This class holds Illumina 450k methylation microarray data and annotations for CNV calling.
## Constructor CNV450kSet(RGChannelSet)
A class defined in the
This class inherits from
eSet. The class is a representation of an intensity matrix that is computed from the
MethylSet class produced by
RGChannelSet in the
Instances are constructed using the
CNV450kSet function with the arguments outlined above.
This class has a few accessors defined on top of those provided in
eSet. In the following code,
object is a
Gets the manifest associated with the object.
Gets list of segments produced by the
Gets statistics extracted from raw data (
RGChannelSet) and preprocessed methylation data (
MethylSet) that are used internally.
dropSNPprobes(object, maf_threshold = 0)
Removes probes mapping to or targeting known SNPs from consideration. Returns a new CNV450kSet object in which SNP-containing probes are discarded.
normalize(object, type = c("functional", "quantile"))
Normalizes the intensity matrix and returns normalized object. Refer to the vignette for details on the types of normalization.
segmentize(object, verbose = TRUE, p.adjust.method = "bonferroni")
Arranges probes into segments related by signal intensity and proximity by circular binary segmentation. Certain class methods require that segments be created. Refer to the vignette for more details on the algorithm.
computeSignificance(object, p.value.threshold = 0.01, num.mark.threshold = 10)
Computes the significance for each segment in each sample. Requires that
segmentize be called on the object beforehand.
The following methods require that
segmentize be called on the object before their calls.
findCNV(object, gene_names, type = c("gain", "loss", "both"))
Returns a matrix in which
i, j denotes the presence or absence of a CNV event for gene
i and sample
gene_names is a vector containing the gene symbols of interest.
intersectCNV(object, sample_indices, type = c("gain", "loss", "both"))
Returns a vector of gene symbols corresponding to gain or loss of genes within a group, sorted by CNV abundance;
sample_indices is a vector containing the indices of the samples belonging to the group.
subgroupDifference(object, group1_indices, group2_indices)
Returns two vectors (gains and losses) containing Fisher's exact test p-values on gene-based CNV counts between two sample groups (or conditions);
group2_indices are vectors containing the indices of the samples belonging to each respective group.
plotSample(object, index, chr, start, end)
Produces a plot of the genomic segments and relative values for sample at
chr, start ,end are optional parameters to be used to zoom in a specific genomic location.
plotDensity(object, color.by = c("array.row", "array.col", "sample.group", "slide", "origin"),\ color.function = rainbow, legend.position = "topright")
Plots the density distribution of the intensity matrix of the object.
plotPCA(object, color.by = c("array.row", "array.col", "sample.group", "slide", "origin"),\ color.function = rainbow, legend.position = "topright")
Plots the PCA scatter plot of the intensity matrix of the object.
Writes the segment output for each sample in csv format.
Simon Papillon-Cavanagh, Jean-Philippe Fortin, Nicolas De Jay
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library(CopyNumber450kData) library(minfiData) # Load the CopyNumber450kData control set data(RGcontrolSetEx) # Load example data (n=6) from minfiData data(RGsetEx) # In order to reduce example time, let's use only one sample # and 30 controls (instead of 52). In real life situations, it is advised to # use all the available controls RGsetEx <- RGsetEx[, 5] RGcontrolSetEx <- RGcontrolSetEx[, sample(1:ncol(RGcontrolSetEx), 30)] # Combine both RGsets in a single RGset RGset <- combine(RGcontrolSetEx, RGsetEx) # Create the object mcds <- CNV450kSet(RGset) # In order to speed up example computation, we will randomly subset the # probes used by CopyNumber450k. THIS SHOULD NEVER BE DONE AS IT SERVES # ONLY FOR SPEEDING UP THE EXAMPLE. mcds <- mcds[sample(1:nrow(mcds), 10000), ] # Drop SNP probes mcds <- dropSNPprobes(mcds, maf_threshold=0.01) # Normalization mcds <- normalize(mcds, "quantile") # Some plots plotDensity(mcds, main="Density plot of functional normalized data") plotPCA(mcds, main="PCA plot of functional normalized data") # Segmentation mcds <- segmentize(mcds) # Plotting the results plotSample(mcds, 1, main="Genomic view of Sample 1") plotSample(mcds, 1, chr="chr1", ylim=c(-.25,.25)) # Saving the results in csv format write.csv(mcds, file="segments.csv")
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