline for Infinium Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics (2012) 4:325-341.
getSex, minfiQC,
fixMethOutliers for functions used as part of
preprocessQuantile.
if (require(minfiData)) { # NOTE: RGsetEx.sub is a small subset of RGsetEx; only used for computational # speed GMset.sub.quantile <- preprocessQuantile(RGsetEx.sub) } ## Not run: if(require(minfiData)) { GMset <- preprocessQuantile(RGsetEx) } ## End(Not run)if (require(minfiData)) { # NOTE: RGsetEx.sub is a small subset of RGsetEx; only used for computational # speed GMset.sub.quantile <- preprocessQuantile(RGsetEx.sub) } ## Not run: if(require(minfiData)) { GMset <- preprocessQuantile(RGsetEx) } ## End(Not run)
Converts the Red/Green channel for an Illumina methylation array into methylation signal, without using any normalization.
preprocessRaw(rgSet)preprocessRaw(rgSet)
rgSet |
An object of class |
This function takes the Red and the Green channel of an Illumina
methylation array, together with its associated manifest object and
converts it into a MethylSet containing the methylated and
unmethylated signal.
An object of class MethylSet
Kasper Daniel Hansen[email protected].
RGChannelSet and MethylSet
as well as IlluminaMethylationManifest.
if (require(minfiData)) { dat <- preprocessRaw(RGsetEx) slot(name="preprocessMethod", dat)[1] }if (require(minfiData)) { dat <- preprocessRaw(RGsetEx) slot(name="preprocessMethod", dat)[1] }
Subset-quantile Within Array Normalisation (SWAN) is a within array normalisation method for the Illumina Infinium HumanMethylation450 platform. It allows Infinium I and II type probes on a single array to be normalized together.
preprocessSWAN(rgSet, mSet = NULL, verbose = FALSE)preprocessSWAN(rgSet, mSet = NULL, verbose = FALSE)
rgSet |
An object of class |
mSet |
An optional object of class |
verbose |
Should the function be verbose? |
The SWAN method has two parts. First, an average quantile distribution is created using a subset of probes defined to be biologically similar based on the number of CpGs underlying the probe body. This is achieved by randomly selecting N Infinium I and II probes that have 1, 2 and 3 underlying CpGs, where N is the minimum number of probes in the 6 sets of Infinium I and II probes with 1, 2 or 3 probe body CpGs. If no probes have previously been filtered out e.g. sex chromosome probes, etc. N=11,303. This results in a pool of 3N Infinium I and 3N Infinium II probes. The subset for each probe type is then sorted by increasing intensity. The value of each of the 3N pairs of observations is subsequently assigned to be the mean intensity of the two probe types for that row or “quantile”. This is the standard quantile procedure. The intensities of the remaining probes are then separately adjusted for each probe type using linear interpolation between the subset probes.
an object of class MethylSet
SWAN uses a random subset of probes to do the between array
normalization. In order to achive reproducible results, the seed
needs to be set using set.seed.
Jovana Maksimovic[email protected]
J Maksimovic, L Gordon and A Oshlack (2012). SWAN: Subset quantile Within-Array Normalization for Illumina Infinium HumanMethylation450 BeadChips. Genome Biology 13, R44.
RGChannelSet and
MethylSet as well as
IlluminaMethylationManifest.
if (require(minfiData)) { ## RGsetEx.sub is a small subset of RGsetEx; ## only used for computational speed. MsetEx.sub.swan <- preprocessSWAN(RGsetEx.sub) } ## Not run: if (require(minfiData)) { dat <- preprocessRaw(RGsetEx) preprocessMethod(dat) datSwan <- preprocessSWAN(RGsetEx, mSet = dat) datIlmn <- preprocessIllumina(RGsetEx) preprocessMethod(datIlmn) datIlmnSwan <- preprocessSWAN(RGsetEx, mSet = datIlmn) } ## End(Not run)if (require(minfiData)) { ## RGsetEx.sub is a small subset of RGsetEx; ## only used for computational speed. MsetEx.sub.swan <- preprocessSWAN(RGsetEx.sub) } ## Not run: if (require(minfiData)) { dat <- preprocessRaw(RGsetEx) preprocessMethod(dat) datSwan <- preprocessSWAN(RGsetEx, mSet = dat) datIlmn <- preprocessIllumina(RGsetEx) preprocessMethod(datIlmn) datIlmnSwan <- preprocessSWAN(RGsetEx, mSet = datIlmn) } ## End(Not run)
Produces a PDF QC report for Illumina Infinium Human Methylation 450k arrays, useful for identifying failed samples.
qcReport(rgSet, sampNames = NULL, sampGroups = NULL, pdf = "qcReport.pdf", maxSamplesPerPage = 24, controls = c("BISULFITE CONVERSION I", "BISULFITE CONVERSION II", "EXTENSION", "HYBRIDIZATION", "NON-POLYMORPHIC", "SPECIFICITY I", "SPECIFICITY II", "TARGET REMOVAL"))qcReport(rgSet, sampNames = NULL, sampGroups = NULL, pdf = "qcReport.pdf", maxSamplesPerPage = 24, controls = c("BISULFITE CONVERSION I", "BISULFITE CONVERSION II", "EXTENSION", "HYBRIDIZATION", "NON-POLYMORPHIC", "SPECIFICITY I", "SPECIFICITY II", "TARGET REMOVAL"))
rgSet |
An object of class |
sampNames |
Sample names to be used for labels. |
sampGroups |
Sample groups to be used for labels. |
pdf |
Path and name of the PDF output file. |
maxSamplesPerPage |
Maximum number of samples to plot per page in those sections that plot each sample separately. |
controls |
The control probe types to include in the report. |
This function produces a QC report as a PDF file. It is a useful first step after reading in a new dataset to get an overview of quality and to flag potentially problematic samples.
No return value. A PDF is produced as a side-effect.
Martin Aryee [email protected].
mdsPlot, controlStripPlot, densityPlot, densityBeanPlot
if (require(minfiData)) { names <- pData(RGsetEx)$Sample_Name groups <- pData(RGsetEx)$Sample_Group ## Not run: qcReport(RGsetEx, sampNames=names, sampGroups=groups, pdf="qcReport.pdf") ## End(Not run) }if (require(minfiData)) { names <- pData(RGsetEx)$Sample_Name groups <- pData(RGsetEx)$Sample_Group ## Not run: qcReport(RGsetEx, sampNames=names, sampGroups=groups, pdf="qcReport.pdf") ## End(Not run) }
Converting methylation data from methylation and unmethylation channels, to ratios (Beta and M-values).
## S4 method for signature 'MethylSet' ratioConvert(object, what = c("beta", "M", "both"), keepCN = TRUE, ...) ## S4 method for signature 'GenomicMethylSet' ratioConvert(object, what = c("beta", "M", "both"), keepCN = TRUE, ...)## S4 method for signature 'MethylSet' ratioConvert(object, what = c("beta", "M", "both"), keepCN = TRUE, ...) ## S4 method for signature 'GenomicMethylSet' ratioConvert(object, what = c("beta", "M", "both"), keepCN = TRUE, ...)
object |
Either a |
what |
Which ratios should be computed and stored? |
keepCN |
A logical, should copy number values be computed and stored in the object? |
... |
Passed to |
An object of class RatioSet or GenomicRatioSet.
Kasper Daniel Hansen [email protected]
RatioSet or codeGenomicRatioSet for the output
object and MethylSet or codeGenomicMethylSet
for the input object.
if (require(minfiData)) { ## MsetEx.sub is a small subset of MsetEx; ## only used for computational speed. RsetEx.sub <- ratioConvert(MsetEx.sub, keepCN = TRUE) }if (require(minfiData)) { ## MsetEx.sub is a small subset of MsetEx; ## only used for computational speed. RsetEx.sub <- ratioConvert(MsetEx.sub, keepCN = TRUE) }
This class holds preprocessed data for Illumina methylation microarrays.
## Constructor RatioSet(Beta = NULL, M = NULL, CN = NULL, annotation = "", preprocessMethod = "", ...) ## Data extraction / Accessors ## S4 method for signature 'RatioSet' getBeta(object) ## S4 method for signature 'RatioSet' getM(object) ## S4 method for signature 'RatioSet' getCN(object) ## S4 method for signature 'RatioSet' preprocessMethod(object) ## S4 method for signature 'RatioSet' annotation(object) ## S4 method for signature 'RatioSet' pData(object) ## S4 method for signature 'RatioSet' sampleNames(object) ## S4 method for signature 'RatioSet' featureNames(object)## Constructor RatioSet(Beta = NULL, M = NULL, CN = NULL, annotation = "", preprocessMethod = "", ...) ## Data extraction / Accessors ## S4 method for signature 'RatioSet' getBeta(object) ## S4 method for signature 'RatioSet' getM(object) ## S4 method for signature 'RatioSet' getCN(object) ## S4 method for signature 'RatioSet' preprocessMethod(object) ## S4 method for signature 'RatioSet' annotation(object) ## S4 method for signature 'RatioSet' pData(object) ## S4 method for signature 'RatioSet' sampleNames(object) ## S4 method for signature 'RatioSet' featureNames(object)
object |
A |
Beta |
A matrix of beta values (between zero and one) with each row being a methylation loci and each column a sample. |
M |
A matrix of log-ratios (between minus infinity and infinity) with each row being a methylation loci and each column a sample. |
CN |
An optional matrix of copy number estimates with each row being a methylation loci and each column a sample. |
annotation |
An annotation string, optional. |
preprocessMethod |
A |
... |
For the constructor, additional arguments to be passed to
|
This class inherits from eSet. Essentially the class
is a representation of a Beta matrix and/or a M matrix
and optionally a CN (copy number) matrix linked to a
pData data frame.
In addition, an annotation and a preprocessMethod slot is present. The annotation slot describes the type of array and also which annotation package to use. The preprocessMethod slot describes the kind of preprocessing that resulted in this dataset.
For a RatioSet, M-values are defined as logit2 of the
Beta-values if the M-values are not present in the object. Similarly,
if only M-values are present in the object, Beta-values are
ilogit2 of the M-values.
An object of class RatioSet for the constructor.
Instances are constructed using the RatioSet function with the
arguments outlined above.
In the following code, object is a RatioSet.
getBeta(object), getM(object), CN(object)
Get the Beta, M or CN matrix.
getManifest(object)get the manifest associated with the object.
preprocessMethod(object)Get the preprocess method character.
In the following code, object is a RatioSet.
combine:Combines two different RatioSet,
eventually using the combine method for eSet.
Kasper Daniel Hansen [email protected]
eSet for the basic class structure.
Objects of this class are typically created from an
MethylSet using ratioConvert.
showClass("RatioSet")showClass("RatioSet")
Parsing IDAT files from Illumina methylation arrays.
read.metharray(basenames, extended = FALSE, verbose = FALSE, force = FALSE)read.metharray(basenames, extended = FALSE, verbose = FALSE, force = FALSE)
basenames |
The basenames or filenames of the IDAT files. By
basenames we mean the filename without the ending |
extended |
Should a |
verbose |
Should the function be verbose? |
force |
Should reading different size IDAT files be forced? See Details. |
The type of methylation array is guess by looking at the number of probes in the IDAT files.
We have seen IDAT files from the same array, but with different number
of probes in the wild. Specifically this is the case for early access
EPIC arrays which have fewer probes than final release EPIC arrays.
It is possible to combine IDAT files from the same inferred array, but
with different number of probes, into the same RGChannelSet by
setting force=TRUE. The output object will have the same
number of probes as the smallest array being parsed; effectively
removing probes which could have been analyzed.
An object of class RGChannelSet or
RGChannelSetExtended.
Kasper Daniel Hansen[email protected].
read.metharray.exp for a convenience function for
reading an experiment, read.metharray.sheet for
reading a sample sheet and RGChannelSet for the
output class.
if(require(minfiData)) { baseDir <- system.file("extdata", package = "minfiData") RGset1 <- read.metharray(file.path(baseDir, "5723646052", "5723646052_R02C02")) }if(require(minfiData)) { baseDir <- system.file("extdata", package = "minfiData") RGset1 <- read.metharray(file.path(baseDir, "5723646052", "5723646052_R02C02")) }
Reads an entire methylation array experiment using a sample sheet or (optionally) a target like data.frame.
read.metharray.exp(base = NULL, targets = NULL, extended = FALSE, recursive = FALSE, verbose = FALSE, force = FALSE)read.metharray.exp(base = NULL, targets = NULL, extended = FALSE, recursive = FALSE, verbose = FALSE, force = FALSE)
base |
The base directory. |
targets |
A targets |
extended |
Should the output of the function be a
|
recursive |
Should the search be recursive (see details) |
verbose |
Should the function be verbose? |
force |
Should reading different size IDAT files be forced? See
the documentation for |
If the targets argument is NULL, the function finds all
two-color IDAT files in the directory given by base. If
recursive is TRUE, the function searches base and
all subdirectories. A two-color IDAT files are pair of files with
names ending in _Red.idat or _Grn.idat.
If the targets argument is not NULL it is assumed it has
a columned named Basename, and this is assumed to be pointing
to the base name of a two color IDAT file, ie. a name that can be made
into a real IDAT file by appending either _Red.idat or
_Grn.idat.
The type of methylation array is guess by looking at the number of probes in the IDAT files.
An object of class "RGChannelSet" or
"RGChannelSetExtended".
Kasper Daniel Hansen [email protected].
read.metharray for the workhorse function,
read.metharray.sheet for reading a sample sheet and
RGChannelSet for the output class.
if(require(minfiData)) { baseDir <- system.file("extdata", package = "minfiData") RGset <- read.metharray.exp(file.path(baseDir, "5723646052")) }if(require(minfiData)) { baseDir <- system.file("extdata", package = "minfiData") RGset <- read.metharray.exp(file.path(baseDir, "5723646052")) }
Reading an Illumina methylation sample sheet, containing pheno-data information for the samples in an experiment.
read.metharray.sheet(base, pattern = "csv$", ignore.case = TRUE, recursive = TRUE, verbose = TRUE)read.metharray.sheet(base, pattern = "csv$", ignore.case = TRUE, recursive = TRUE, verbose = TRUE)
base |
The base directory from which the search is started. |
pattern |
What pattern is used to identify a sample sheet file,
see |
ignore.case |
Should the file search be case sensitive? |
recursive |
Should the file search be recursive, see |
verbose |
Should the function be verbose? |
This function search the directory base (possibly including
subdirectories depending on the argument recursive for
“sample sheet” files (see below). These files are identified
solely on the base of their filename given by the arguments
pattern and ignore.case (note the use of a dollarsign to
mean end of file name).
In case multiple sheet files are found, they are all read and the return object will contain the concatenation of the files.
A sample sheet file is essentially a CSV (comma-separated) file
containing one line per sample, with a number of columns describing
pheno-data or other important information about the sample. The file
may contain a header, in which case it is assumed that all lines up to
and including a line starting with \[Data\] should be dropped.
This is modelled after a sample sheet file Illumina provides. It is
also very similar to the targets file made used by the popular
limma package (see the extensive package vignette).
An attempt at guessing the file path to the IDAT files represented in the sheet is made. This should be doublechecked and might need to manually changed.
The type of methylation array is guess by looking at the number of probes in the IDAT files.
A data.frame containing the columns of all the sample sheets.
As described in details, a column named Sentrix_Position is
renamed to Array and Sentrix_ID is renamed to
Slide. In addition the data.frame will contain a column
named Basename.
Kasper Daniel Hansen[email protected].
read.metharray.exp and read.metharray for functions
reading IDAT files. list.files for help on the
arguments recursive and ignore.case.
if(require(minfiData)) { baseDir <- system.file("extdata", package = "minfiData") sheet <- read.metharray.sheet(baseDir) }if(require(minfiData)) { baseDir <- system.file("extdata", package = "minfiData") sheet <- read.metharray.sheet(baseDir) }
Read in Unmethylated and Methylated signals from a GEO raw file.
readGEORawFile(filename, sep = ",", Uname = "Unmethylated signal", Mname = "Methylated signal", row.names = 1, pData = NULL, array = "IlluminaHumanMethylation450k", annotation = .default.450k.annotation, mergeManifest = FALSE, showProgress = TRUE, ...)readGEORawFile(filename, sep = ",", Uname = "Unmethylated signal", Mname = "Methylated signal", row.names = 1, pData = NULL, array = "IlluminaHumanMethylation450k", annotation = .default.450k.annotation, mergeManifest = FALSE, showProgress = TRUE, ...)
filename |
The name of the file to be read from. |
sep |
The field separator character. Values on each line of the file are separated by this character. |
Uname |
A string that uniquely identifies the columns containing the unmethylated signals. |
Mname |
A string that uniquely identifies the columns containing the methylated signals. |
row.names |
The column containing the feature (CpG) IDs. |
pData |
A |
array |
Array name. |
annotation |
The feature annotation to be used. This includes the location of features thus depends on genome build. |
mergeManifest |
Should the Manifest be merged to the final object. |
showProgress |
TRUE displays progress on the console. It is produced in fread's C code. |
... |
Additional arguments passed to |
450K experiments uploaded to GEO typically include a raw data file as
part of the supplementary materials. Unfortunately there does not
appear to be a standard format. This function provides enough
flexibility to read these files. Note that you will likely need to
change the sep, Uname, and Mname arguments and
make sure the first column includes the feature (CpG) IDs. You can use the
readLines function to decipher how to set these
arguments.
Note that the function uses the fread
function in the data.table package to read the data. To install
data.table type install.packages("data.table"). We use this
package because the files too large for read.table.
A GenomicMethylSet object.
Rafael A. Irizarry[email protected].
## Not run: library(GEOquery) getGEOSuppFiles("GSE29290") gunzip("GSE29290/GSE29290_Matrix_Signal.txt.gz") # NOTE: This particular example file uses a comma as the decimal separator # (e.g., 0,00 instead of 0.00). We replace all such instances using the # command line tool 'sed' before reading in the modified file. cmd <- paste0("sed s/,/\./g GSE29290/GSE29290_Matrix_Signal.txt > ", "GSE29290/GSE29290_Matrix_Signal_mod.txt") system(cmd) gmset <- readGEORawFile(filename = "GSE29290/GSE29290_Matrix_Signal_mod.txt", Uname = "Signal_A", Mname = "Signal_B", sep = "\t") ## End(Not run)## Not run: library(GEOquery) getGEOSuppFiles("GSE29290") gunzip("GSE29290/GSE29290_Matrix_Signal.txt.gz") # NOTE: This particular example file uses a comma as the decimal separator # (e.g., 0,00 instead of 0.00). We replace all such instances using the # command line tool 'sed' before reading in the modified file. cmd <- paste0("sed s/,/\./g GSE29290/GSE29290_Matrix_Signal.txt > ", "GSE29290/GSE29290_Matrix_Signal_mod.txt") system(cmd) gmset <- readGEORawFile(filename = "GSE29290/GSE29290_Matrix_Signal_mod.txt", Uname = "Signal_A", Mname = "Signal_B", sep = "\t") ## End(Not run)
Read in tab deliminited file in the TCGA format
readTCGA(filename, sep = "\t", keyName = "Composite Element REF", Betaname = "Beta_value", pData = NULL, array = "IlluminaHumanMethylation450k", annotation = .default.450k.annotation, mergeManifest = FALSE, showProgress = TRUE)readTCGA(filename, sep = "\t", keyName = "Composite Element REF", Betaname = "Beta_value", pData = NULL, array = "IlluminaHumanMethylation450k", annotation = .default.450k.annotation, mergeManifest = FALSE, showProgress = TRUE)
filename |
The name of the file to be read from. |
sep |
The field separator character. Values on each line of the file are separated by this character. |
keyName |
The column name of the field containing the feature IDs. |
Betaname |
The character string contained all column names of the beta value fields. |
pData |
A |
array |
Array name. |
annotation |
The feature annotation to be used. This includes the location of features thus depends on genome build. |
mergeManifest |
Should the Manifest be merged to the final object. |
showProgress |
TRUE displays progress on the console. It is produced in fread's C code. |
This function is a wrapper for
makeGenomicRatioSetFromMatrix. It assumes a very specific
format, used by TCGA, and then uses the fread
function in the data.table package to read the data. To install
data.table type install.packages("data.table"). We use this
package because the files too large for read.table.
Currently, an example of a file that this function reads is here: http://gdac.broadinstitute.org/runs/stddata__2014_10_17/data/UCEC/20141017/gdac.broadinstitute.org_UCEC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.Level_3.2014101700.0.0.tar.gz. Note it is a 8.1 GB archive.
A GenomicRatioSet object.
Rafael A. Irizarry[email protected].
## Not run: filename <- "example.txt" ##file must be in the specicif TCGA format readTCGA(filename) ## End(Not run)## Not run: filename <- "example.txt" ##file must be in the specicif TCGA format readTCGA(filename) ## End(Not run)
"RGChannelSet"
These classes represents raw (unprocessed) data from a two color micro array; specifically an Illumina methylation array.
## Constructors RGChannelSet(Green = new("matrix"), Red = new("matrix"), annotation = "", ...) RGChannelSetExtended(Green = new("matrix"), Red = new("matrix"), GreenSD = new("matrix"), RedSD = new("matrix"), NBeads = new("matrix"), annotation = "", ...) ## Accessors ## S4 method for signature 'RGChannelSet' annotation(object) ## S4 method for signature 'RGChannelSet' pData(object) ## S4 method for signature 'RGChannelSet' sampleNames(object) ## S4 method for signature 'RGChannelSet' featureNames(object) ## S4 method for signature 'RGChannelSet' getBeta(object, ...) getGreen(object) getRed(object) getNBeads(object) ## S4 method for signature 'RGChannelSet' getManifest(object) ## Convenience functions getOOB(object) getSnpBeta(object)## Constructors RGChannelSet(Green = new("matrix"), Red = new("matrix"), annotation = "", ...) RGChannelSetExtended(Green = new("matrix"), Red = new("matrix"), GreenSD = new("matrix"), RedSD = new("matrix"), NBeads = new("matrix"), annotation = "", ...) ## Accessors ## S4 method for signature 'RGChannelSet' annotation(object) ## S4 method for signature 'RGChannelSet' pData(object) ## S4 method for signature 'RGChannelSet' sampleNames(object) ## S4 method for signature 'RGChannelSet' featureNames(object) ## S4 method for signature 'RGChannelSet' getBeta(object, ...) getGreen(object) getRed(object) getNBeads(object) ## S4 method for signature 'RGChannelSet' getManifest(object) ## Convenience functions getOOB(object) getSnpBeta(object)
object |
An |
Green |
A matrix of Green channel values (between zero and infinity) with each row being a methylation loci and each column a sample. |
Red |
See the |
GreenSD |
See the |
RedSD |
See the |
NBeads |
See the |
annotation |
An annotation string, optional. |
... |
For the constructor(s), additional arguments to be passed
to |
An object of class RGChannelSet or RGChannelSetExtended
for the constructors.
Instances are constructed using the
RGChannelSet or RGChannelSetExtended functions with the
arguments outlined above.
as(object, "RGChannelSet") coerces a
RGChannelSetExtended object into a RGChannelSet.
getGreen:Gets the Green channel as a matrix.
getRed:Gets the Red channel as a matrix.
getNBeads:Gets the number of beads as a matrix, this
requires an RGChannelSetExtended.
getManifest:Gets the manifest object itself associated with the array type
getOOB:Retrives the so-called “out-of-band”
(OOB) probes. These are the measurements of Type I probes in the
“wrong” color channel. Return value is a list with two
matrices, named Red and Grn.
getSnpBeta:Retrives the measurements of the 65 SNP probes located on the array. These SNP probes are intended to be used for sample tracking and sample mixups. The return value is a matrix of beta values. Each SNP probe ought to have values clustered around 3 distinct values corresponding to homo-, and hetero-zygotes.
combine:Combines two different RGChannelSet,
eventually using the combine method for eSet.
The class inherits a number of useful methods from SummarizedExperiment. In earliers versions of minfi, this class inherited
from eSet, and we have kept of number of methods related to this, for example pData.
The best way to access phenotype data and sample names are colData and colnames.
Amongst the useful methods are
dim, nrow, ncol
The dimension (number of probes by number of samples) of the experiment.
colData, colnames, pData, sampleNames
Phenotype information and sample names.
rownames, featureNames
This is the addresses (probe identifiers) of the array.
Kasper Daniel Hansen [email protected]
See SummarizedExperiment for the basic class that is used as a
building block for "RGChannelSet(Extended)". See
IlluminaMethylationManifest for a class representing the
design of the array.
showClass("RGChannelSet")showClass("RGChannelSet")
Subset an RGChannelSet by CpG loci.
subsetByLoci(rgSet, includeLoci = NULL, excludeLoci = NULL, keepControls = TRUE, keepSnps = TRUE)subsetByLoci(rgSet, includeLoci = NULL, excludeLoci = NULL, keepControls = TRUE, keepSnps = TRUE)
rgSet |
An object of class |
includeLoci |
A character vector of CpG identifiers which should be kept. |
excludeLoci |
A character vector of CpG identifiers which should be excluded. |
keepControls |
Should control probes be kept? |
keepSnps |
Should SNP probes be kept? |
This task is non-trivial because an RGChannelSet is indexed by
probe position on the array, not by loci name.
An object of class RGChannelSet, which some probes removed.
if(require(minfiData)) { loci <- c("cg00050873", "cg00212031", "cg00213748", "cg00214611") subsetByLoci(RGsetEx.sub, includeLoci = loci) subsetByLoci(RGsetEx.sub, excludeLoci = loci) }if(require(minfiData)) { loci <- c("cg00050873", "cg00212031", "cg00213748", "cg00214611") subsetByLoci(RGsetEx.sub, includeLoci = loci) subsetByLoci(RGsetEx.sub, excludeLoci = loci) }