Package 'Repitools'

Description

This function performs differential analyses, given a QdnaData object with the sample-specific offsets already calculated (i.e. call getSampleOffsets before calling abcdDNA), a coefficient (or set of coefficients) to test and dispersion(s). In essence, the function is a wrapper for constructing the offset matrix, fitting the generalized linear model and performing a likelihood ratio test.

Usage

abcdDNA(obj, coef = ncol(obj$design), dispersion = NULL)

Arguments

Details

This function is simply a wrapper for taking the details in an QdnaData object and perform the differential analyses, adjusting for copy number if specified.

Value

a DGEGLM (see the edgeR package) containing the results of the differential comparison

Author(s)

Mark Robinson

References

http://imlspenticton.uzh.ch/robinson_lab/ABCD-DNA/ABCD-DNA.html

See Also

QdnaData,

Examples

# library(Repitools)
# qd <- QdnaData(counts=counts, regions=gb, design=design, 
#                cnv.offsets=cn, neutral=(regs=="L=4 P=2"))
# qd <- getSampleOffsets(qd,ref=1)
# plotQdnaByCN(qd,cnv.group=regs,idx.ref=3,idx.sam=2)
# f <- abcdDNA(qd, dispersion=.05, coef=2)
# topTags(f)

Description

This function uses the GCadjustCopy function to convert a matrix of count data into absolute copy number estimates, then it segments them, and reports the copy number of either the input regions or user-defined regions of interest.

Usage

## S4 method for signature 'data.frame,matrix,GCAdjustParams'
absoluteCN(input.windows, input.counts, gc.params, ...)
  ## S4 method for signature 'GRanges,matrix,GCAdjustParams'
absoluteCN(input.windows, input.counts, gc.params,
                                                       segment.sqrt = TRUE, ..., verbose = TRUE)

Arguments

Details

For details of the absolute copy number estimation step, see the documentation for GCadjustCopy.

For details of the segmentation, see segment documentation. By default, no weights are used.

Value

A CopyEstimate object. If regions was not provided, it describes the input windows, otherwise it describes the windows specified by regions.

Author(s)

Dario Strbenac

Examples

## Not run: 
    library(BSgenome.Hsapiens.UCSC.hg18)
    library(BSgenome.Hsapiens36bp.UCSC.hg18mappability)
    load("inputsReads.RData")
    windows <- genomeBlocks(Hsapiens, chrs = paste("chr", c(1:22, 'X', 'Y'), sep = ''),
                            width = 20000)
    counts <- annotationBlocksCounts(inputsReads, anno = windows, seq.len = 300)

    gc.par <- GCAdjustParams(genome = Hsapiens, mappability = Hsapiens36bp,
                             min.mappability = 50, n.bins = 10, min.bin.size = 10,
                             poly.degree = 4, ploidy = c(2, 4))
    abs.cn <- absoluteCN(input.windows = windows, input.counts = counts, gc.params = gc.par)
  
## End(Not run)

Description

Contains the genomic coordinates of regions, the raw counts before GC adjustment, the GC content and mappability of each region, and the polynomial model fit, and the GC-adjusted copy number estimates.

Constructor

AdjustedCopyEstimate(ploidy, windows, mappability, gc, unadj.CN, models, adj.CN) Creates a AdjustedCopyEstimate object.

ploidy

Sets of chromosomes in each sample.

windows

A GRanges object.

mappability

A numeric vector of mappability. Elements between 0 and 1.

gc

A numeric vector of GC content Elements between 0 and 1.

unadj.CN

A matrix of estimated copy numbers after mappability adjustment, but before GC content adjustment, if slot type is "absolute". Otherwise, fold changes.

models

The polynomial models that were fit to the counts.

adj.CN

A matrix of estimated copy numbers after mappability adjustment and GC content adjustment, if slot type is "absolute". Otherwise, a matrix of fold changes, based on GC adjusted absolute copy estimates.

Note that mappability and gc become metadata columns of windows when the object is created.

Superclass

This class inherits from CopyEstimate.

Additional Slots

These are added to by absoluteCN or relativeCN

unadj.CN.seg

A GRangesList of copy number segmentations for each sample.

adj.CN.seg

A GRangesList of copy number segmentations for each sample, using GC adjusted data.

type

A flag that contains if the copy number data is absolute or relative.

Description

The documentation is available by typing ?aroma.affymetrix::AffymetrixCdfFile, but to avoid a check warning in the Repitools package, this help file is present.

Description

The documentation is available by typing ?aroma.affymetrix::AffymetrixCelSet, but to avoid a check warning in the Repitools package, this help file is present.

Description

Checks that the data.frame has the required columns, chr, start, end, then creates a GRanges, keeping all of the additional columns.

Usage

## S4 method for signature 'data.frame'
annoDF2GR(anno)

Arguments

Details

Extra columns are added to the elementMetadata of the GRanges object.

Value

A GRanges of the annotation.

Author(s)

Dario Strbenac

Examples

df <- data.frame(chr = c("chr1", "chr3", "chr7", "chr22"),
                   start = seq(1000, 4000, 1000),
                   end = seq(1500, 4500, 1000),
                   t = c(3.11, 0.93, 2.28, -0.18),
                   gc = c("High", "High", "Low", "High"))

  annoDF2GR(df)

Description

Converting a GRanges that might be annotated with some kind of results to a data.frame is useful, because it allows easier writing to file and viewing in other programs, like a spreadsheet program.

Usage

## S4 method for signature 'GRanges'
annoGR2DF(anno)

Arguments

Details

The column name seqnames is changed to chr, and if all the strands are *, then the strand column is dropped.

Value

A data.frame of the annotation.

Author(s)

Dario Strbenac

Examples

require(GenomicRanges)
  chrs <- c("chr1", "chr3", "chr7", "chr22")
  starts <- seq(1000, 4000, 1000)
  ends <- seq(1500, 4500, 1000)
  t <- c(3.11, 0.93, 2.28, -0.18)
  gc <- c("High", "High", "Low", "High")
  gr <- GRanges(chrs, IRanges(starts, ends), strand = '*', t, gc)

  annoGR2DF(gr)

Description

Counts reads inside blocks.

Usage

## S4 method for signature 'ANY,data.frame'
annotationBlocksCounts(x, anno, ...)
  ## S4 method for signature 'character,GRanges'
annotationBlocksCounts(x, anno, ...)
  ## S4 method for signature 'GRanges,GRanges'
annotationBlocksCounts(x, anno, seq.len = NULL, verbose = TRUE)
  ## S4 method for signature 'GRangesList,GRanges'
annotationBlocksCounts(x, anno, ...)

Arguments

Value

A matrix of counts is returned, one column per sample and one row per row of genomic features supplied.

Author(s)

Aaron Statham

See Also

annotationCounts, genomeBlocks

Examples

require(GenomicRanges)
  reads <- GRanges(seqnames = rep("chr1", 5),
                   IRanges(c(3309, 4756, 4801, 4804, 5392), width = 36),
                   strand = c('+', '-', '-', '+', '+'))
  genes <- GRanges("chr1", IRanges(5000, 7000), strand = '+')
  annotationBlocksCounts(reads, genes, 300)

Description

Starting from a table of genome locations for probes, and a table of regions of interest, this procedure forms a list structure that contains the indices to map from one to the other.

Usage

## S4 method for signature 'data.frame,data.frame'
annotationBlocksLookup(x, anno, ...)
  ## S4 method for signature 'data.frame,GRanges'
annotationBlocksLookup(x, anno, verbose = TRUE)

Arguments

Details

Strandedness of probes is ignored, even if it is given.

If x has no index column, then the probes are given indices from 1 to the number of probes, in the order that they appear in the data.frame or GRanges object.

Value

A list with elements

Author(s)

Aaron Statham, Mark Robinson

See Also

annotationLookup which simplifies annotation lookups for constant sized regions

Examples

# create example set of probes and gene start sites
probeTab <- data.frame(position=seq(1000,3000,by=200), chr="chrX", strand="+")
genes <- data.frame(chr="chrX", start=c(2100,2200), end=c(2500, 2400), strand=c("+","-"))
rownames(genes) <- paste("gene",1:2,sep="")

# Call annotationLookup() and look at output
annotationBlocksLookup(probeTab, genes)

Description

Counts are made in windows with boundaries fixed distances either side of a reference point.

Usage

# ANY,data.frame method
annotationCounts(x, anno, ...)
# ANY,GRanges method
annotationCounts(x, anno, up, down, ...)

Arguments

x:

A character vector of BAM paths, GRangesList, or GRanges object.

anno:

A set of genomic features to make windows around a reference point of theirs. Either a data.frame with (at least) colums chr, start, and end, or a GRanges object.

up:

The number of bases upstream to look.

down:

The number of bases downstream to look.

seq.len:

If sequencing reads need to be extended, the fragment size to be used. Default: NULL (no extension).

verbose:

Whether to print progress. Default: TRUE.

...:

Parameters described above, that are not used in the function called, but are passed into annotationBlocksCounts, that uses them in its processing.

Details

If the genomic features annotation contains all unstranded features, the up and down distances refer to how far towards the start of a chromosome, and how far towards the end to make the counting window boundaries. If the annotation is all stranded, then the up and down distances are relative to the TSS of the features.

Value

A matrix of counts is returned, one column per sample and one row per row of genomic features supplied.

Author(s)

Aaron Statham

See Also

annotationBlocksCounts, genomeBlocks

Examples

require(GenomicRanges)
  reads <- GRanges(seqnames = rep("chr1", 5),
                   IRanges(c(3309, 4756, 4801, 4804, 5392), width = 36),
                   strand = c('+', '-', '-', '+', '+'))
  genes <- GRanges("chr1", IRanges(5000, 7000), strand = '+')

  annotationCounts(reads, genes, 500, 500, 300)

Description

Starting from genome locations for probes and a locations for a set of genes, this procedure forms a list structure that contains the indices to map from one to the other.

Usage

The data.frame,data.frame method:
annotationLookup(x, anno, ...)
The data.frame,GRanges method:
annotationLookup(x, anno, up, down, ...)

Arguments

x:

Probe genomic locations, a data.frame with required elements chr, position, and optionally index

anno:

a data.frame with required elements chr, start, end, strand and optional element name. Also may be a GRanges with optional elementMetadata column name.

up:

The number of bases upstream to look.

down:

The number of bases downstream to look.

verbose:

Whether to print progress to screen. Default: TRUE

...:

Parameters described above, that are not used in the function called, but are passed further into annotationBlocksLookup, which uses them in its processing.

Details

This function is a wrapper for the generic function annotationBlocksLookup which can handle annotations of varying sizes. annotationLookup is appropriate where you wish to map probes that are within a fixed distance of points of annotation e.g gene transcription start sites. Even if strand information is given for probes, it is ignored.

If x has no index column, then the probes are given indices from 1 to the number of probes, in the order that they appear in the data.frame or GRanges object.

It is an error for the gene annotation to have unstranded features.

Value

A list with elements

indexes

a list for each gene in y, giving a vector of indices to the probe data.

offsets

a list for each gebe in y, giving a vector (corresponding to indexes) of offsets relative to the genes' TSSs for each probe that mapped that that gene.

Author(s)

Aaron Statham, Mark Robinson

See Also

annotationBlocksLookup, makeWindowLookupTable

Examples

# create example set of probes and gene start sites
probes <- data.frame(position=seq(1000, 3000, by = 200), chr = "chrX", strand = '-')
genes <- data.frame(chr = "chrX", start=c(2100, 1000), end = c(3000, 2200),
                    strand=c("+","-"))
rownames(genes) <- paste("gene", 1:2, sep = '')

# Call annotationLookup() and look at output
annotationLookup(probes, genes, 500, 500)

Description

A wrapper script for coverting the contents of BAM files for use with GenomicRanges classes.

Usage

## S4 method for signature 'character'
BAM2GRanges(path, what = character(),
      flag = scanBamFlag(isUnmappedQuery = FALSE, isDuplicate = FALSE),
      verbose = TRUE)
  ## S4 method for signature 'character'
BAM2GRangesList(paths, what = character(),
      flag = scanBamFlag(isUnmappedQuery = FALSE, isDuplicate = FALSE),
      verbose = TRUE)

Arguments

Value

For the single pathname method; a GRanges object. For the multiple pathnames method; a GRangesList object.

Author(s)

Dario Strbenac

Examples

tiny.BAM <- system.file("extdata", "ex1.bam", package = "Rsamtools")
  if(length(tiny.BAM) > 0)
    print(BAM2GRanges(tiny.BAM))

Description

This S4 class captures the genomic windows together with the number of read counts obtained by affinity-enrichment sequencing experiments for a fully methylated control and one or more samples of interest. Furthermore CpG-density is stored.

Constructor

Creates a BayMethList object:

BayMethList(windows, control, sampleInterest, cpgDens, f=matrix(), priorTab=list(), methEst=list(), maskEmpBayes=logical())

windows

A GRanges object.

control

A matrix of read counts obtained by an affinity enrichment sequencing experiment for the fully methylated (SssI) treated sample. The number of rows must be equal to length(windows). Each column contains the counts of one sample. The number of columns must be either one or equal to the number of columns of sampleInterest.

sampleInterest

A matrix of read counts obtained by an affinity enrichment sequencing experiment for the samples of interest. The number of rows must be equal to length(windows). Each column contains the counts of one sample.

cpgDens

A numeric vector containing the CpG density for windows. The length must be equal to length(windows)

fOffset

A matrix where each column contains the normalizing offsets for one sample. The number of rows must be either equal to one or the number of windows.

priorTab

A list containing for each sample of interest the prior parameters as determined by empBayes.

methEst

A list containing the methylation estimates as determined by methylEst.

maskEmpBayes

A logical vector indicating which bins should be masked out in the empirical Bayes analysis. TRUE indicates to neglect the bin in the empirical Bayes approach.

Methods

x[i]

signature(x = "BayMethList"): Creates a BayMethList object, keeping only the i entries.

length

signature(x= "BayMethList"): gets the number of genomic regions included.

control<-

signature(x = "BayMethList"): replace the control slot

control

signature(object = "BayMethList"): extract the control matrix slot.

cpgDens<-

signature(x = "BayMethList"): replace the cpgDens slot

cpgDens

signature(object = "BayMethList"): extract the cpgDens slot.

sampleInterest<-

signature(x = "BayMethList"): replace the sampleInterest slot

sampleInterest

signature(object = "BayMethList"): extract the sampleInterest matrix slot.

show

signature(object = "BayMethList"): show an overview of the object

windows<-

signature(x = "BayMethList"): replace the windows slot

windows

signature(object = "BayMethList"): extract the windows GRanges slot.

fOffset<-

signature(x = "BayMethList"): replace the fOffset slot

fOffset

signature(object = "BayMethList"): extract the fOffset slot.

priorTab<-

signature(x = "BayMethList"): replace the priorTab slot

priorTab

signature(object = "BayMethList"): extract the priorTab slot.

methEst<-

signature(x = "BayMethList"): replace the methEst slot

methEst

signature(object = "BayMethList"): extract the methEst slot.

maskEmpBayes<-

signature(x = "BayMethList"): replace the maskEmpBayes slot

maskEmpBayes

signature(object = "BayMethList"): extract the maskEmpBayes slot.

ncontrol

signature(object = "BayMethList"): get the number of provided SssI samples.

nsampleInterest

signature(object = "BayMethList"): get the number of provided samples of Interest.

Author(s)

Andrea Riebler and Mark Robinson

See Also

determineOffset, empBayes, methylEst

Examples

if(require(BSgenome.Hsapiens.UCSC.hg18)){
        windows <- genomeBlocks(Hsapiens, chrs="chr21", width=100, spacing=100)
        cpgdens <- cpgDensityCalc(windows, organism=Hsapiens,  
            w.function="linear", window=700)  
        co <- matrix(rnbinom(length(windows), mu=10, size=2), ncol=1)
        sI <- matrix(rnbinom(2*length(windows), mu=5, size=2), ncol=2)
        bm <- BayMethList(windows=windows, control=co, sampleInterest=sI,
            cpgDens=cpgdens)

        cat("Number of genomic regions", length(bm), "\n")
        cat("Number of fully methylated control samples:", ncontrol(bm), "\n")
        cat("Number of samples of interest:", nsampleInterest(bm), "\n")
        bm[2:20]
    }

Description

Using a specified ordering of genes, they are split into multiple bins. In each bin, the signal across is summarized and displayed visually.

Usage

## S4 method for signature 'ScoresList'
binPlots(x, summarize = c("mean", "median"), ordering = NULL,
   ord.label = NULL, plot.type = c("line", "heatmap", "terrain"), n.bins = 10, cols = NULL,
   lwd = 3, lty = 1, same.scale = TRUE, symm.scale = FALSE, verbose = TRUE)

Arguments

Details

If plotType = "line", a line is plotted for each bin across the promoter.

If plotType = "heatmap", a series of bins are plotted as a heatmap. This can be useful to display a larger number of bins.

If plotType = "terrain", a series of bins are plotted as a 3D-terrain map. This can be useful to display a larger number of bins.

Value

Either a single- or multiple-panel figure.

Author(s)

Mark Robinson

Examples

data(chr21genes)
  data(samplesList)  # Loads 'samples.list.subset'.
  data(expr)  # Loads 'expr.subset'.

  fs <- featureScores(samples.list.subset, chr21genes, up = 5000, down = 1000, dist = "base", freq = 1000,
                      s.width = 500)
  fs@scores <- list(tables(fs)[[2]] - tables(fs)[[4]])
  names(fs) <- "PC-Norm"

  binPlots(fs, ordering = expr.subset, ord.label = "expression", plot.type = "line", n.bins = 4)
  binPlots(fs, ordering = expr.subset, ord.label = "expression", plot.type = "heatmap", n.bins = 8)

Description

For each region of interest or TSS, this routine interrogates probes or sequence data for either a high level of absolute signal or a change in signal for some specified contrast of interest. Regions can be surroundings of TSSs, or can be user-specified regions. The function determines if the start and end coordinates of anno should be used as regions or as TSSs, if the up and down coordinates are NULL or are numbers.

Usage

The ANY,data.frame method:
blocksStats{ANY,data.frame}(x, anno, ...)
The ANY,GRanges method:
blocksStats{ANY,GRanges}(x, anno, up = NULL, down = NULL, ...)

Arguments

x:

A GRangesList, AffymetrixCelSet, or a data.frame of data. Or a character vector of BAM paths to the location of the BAM files.

anno:

Either a data.frame or a GRanges giving the gene coordinates or regions of interest. If it is a data.frame, then the column names are (at least) chr, name, start, end. Column strand is also mandatory, if up and down are NULL.

seq.len:

If sequencing reads need to be extended, the fragment size to be used.

p.anno:

A data.frame with (at least) columns chr, position, and index. This is an optional parameter of the AffymetrixCelSet method, because it can be automatically retrieved for such array data. The parameter is also optional, if mapping is not NULL.

mapping:

If a mapping with annotationLookup or annotationBlocksLookup has already been done, it can be passed in, and avoids unnecessary re-conmputing of the mapping list within blocksStats.

chrs:

If p.anno is NULL, and is retrieved from an ACP file, this vector gives the textual names of the chromosomes.

log2.adj:

Whether to take $log_2$ of array intensities.

design:

A design matrix specifying the contrast to compute (i.e. The samples to use and what differences to take.).

up:

The number of bases upstream to consider in calculation of statistics. If not provided, the starts and ends in anno are used as region boundaries.

down:

The number of bases upstream to consider in calculation of statistics. If not provided, the starts and ends in anno are used as region boundaries.

lib.size:

A string that indicates whether to use the total lane count, total count within regions specified by anno, or normalisation to a reference lane by the negative binomial quantile-to-quantile method, as the library size for each lane. For total lane count use "lane", for region sums use "blocks", and for the normalisation use "ref".

robust:

Numeric. If it is 0, then a robust linear model is not fitted. If it is greater than 0, a robust linear model is used, and the number specifies the minimum number of probes a region has to have, for statistics to be reported for that region.

p.adj:

The method used to adjust p-values for multiple testing. Possible values are listed in p.adjust.

Acutoff:

If libSize is "ref", this argument must be provided. Otherwise, it must not. This parameter is a cutoff on the "A" values to take, before calculating trimmed mean.

verbose:

Logical; whether to output commments of the processing.

...

Parameters described above, that are not used in the function called, but are passed further into a private function that uses them in its processing.

Details

For array data, the statstics are either determined by a t-test, or a linear model. For sequencing data, the two groups are assumed to be from a negative binomial distribution, and an exact test is used.

Value

A data.frame, with the same number of rows as there are features described by anno, but with additional columns for the statistics calculated at each feature.

Author(s)

Mark Robinson

See Also

annotationLookup and annotationBlocksLookup

Examples

require(GenomicRanges)
  intensities <- matrix(c(6.8, 6.5, 6.7, 6.7, 6.9,
                          8.8, 9.0, 9.1, 8.0, 8.9), ncol = 2)
  colnames(intensities) <- c("Normal", "Cancer")
  d.matrix <- matrix(c(-1, 1))
  colnames(d.matrix) <- "Cancer-Normal"
  probe.anno <- data.frame(chr = rep("chr1", 5),
                           position = c(4000, 5100, 6000, 7000, 8000), 
                           index = 1:5)
  anno <- GRanges("chr1", IRanges(7500, 10000), '+', name = "Gene 1")
  blocksStats(intensities, anno, 2500, 2500, probe.anno, log2.adj = FALSE, design = d.matrix)

Description

Given a set of gene coordinates, and probe mappings to the genome, a plot is created across every gene region of how many probes mapped to each position.

Usage

## S4 method for signature 'data.frame,data.frame'
checkProbes(regs, probes, up = NULL, down = NULL, ...)
  ## S4 method for signature 'GRanges,GRanges'
checkProbes(regs, probes, up = NULL, down = NULL, ...)

Arguments

Details

If up and down are NULL, then the gene is plotted as it is described by its start and end coordinates.

This function produces a number of plots. Sending output to a PDF device is recommended.

Value

A set of plots is created, one for each of the genes. The lines in the plot show where a probe hits (the x - axis) and how many places in total the probe hits in the genome (y - axis).

Author(s)

Dario Strbenac

Examples

p.table <- data.frame(name = c("probeA", "probeB", "probeC", "probeC", "probeC"),
			    strand = c('+', '-', '+', '-', '-'),
                               chr = c("chr1", "chr2", "chr1", "chr2", "chr2"),
                             start = c(20, 276, 101, 101, 151),
                               end = c(44, 300, 125, 125, 175))
	r.table <- data.frame(name = c("gene1", "gene2", "gene3"),
                               chr = c("chr1", "chr2", "chr2"),
                            strand = c('+', '-', '+'),
                             start = c(20, 500, 75),
                               end = c(200, 800, 400))
	pdf("tmp.pdf", height = 6, width = 14)
	checkProbes(r.table, p.table, lwd = 4, col = "blue")
	dev.off()

Description

Annotation of chromosome 21 genes from RefSeq in June 2010.

Usage

chr21genes

Format

A data frame.

Source

UCSC Genome Browser tables.

Description

This function discovers regions of enrichment in ChIP-seq data, using the method described in Hawkins RD. et al 2010 Cell Stem Cell.

Usage

## S4 method for signature 'GRangesList,GRangesList'
ChromaBlocks(rs.ip, rs.input, organism, chrs, ipWidth=100, inputWidth=500, preset=NULL, blockWidth=NULL, minBlocks=NULL, extend=NULL, cutoff=NULL, FDR=0.01, nPermutations=5, nCutoffs=20, cutoffQuantile=0.98, verbose=TRUE, seq.len=NULL)

Arguments

Value

A ChromaResults object.

Author(s)

Aaron Statham

See Also

ChromaResults

Description

The ChromaResults class stores the results of a ChromaBlocks run.

Slots of a ChromaResults object

blocks:GRanges of the blocks used across the genome, with their calculated RPKM regions:IRangesList of regions determined to be enriched FDRTable:data.frame showing the FDR at each cutoff tested cutoff:The cutoff used to determine enrichment

Author(s)

Aaron Statham

See Also

ChromaBlocks

Description

Generates plots of position along chromosomes vs. estimated copy number. If GC adjustment was performed, then there are two plots per page; one before adjustment and one after adjustment.

Usage

## S4 method for signature 'CopyEstimate'
chromosomeCNplots(copy, y.max = NULL, pch = 19, cex = 0.2,
             pch.col = "black", seg.col = "red", lty = 1, lwd = 2, verbose = TRUE)
  ## S4 method for signature 'AdjustedCopyEstimate'
chromosomeCNplots(copy, y.max = NULL, pch = 19, cex = 0.2,
             pch.col = "black", seg.col = "red", lty = 1, lwd = 2, verbose = TRUE)

Arguments

Details

See absoluteCN or relativeCN for how to do the GC adjusted copy number estimates, if this is required. The segmentation line plotted is of the segmentation regions found by circular binary segmentation.

Value

A number of pages of scatterplots. The output should, therefore, be sent to a PDF device.

Author(s)

Dario Strbenac

Examples

## Not run: 
    library(BSgenome.Hsapiens.UCSC.hg18)
    library(BSgenome.Hsapiens36bp.UCSC.hg18mappability)
    load("inputsReads.RData")
    windows <- genomeBlocks(Hsapiens, chrs = paste("chr", c(1:22, 'X', 'Y'), sep = ''),
                            width = 20000)
    counts <- annotationBlocksCounts(inputsReads, anno = windows, seq.len = 300)

    gc.par <- GCAdjustParams(genome = Hsapiens, mappability = Hsapiens36bp,
                             min.mappability = 50, n.bins = 10, min.bin.size = 10,
                             poly.degree = 4, ploidy = c(2, 4))
    abs.cn <- absoluteCN(input.windows = windows, input.counts = counts, gc.params = gc.par)

    pdf("chrProfiles.pdf")
    chromosomeCNplots(abs.cn, y.max = 8)
    dev.off()
  
## End(Not run)

Description

Contains a list of coverage matrices, the parameters that were used to generate them origin, and also cluster membership and expression data.

It also allows the user to take the ScoresList output of featureScores, and do their own custom clustering on the coverage matrices, then save the clustering results in this container.

Constructor

ClusteredScoresList(x, anno = x@anno, scores = tables(x), expr = NULL, expr.name = NULL, cluster.id, sort.name = NULL, sort.data = NULL) Creates a ClusteredScoresList object.

x

A ScoresList object.

anno

A GRanges object. Give this value if only a subset of features was used for clustering.

scores

A list of coverage matrices. Give this if the matrices in x were modified before clustering.

expr

A numeric vector, same length as number of rows of every coverage matrix.

expr.name

A label, describing the expression data.

cluster.id

A vector, same length as number of rows of every coverage matrix.

sort.data

Vector of data to order features within clusters by.

sort.name

Human readable description of what the sorting data is of.

Subsetting

In the following code snippets, x is a ClusteredScoresList object.

x[i] Creates a ClusteredScoresList object, keeping only the i matrices.

subsetRows(x, i = NULL) Creates a ClusteredScoresList object, keeping only the i features.

clusters(x) Creates a ClusteredScoresList object, keeping only the i features.

Accessors

In the following code snippets, x is a ClusteredScoresList object.

clusters(x) Get the cluster ID of each feature.

Author(s)

Dario Strbenac

Description

Takes the output of featureScores, or a modified version of it, and plots a heatmaps or lineplots representation of clustered coverages.

Usage

## S4 method for signature 'ClusteredScoresList'
clusterPlots(
     scores.list, plot.ord = 1:length(scores.list), plot.type = c("heatmap", "line", "by.cluster"),
     heat.bg.col = "black", summarize = c("mean", "median"), symm.scale = FALSE, cols = NULL, t.name = NULL,
     verbose = TRUE, ...)
  ## S4 method for signature 'ScoresList'
clusterPlots(scores.list, scale = function(x) x,
    cap.q = 0.95, cap.type = c("sep", "all"), all.mappable = FALSE, n.clusters = NULL,
    plot.ord = 1:length(scores.list), expr = NULL, expr.name = NULL, sort.data = NULL,
    sort.name = NULL, plot.type = c("heatmap", "line", "by.cluster"),
    summarize = c("mean", "median"), cols = NULL, t.name = NULL, verbose = TRUE, ...)

Arguments

Details

A ClusteredScoresList should be created by the user, if they wish to do some custom clustering and normalisation on the coverage matrices. Otherwise, if the user is happy with k-means or PAM clustering, then the ScoresList object as output by featureScores() can be directly used. If called with a ScoresList, then the matrices for each coverage type are joined. Then the function supplied by the scale argument is used to scale the data. Next, each matrix is capped. Then each matrix is divided by its maximum value, so that the Euclidean distance between maximum reads and no reads is the same for each matrix. Lastly, either k-means or PAM clustering is performed to get the cluster membership of each feature. If there are any NAs in the scores, then PAM will be used. Otherwise, k-means is used for speed. Then, a ClusteredScoresList object is created, and used. The clusters are guaranteed to be given IDs in descending order of summarised cluster expression, if it is provided. If called with a ClusteredScoresList, no scaling or capping is done, so it is the user's responsibility to normalise the coverage matrices as they see fit, when creating the ClusteredScoresList object.

If a ClusteredScoresList object is subsetted, the original data range is saved in a private slot, so that if the user wants to plot a subset of the features, such as a certain cluster, for example, the intensity range of the heatmap, or the y-axis range of the lineplot will be the same as before subsetting.

If expression data is given, the summarised expression level of each cluster is calculated, and the clusters are plotted in order of decreasing expression, down the page. Otherwise, they are plotted in ascending order of cluster ID. If a heatmap plot is being drawn, then a heatmap is drawn for every coverage matrix, side-by-side, and a plot of each feature's expression is put alongside the heatmaps, if provided. If additional sort vector was given, the data within clusters are sorted on this vector, then a plot of this data is made as the rightmost graph.

The lineplot style is similar to the heatmap plot, but clusters are summarised. A grid, with as many rows as there are clusters, and as many columns as there are clusters is made, and lineplots showing the summarised scores are made in the grid. Beside the grid, a boxplot of expression is drawn for each cluster, if provided.

For a cluster-wise lineplot, a graph is drawn for each cluster, with the colours being the different coverage types. Because it makes sense that there will be more clusters than there are types of coverage (typically double to triple the number), the plots are not drawn side-by-side, as is the layout for the heatmaps. For this reason, sending the output to a PDF device is necessary. It is recommended to make the width of the PDF device wider than the default. Since the coverage data between different marks is not comparable, this method is inappropriate for visualising a ClusteredScoresList object if it was created by the clusterPlots scoresList method. If the user, however, can come up with a normalisation method to account for the differences that are apparent between different types (i.e. peaked vs. spread) of marks that makes the coverages meaningfully comparable, they can alter the tables, do their own clustering, and create a ClusteredScoresList object with the modified tables.

Value

If called with a ScoresList, then a ClusteredScoresList is returned. If called with a ClusteredScoresList, then nothing is returned.

Author(s)

Dario Strbenac

See Also

featureScores for generating coverage matrices.

Examples

data(samplesList)  # Loads 'samples.list.subset'.
  data(expr)  # Loads 'expr.subset'.
  data(chr21genes)

  fs <- featureScores(samples.list.subset[1:2], chr21genes, up = 2000, down = 1000,
                      freq = 500, s.width = 500)
  clusterPlots(fs, function(x) sqrt(x), n.clusters = 5, expr = as.numeric(expr.subset),
               plot.type = "heatmap", pch = 19, cex = 0.5)

Description

Contains the genomic coordinates of regions, and fold change estimates.

Constructor

CopyEstimate(windows, unadj.CN, unadj.CN.seg) Creates a CopyEstimate object.

windows

A GRanges object.

unadj.CN

A matrix of fold changes.

unadj.CN.seg

A GRangesList object holding the segmentation results.

Additional Slots

These are added to by absoluteCN or relativeCN

type

A flag that contains if the copy number data is absolute or relative.

Description

Either makes a side by side boxplot of two designs, or plots a single boxplot for the difference between the two designs.

Usage

## S4 method for signature 'AffymetrixCelSet'
cpgBoxplots(this, samples=c(1,2), subsetChrs="chr[1-5]", gcContent=7:18, calcDiff=FALSE, verbose=FALSE, nBins=40, pdfFile=NULL, ylim=if (calcDiff) c(-5,6) else c(4,15), col=if (calcDiff) "salmon" else c("lightgreen","lightblue"), mfrow=if (!is.null(pdfFile)) c(2,2) else c(1,1))
## S4 method for signature 'matrix'
cpgBoxplots(this, ndfTable = NULL, organism, samples=c(1,2), subsetChrs="chr[1-5]", gcContent=7:18, calcDiff=FALSE, verbose=FALSE, nBins=40, pdfFile=NULL, ylim=if (calcDiff) c(-5,6) else c(4,15), col=if (calcDiff) "salmon" else c("lightgreen","lightblue"), mfrow=if (!is.null(pdfFile)) c(2,2) else c(1,1))

Arguments

Details

CpG content of probes is calculated in a 600 base window surrounding the probe, with a linearly decresasing weighting further away from the probe.

Value

Invisibly returns a list of the plots.

Author(s)

Mark Robinson, Dario Strbenac

Description

Function to calculate CpG density around a position.

Usage

## S4 method for signature 'data.frame,BSgenome'
cpgDensityCalc(x, organism, ...)
  ## S4 method for signature 'GRangesList,BSgenome'
cpgDensityCalc(x, organism, verbose = TRUE, ...)
  ## S4 method for signature 'GRanges,BSgenome'
cpgDensityCalc(x, organism, seq.len = NULL, window = NULL,
                                          w.function = c("none", "linear", "exp", "log"),
                                          verbose = TRUE)

Arguments

Details

If the version of the data frame with the start, end, and strand columns is given, the window will be created around the TSS.

For weighting scheme "none", this is equivalent to the number of CG matches in the region. For "linear" weighting, each match is given a score 1/x where x is the number of bases from the postition that the match occurred, and the scores are summed. For exponential weighting and logarithmic weighting, the idea is similar, but the scores decay exponentially (exp^-5x/window) and logarithmically (log2(2 - (distancesForRegion / window))).

Value

A numeric vector of CpG densities for each region.

Author(s)

Dario Strbenac

Examples

if(require(BSgenome.Hsapiens.UCSC.hg18))
  {
    TSSTable <- data.frame(chr = c("chr1", "chr2"), position = c(100000, 200000))
    cpgDensityCalc(TSSTable, organism = Hsapiens, window = 600)
  }

Description

Function to generate a plot of the distribution of sequencing reads CpG densities.

Usage

## S4 method for signature 'GRangesList'
cpgDensityPlot(x, cols=rainbow(length(x)), xlim=c(0,20), lty = 1, lwd = 1, main="CpG Density Plot", verbose=TRUE, ...)

Arguments

Details

See cpgDensityCalc for details of options for calculating the CpG density.

Value

A plot is created. The data processed by cpgDensityCalc is invisibly returned.

Author(s)

Aaron Statham

Examples

if(require(BSgenome.Hsapiens.UCSC.hg18))
{
  data(samplesList) # Loads 'samples.list.subset'.
  cpgDensityPlot(samples.list.subset, seq.len=300, organism=Hsapiens, lwd=4, verbose=TRUE)
}

Description

The composition of a library influences the resulting read densities. To adjust the modelled mean (in the Poisson model) for these composition effects, we estimate a normalising factor f that accounts simultaneously for overall sequencing depth and composition. The derivation of this offset is motivated by the M (log ratio) versus A (average-log-count) plot.

Usage

determineOffset(x, quantile = 0.998, controlPlot = list(show = FALSE, 
    nsamp = 50000, mfrow=c(1,1), xlim=NULL, ylim=NULL, main=NULL, ask=FALSE))

Arguments

Value

A BayMethList object given as input, where the slot fOffset is filled accordingly.

Author(s)

Andrea Riebler

See Also

maPlot, plotSmear

Examples

if(require(BSgenome.Hsapiens.UCSC.hg18)){
        windows <- genomeBlocks(Hsapiens, chrs="chr21", width=100, spacing=100)
        cpgdens <- cpgDensityCalc(windows, organism=Hsapiens, 
            w.function="linear", window=700)  
        co <- matrix(rnbinom(length(windows), mu=10, size=2), ncol=1)
        sI <- matrix(rnbinom(2*length(windows), mu=5, size=2), ncol=2)
        bm <- BayMethList(windows=windows, control=co, sampleInterest=sI,
            cpgDens=cpgdens)

        bm <- determineOffset(bm, controlPlot=list(show=TRUE, mfrow=c(1,2)))
   }

Description

Under the empirical Bayes approach (and assuming a uniform prior for the methylation level) the shape and scale parameters for the gamma prior of the region-specific read density are derived. The parameters are thereby determined in a CpG-dependent manner.

Usage

empBayes(x, ngroups = 100, ncomp = 1, maxBins=50000, method="beta", controlMethod=list(mode="full", weights=c(0.1, 0.8, 0.1), param=c(1,1)), ncpu = NULL, verbose = FALSE)

Arguments

Details

BayMeth takes advantage of the relationship between CpG-density and read depth to formulate a CpG-density-dependent gamma prior distribution for the region-specific read density. Taking CpG-density into account the prior should stabilise the methylation estimation procedure for low counts and in the presence of sampling variability. The shape and scale parameter of the gamma prior distribution are determined in a CpG-density-dependent manner using empirical Bayes. For each genomic bin the CpG density is provided in the BayMethList-object. Each bin is classified based on its CpG-density into one of ngroups non-overlapping CpG-density intervals. For each class separately, we derive the values for the shape and scale parameter under an empirical Bayes framework using maximum likelihood. For CpG classes which contain more than maxBins bins, a random sample drawn with replacement of size maxBins is used to derive these prior parameters. Note that both read depths, from the SssI control and the sample of interest, are thereby taken into account. We end up with ngroups parameter sets for shape and rate.

Value

A BayMethList object where the slot priorTab is filled. priorTab represent a list. The first list entry contains the CpG group a bin is assigned to. The second entry contains the number of components that have been used for the prior (at the moment 1). The following list entries correspond to one sample of interest, respectively, and contain a matrix with the optimal shape and scale parameters for all CpG classes. The first row contains the optimal shape parameter and the second row the optimal scale parameter. The number of columns corresponds to the number of CpG classes specified in ngroups.

Author(s)

Andrea Riebler

Examples

if(require(BSgenome.Hsapiens.UCSC.hg18)){
        windows <- genomeBlocks(Hsapiens, chrs="chr21", width=100, spacing=100)
        cpgdens <- cpgDensityCalc(windows, organism=Hsapiens, 
            w.function="linear", window=700)  
        co <- matrix(rnbinom(length(windows), mu=10, size=2), ncol=1)
        sI <- matrix(rnbinom(2*length(windows), mu=5, size=2), ncol=2)
        bm <- BayMethList(windows=windows, control=co, 
            sampleInterest=sI, cpgDens=cpgdens)
        bm <- determineOffset(bm)
 
        # mask out unannotated high copy number regions
        # see Pickrell et al. (2011), Bioinformatics 27: 2144-2146.

        # should take about 3 minutes for both sample of interests with 2 CPUs.
        bm <- empBayes(bm, ngroups=20) 
   }

Description

Function to calculate enrichment over the whole genome of sequencing reads.

Usage

## S4 method for signature 'GRanges'
enrichmentCalc(x, seq.len = NULL, verbose = TRUE)
  ## S4 method for signature 'GRangesList'
enrichmentCalc(x, verbose = TRUE, ...)

Arguments

Details

If seq.len is supplied, x is firstly extended, and then turned into a coverage object. The number of extended reads covering each base pair of the genome is then tabulated, and returned as a data.frame.

Value

For the GRanges method, data.frame containing columns coverage and bases. For the GRangesList method, a list of such data.frames.

Author(s)

Aaron Statham

Examples

require(GenomicRanges)
  data(samplesList)  # Loads 'samples.list.subset'.
  seqlengths(samples.list.subset)

  tc <- enrichmentCalc(samples.list.subset, seq.len = 300)

Description

Function to generate a plot of the distribution of sequencing reads enrichments.

Usage

## S4 method for signature 'GRangesList'
enrichmentPlot(x, seq.len, cols = rainbow(length(x)),
      xlim = c(0, 20), main = "Enrichment Plot", total.lib.size = TRUE, verbose = TRUE, ...)

Arguments

Details

See enrichmentCalc for details of how the results are determined.

Value

A plot is created. The data processed by enrichmentCalc is invisibly returned.

Author(s)

Aaron Statham

Examples

data(samplesList)  # GRangesList of reads 'samples.list.subset'
  enrichmentPlot(samples.list.subset, seq.len = 300, total.lib.size = FALSE)

Description

The t-statistics of differences in expression for genes on chromosome 21 between prostate cancer and normal epithelial cells.

Usage

expr.subset

Format

A numeric matrix, 309 rows and 1 column.

Description

The FastQC class stores results obtained from the FastQC application (see references), with a slot for each FastQC module. The SequenceQC class contains the QC results of a single lane of sequencing in three slots: Unaligned - FastQC results obtained from all reads (before alignment) Aligned - FastQC results obtained from only reads which aligned Mismatches - a data.frame containing counts for the number of mismatches of each type found at each sequencing cycle

Slots of a FastQC object

Basic_Statistics

Per_base_sequence_quality

Per_sequence_quality_scores

Per_base_sequence_content

Per_base_GC_content

Per_sequence_GC_content

Per_base_N_content

Sequence_Length_Distribution

Sequence_Duplication_Levels

Overrepresented_sequences

Slots of a SequenceQC object

Unaligned - FastQC results obtained from all reads (before alignment)

Aligned - FastQC results obtained from only reads which aligned

Mismatches - a data.frame containing counts for the number of mismatches of each type found at each sequencing cycle

MismatchTable - a data.frame containing counts of how many mismatches aligned sequences contain

Author(s)

Aaron Statham

References

FastQC - http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/

Description

Windows are made around a reference point, which is the start coordinate for features on the + strand, and the end coordinate for features on the - strand. For unstranded features, the reference point is taken to be the mid-point of the feature.

Usage

## S4 method for signature 'data.frame'
featureBlocks(anno, ...)
  ## S4 method for signature 'GRanges'
featureBlocks(anno, up = NULL, down = NULL, dist = c("base", "percent"),
                                    keep.strand = FALSE)

Arguments

Details

up refers to how many bases to go upstream for stranded features, or for unstranded features, how many bases to go towards the start of the chromosome, from the mid-point of the feature. Having a negative value for up means that the windows will start downstream by that amount, for stranded features. For unstranded features, it will start that many bases closer to the end of the chromosome, relative to the feature mid-point.

down is defined analogously.

Value

A GRanges of windows surrounding reference points for the features described by anno.

Author(s)

Dario Strbenac

Examples

genes <- data.frame(chr = c("chr1", "chr3", "chr7", "chr22"),
                   start = seq(1000, 4000, 1000),
                   end = seq(1500, 4500, 1000),
                   strand = c('+', '-', '-', '+'))

  featureBlocks(genes, 500, 500)

Description

Given a GRanges / GRangesList object, or BAM file paths, of reads for each experimental condition, or a matrix or an AffynetrixCelSet, or a numeric matrix of array data, where the rows are probes and the columns are the different samples,and an anntotation of features of interest, scores at regularly spaced positions around the features is calculated. In the case of sequencing data, it is the smoothed coverage of reads divided by the library size. In the case of array data, it is array intensity.

Usage

The ANY,data.frame method:
featureScores(x, anno, ...)
The ANY,GRanges method:
featureScores(x, anno, up = NULL, down = NULL, ...)

Arguments

x:

Paths to BAM files, a collection of mapped short reads, or a collection of microarray data.

anno:

Annotation of the features to sample around.

p.anno:

A data.frame with columns chr, position, an optionally index. Only provide this if x is array data. If index is not provided, the rows are assumed to be in the same order as the elements of x.

mapping:

A mapping between probes and genes, as made by annotationLookup. Avoids re-computing the mapping if it has already been done. Only provide this if x is array data.

chrs:

A mapping between chromosome names in an ACP file to the user's feature annotation. Only provide this if x is an AffymetrixCelSet. There is no need to provide this if the feature annotation uses the same chromosome names as the ACP files do. Element i of this vector is the name to give to the chromosome numbered i in the ACP information.

up:

How far to go from the features' reference points in one direction.

down:

How far to go from the features' reference points in the opposite direction.

dist:

The type of distance measure to use, in determining the boundaries of the sampling area. Only provide this if x is sequencing data. Default: "base". "percent" is also accepted.

freq:

Score sampling frequency.

log2.adj:

Whether to log2 scale the array intensities. Only provide this if x is array data. Default: TRUE.

s.width:

The width of smoothing to apply to the coverage. Only provide this if x is sequencing data. This argument is optional. If not provided, then no smoothing is done.

mappability:

A BSgenome object, or list of such objects, the same length as x that has bases for which no mappable reads start at masked by N. If this was provided, then either s.width or tag.len must be provided (but not both).

map.cutoff:

The percentage of bases in a window around each sampling position that must be mappable. Otherwise, the score at that position is repalced by NA. Default: 0.5

tag.len:

Provide this if mappability was provided, but s.width was not.

use.strand:

Whether to only consider reads on the same strand as the feature. Useful for RNA-seq applications.

verbose:

Whether to print the progess of processing. Default: TRUE.

Details

If x is a vector of paths or GRangesList object, then names(x) should contain the types of the experiments.

If anno is a data.frame, it must contan the columns chr, start, and end. Optional columns are strand and name. If anno is a GRanges object, then the name can be present as a column called name in the element metadata of the GRanges object. If names are given, then the coverage matrices will use the names as their row names.

An approximation to running mean smoothing of the coverage is used. Reads are extended to the smoothing width, rather than to their fragment size, and coverage is used directly. This method is faster than a running mean of the calculated coverage, and qualtatively almost identical.

If providing a matrix of array intensity values, the column names of this matrix are used as the names of the samples.

The annotation can be stranded or not. if the annotation is stranded, then the reference point is the start coordinate for features on the + strand, and the end coordinate for features on the - strand. If the annotation is unstranded (e.g. annotation of CpG islands), then the midpoint of the feature is used for the reference point.

The up and down values give how far up and down from the reference point to find scores. The semantics of them depend on if the annotation is stranded or not. If the annotation is stranded, then they give how far upstream and downstream will be sampled. If the annotation is unstranded, then up gives how far towards the start of a chromosome to go, and down gives how far towards the end of a chromosome to go.

If sequencing data is being analysed, and dist is "percent", then they give how many percent of each feature's width away from the reference point the sampling boundaries are. If dist is "base", then the boundaries of the sampling region are a fixed width for every feature, and the units of up and down are bases. up and down must be identical if the features are unstranded. The units of freq are percent for dist being "percent", and bases for dist being "base".

In the case of array data, the sequence of positions described by up, down, and freq actually describe the boundaries of windows, and the probe that is closest to the midpoint of each window is chosen as the representative score of that window. On the other hand, when analysing sequencing data, the sequence of positions refer to the positions that coverage is taken for.

Providing a mappability object for sequencing data is recommended. Otherwise, it is not possible to know if a score of 0 is because the window around the sampling position is unmappable, or if there were really no reads mapping there in the experiment. Coverage is normalised by dividing the raw coverage by the total number of reads in a sample. The coverage at a sampling position is multiplied by 1 / mappability. Any positions that have mappabilty below the mappability cutoff will have their score set to NA.

Value

A ScoresList object, that holds a list of score matrices, one for each experiment type, and the parameters that were used to create the score matrices.

Author(s)

Dario Strbenac, with contributions from Matthew Young at WEHI.

See Also

mergeReplicates for merging sequencing data replicates of an experiment type.

Examples

data(chr21genes)
  data(samplesList) # Loads 'samples.list.subset'.

  fs <- featureScores(samples.list.subset[1:2], chr21genes, up = 2000, down = 1000,
                      freq = 500, s.width = 500)

Description

Given a table of gene positions that has a score column, genes will first be sorted into positional order and consecutive windows of high or low scores will be reported.

Usage

findClusters(stats, score.col = NULL, w.size = NULL, n.med = NULL, n.consec = NULL,
               cut.samps = NULL, maxFDR = 0.05, trend = c("down", "up"), n.perm = 100,
               getFDRs = FALSE, verbose = TRUE)

Arguments

Details

First, the median over a window of size w.size is calculated in a rolling window and then associated with the middle gene of the window. Windows are again run over the genes, and the gene at the centre of the window is significant if there are also at least n.med genes with representative medians above the score cutoff, in the window that surrounds it. These marker genes are extended outwards, for as long as the score has the same sign. The order of the stats rows is randomised, and this process in done for every randomisation.

The procedure for calling clusters is done at a range of score cutoffs. The first score cutoff to give an FDR below maxFDR is chosen as the cutoff to use, and clusters are then called based on this cutoff.

Value

If getFDRs is FALSE, then only the stats table, with an additional column, cluster. If getFDRs is TRUE, then a list with elements :

Author(s)

Dario Strbenac, Aaron Statham

References

Saul Bert, in preparation

Examples

chrs <- sample(paste("chr", c(1:5), sep = ""), 500, replace = TRUE)
  starts <- sample(1:10000000, 500, replace = TRUE)
  ends <- starts + 10000
  genes <- data.frame(chr = chrs, start = starts, end = ends, strand = '+')
  genes <- genes[order(genes$chr, genes$start), ]
  genes$t.stat = rnorm(500, 0, 2)
  genes$t.stat[21:30] = rnorm(10, 4, 1)
  findClusters(genes, 5, 5, 2, 3, seq(1, 10, 1), trend = "up", n.perm = 2)

Description

Taking into account mappability and GC content biases, the absolute copy number is calculated, by assuming that the median read depth is a copy number of 1.

Usage

## S4 method for signature 'data.frame,matrix,GCAdjustParams'
GCadjustCopy(input.windows, input.counts,
                                                           gc.params, ...)
  ## S4 method for signature 'GRanges,matrix,GCAdjustParams'
GCadjustCopy(input.windows, input.counts,
                                                         gc.params, verbose = TRUE)

Arguments

Details

First, the mappability of all counting windows is calculated, and windows that have mappability less than the cutoff specified by in the parameters object are ignored in further steps. The remaining windows have their counts scaled by multiplying their counts by 100 / percentage mappability.

The range of GC content of the counting windows is broken into a number of bins, as specified by the user in the parameters object. A probability density function is fitted to the counts in each bin, so the mode can be found. The mode is taken to be the counts of the copy neutral windows, for that GC content bin.

A polynomial function is fitted to the modes of GC content bins. Each count is divided by its expected counts from the polynomial function to give an absolute copy number estimate. If the ploidy has been provided in the parameters object, then all counts within a sample are multiplied by the ploidy for that sample. If the sample ploidys were omitted, then no scaling for ploidy is done.

Value

A AdjustedCopyEstimate object describing the input windows and their estimates.

Author(s)

Dario Strbenac

Examples

## Not run: 
    library(BSgenome.Hsapiens.UCSC.hg18)
    library(BSgenome.Hsapiens36bp.UCSC.hg18mappability)
    load("inputsReads.RData")
    windows <- genomeBlocks(Hsapiens, chrs = paste("chr", c(1:22, 'X', 'Y'), sep = ''),
                            width = 20000)
    counts <- annotationBlocksCounts(inputsReads, anno = windows, seq.len = 300)

    gc.par <- GCAdjustParams(genome = Hsapiens, mappability = Hsapiens36bp,
                             min.mappability = 50, n.bins = 10, min.bin.size = 10,
                             poly.degree = 4, ploidy = c(2, 4))
    abs.cn <- GCadjustCopy(input.windows = windows, input.counts = counts, gc.params = gc.par)
  
## End(Not run)

Description

The parameters are used by the absoluteCN function.

Constructor

GCAdjustParams(genome, mappability, min.mappability, n.bins = NULL, min.bin.size = 1, poly.degree = NULL, ploidy = 1) Creates a GCAdjustParams object.

genome

A BSgenome object of the species that the experiment was done for.

mappability

A BSgenome object, or the path to a FASTA file generated by GEM mappability containing the mappability of each base in the genome.

min.mappability

A number between 0 and 100 that is a cutoff on window mappability.

n.bins

The number of GC content bins to divide the windows into, before finding the mode of counts in each window.

min.bin.size

GC bins with less than this many count windows inside them will be ignored.

poly.degree

The degree of the polynomial to fit to the GC bins' count modes.

ploidy

A vector of multipliers to use on the estimated absolute copy number of each sample, if the number of sets of chromosomes is known.

Author(s)

Dario Strbenac

Description

Two plots on the same plotting page are made for each sample. The top plot has estimates of copy number separated by GC content before any GC correction was applied. The bottom plot shows the copy number estimates after GC correction was applied.

Usage

## S4 method for signature 'AdjustedCopyEstimate'
GCbiasPlots(copy, y.max = NULL, pch = 19,
            cex = 0.2, pch.col = "black", line.col = "red", lty = 1, lwd = 2, verbose = TRUE)

Arguments

Details

See absoluteCN or relativeCN for how to do the GC adjusted copy number estimates. The line plotted through the scatterplots is a lowess line fit to the data points.

Value

A number of pages of scatterplots equal to the number of samples described by copy. The output should, therefore, be sent to a PDF device.

Author(s)

Dario Strbenac

Examples

## Not run: 
    library(BSgenome.Hsapiens.UCSC.hg18)
    library(BSgenome.Hsapiens36bp.UCSC.hg18mappability)
    load("inputsReads.RData")
    windows <- genomeBlocks(Hsapiens, chrs = paste("chr", c(1:22, 'X', 'Y'), sep = ''),
                            width = 20000)
    counts <- annotationBlocksCounts(inputsReads, anno = windows, seq.len = 300)

    gc.par <- GCAdjustParams(genome = Hsapiens, mappability = Hsapiens36bp,
                             min.mappability = 50, n.bins = 10, min.bin.size = 10,
                             poly.degree = 4, ploidy = c(2, 4))
    abs.cn <- absoluteCN(input.windows = windows, input.counts = counts, gc.params = gc.par)

    pdf("bias.pdf")
    GCbiasPlots(abs.cn, y.max = 8)
    dev.off()
  
## End(Not run)

Description

Function to calculate the GC content of windows

Usage

## S4 method for signature 'GRanges,BSgenome'
gcContentCalc(x, organism, verbose = TRUE)
  ## S4 method for signature 'data.frame,BSgenome'
gcContentCalc(x, organism, window = NULL, ...)

Arguments

Details

The windows considered will be windowSize/2 bases upstream and windowSize/2-1 bases downstream of the given position, for each position. The value returned for each region is a percentage of bases in that region that are a G or C.

Value

A vector of GC content percentages, one for each region.

Author(s)

Aaron Statham

Examples

require(BSgenome.Hsapiens.UCSC.hg18)
TSSTable <- data.frame(chr = paste("chr", c(1,2), sep = ""), position = c(100000, 200000))
gcContentCalc(TSSTable, 200, organism=Hsapiens)

Description

Creates a compact GRanges representation of bins across specified chromosomes of a given genome.

Usage

## S4 method for signature 'numeric'
genomeBlocks(genome, chrs = names(genome), width = NULL,
                                   spacing = width)
  ## S4 method for signature 'BSgenome'
genomeBlocks(genome, chrs = seqnames(genome), width = NULL,
                                    spacing = width)

Arguments

Value

Returns a GRanges object, compatible with direct usage in annotationBlocksCounts

Author(s)

Aaron Statham

See Also

annotationBlocksCounts

Examples

chr.lengths <- c(800, 200, 200)
  names(chr.lengths) <- c("chr1", "chr2", "chr3")
  genomeBlocks(chr.lengths, width = 200)

Description

A series of quality control plots for sequencing data are made.

Usage

## S4 method for signature 'character'
genQC(qc.data, ...)
  ## S4 method for signature 'SequenceQCSet'
genQC(qc.data, expt = "Experiment")

Arguments

Details

qc.data can be named, in which case this gives the names of the lanes used in the plotting. Otherwise the lanes will be given the names "Lane 1", "Lane 2", ..., "Lane n".

Value

The function is called for its output. The output is multiple pages, so the pdf device should be called before this function is.

Author(s)

Dario Strbenac

References

FastQC: http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/

Examples

## Not run: 
    qc.files <- list.files(qc.dir, "QC.*RData", full.names = TRUE)
    genQC(qc.files, "My Simple Experiment")
  
## End(Not run)

Description

Translates the probe information in the AromaCellPositionFile to a data.frame object.

Usage

## S4 method for signature 'AffymetrixCdfFile'
getProbePositionsDf(cdf, chrs, ..., verbose = TRUE)

Arguments

Details

This assumes that the AromaCellPositionFile exist.

Value

A data.frame with 3 columns: chr, position, index

Author(s)

Mark Robinson

Examples

## not run
# probePositions <- getProbePositionsDf(cdfU)

Description

ABCD-DNA combines CNV offsets with sample specific factors. This function calculates the latter, using a set of neutral regions (and corresponding counts in the count table).

Usage

getSampleOffsets(obj, ref = 1, quantile = 0.99, min.n = 100, plot.it = FALSE, force = FALSE, ...)

Arguments

Details

The sample-specific offset is calculated as the median M-value beyond (i.e. to the right) an A-value quantile, using only the copy-number-neutral regions, as specified in the incoming QdnaData object.

Value

returns a QdnaData object (copied from the obj argument) and populates the $DGEList$samples$norm.factors element and sets the $sample.specific.calculated to TRUE.

Author(s)

Mark Robinson

References

http://imlspenticton.uzh.ch/robinson_lab/ABCD-DNA/ABCD-DNA.html

See Also

QdnaData

Examples

# library(Repitools)
# qd <- QdnaData(counts=counts, regions=gb, design=design, 
#               cnv.offsets=cn, neutral=(regs=="L=4 P=2"))
# qd <- getSampleOffsets(qd,ref=1)

Description

File to mask out areas of the genome that are prone to causing false positives in ChIP-seq and other sequencing based functional assays, as proposed by Pickrell et al. (2011), Bioinformatics 27: 2144-2146, http://eqtl.uchicago.edu/Home.html.

Usage

hcRegions

Format

A GRanges object created using the bedfile provided on http://eqtl.uchicago.edu/Masking/seq.cov1.ONHG19.bed.gz.

Source

Pickrell et al. (2011), Bioinformatics 27: 2144-2146.

Description

Computes the value of the Gaussian hypergeometric function 2_F_1 as defined in Abramowitz and Stegun (1972, page 558), i.e. for |z| < 1 and c > b > 0 using the Cephes library.

Usage

hyperg2F1_vec(a,b,c,z)

Arguments

Details

The function is in particular efficient for vectorial arguments as the loop is shifted to C. Note: If vectorial arguments are provided, all arguments need to be of the same length.

Value

The value of the Gaussian hypergeometric function F(a,b,c,z) for c > b > 0 and |z| < 1.

Author(s)

Andrea Riebler and Daniel Sabanes Bove

References

Abramowitz and Stegun 1972. _Handbook of mathematical functions with formulas, graphs and mathematical tables_. New York: Dowver Publications.

www.netlib.org/cephes/

See Also

package hypergeo or BMS.

Examples

hyperg2F1_vec(-10.34,2.05,3.05,0.1725)
    hyperg2F1_vec(30,1,20,.8) # returns about 165.8197
    hyperg2F1_vec(30,10,20,0) # returns one
    hyperg2F1_vec(10,15,20,-0.1) # returns about 0.4872972

    hyperg2F1_vec(c(-10.34, 30, 10), c(2.05, 1, 10), c(3.05, 20, 20), 
        c(0.1725, 0.8, 0))
    hyperg2F1_vec(a=1.2+1:10/10, b=rep(1.4,10), c=rep(1.665,10), z=rep(.3,10))

Description

Reads a file in Nimblegen pair format, returning log2 intensities of probes referenced by the supplied ndf data frame.

Usage

loadPairFile(filename = NULL, ndf = NULL, ncols = 768)

Arguments

Details

Reads in intensities from the specified pair file, then matches probes against those specified in the supplied ndf.

Value

a vector of log2 intensities, the number of rows of the supplied ndf in length.

Author(s)

Aaron Statham

See Also

loadSampleDirectory for reading multiple pair files with the same ndf. processNDF

Examples

# Not run
#
## Read in the NDF file 
# ndfAll <- processNDF("080310_HG18_chr7RSFS_AS_ChIP.ndf")
#
## Subset the NDF to only probes against chromosomes
# ndf <- ndfAll[grep("^chr", ndfAll$chr),]
#
## Read in a pair file using the chromosome only NDF
# arrayIntensity <- loadPairFile("Pairs/Array1_532.pair", ndf)
#

Description

Reads all files in Nimblegen pair format within the specified directory, returning log2 intensities of probes referenced by the supplied ndf data frame.

Usage

loadSampleDirectory(path = NULL, ndf = NULL, what="Cy3", ncols = 768)

Arguments

Details

Reads in intensities of all arrays contained within path. The parameter what determines which fluorescent channels are read, and how the are returned. Cy3 and Cy5 return the log2 intensity of the specified single channel. Cy3/Cy5 and Cy5/Cy3 return the log2 ratio of the two channels. Cy3andCy5 and Cy5andCy3 return the log2 intensity of both channels in separate columns of the matrix.

Value

a matrix of log2 intensites, with the same number of rows as the supplied ndf and depending on the value of what either one or two columns per array.

Author(s)

Aaron Statham

See Also

loadPairFile for reading a single pair files. processNDF

Examples

# Not run
#
## Read in the NDF file 
# ndfAll <- processNDF("080310_HG18_chr7RSFS_AS_ChIP.ndf")
#
## Subset the NDF to only probes against chromosomes
# ndf <- ndfAll[grep("^chr", ndfAll$chr),]
#
## Read in a directory of pair files, returning both the Cy3 and Cy5 fluorescence in separate columns
# arrayIntensities <- loadSampleDirectory("Arrays", ndf, what="Cy3andCy5")
#

Description

To allow easy access to the probe-level data for either a gene, or an area of the promoter (over all genes), this routine takes the output of annotationLookup and organizes the indices into a table, one row for each gene and one column for each region of the promoter.

Usage

makeWindowLookupTable(indexes = NULL, offsets = NULL, starts = NULL, ends = NULL)

Arguments

Details

The vectors starts and ends (which should be the same length) determine the number of columns in the output matrix.

Value

A matrix with rows for each gene and columns for each bin of the promoter. NA signifies that there is no probe in the given distance from a TSS.

Author(s)

Mark Robinson

See Also

annotationLookup

Examples

# create example set of probes and gene start sites
probeTab <- data.frame(position=seq(1000,3000,by=200), chr="chrX", strand = '-')
genes <- data.frame(chr="chrX", start=c(2100, 1000), end = c(3000, 2200), strand=c("+","-"))
rownames(genes) <- paste("gene",1:2,sep="")

# Call annotationLookup() and look at output
aL <- annotationLookup(probeTab, genes, 500, 500)
print(aL)

# Store the results of annotationLookup() in a convenient tabular format
lookupTab <- makeWindowLookupTable(aL$indexes, aL$offsets, starts=seq(-400,200,by=200), ends=seq(-200,400,by=200))
print(lookupTab)

Description

Function to calculate mappability of windows

Usage

## S4 method for signature 'GRanges,MappabilitySource'
mappabilityCalc(x, organism, window = NULL,
              type = c("block", "TSS", "center"), verbose = TRUE)
  ## S4 method for signature 'data.frame,MappabilitySource'
mappabilityCalc(x, organism, window = NULL,
                                         type = c("block", "TSS", "center"), ...)

Arguments

For unstranded features, the effect is the same as for + strand features.

Details

The windows considered will be windowSize/2 bases upstream and windowSize/2-1 bases downstream of the given position of stranded features, and the same number of bases towards the start and end of the chromosome for unstranded features. The value returned for each region is a percentage of bases in that region that are not N (any base in IUPAC nomenclature).

For any positions of a window that are off the end of a chromosome, they will be considered as being N.

Value

A vector of mappability percentages, one for each region.

Author(s)

Aaron Statham

Examples

## Not run: 
    require(BSgenome.Hsapiens36bp.UCSC.hg18mappability)
    TSSTable <- data.frame(chr = paste("chr", c(1,2), sep = ""), position = c(100000, 200000))
    mappabilityCalc(TSSTable, Hsapiens36bp, window = 200, type = "TSS")
  
## End(Not run)

Description

This class is simply the union of character and BSgenome classes.

Author(s)

Dario Strbenac

Description

Function to mask out regions that are prone to causing problems in the empirical Bayes approach empBayes. The corresponding bins are marked and in the empirical Bayes approach not taken into account. Notice that methylation estimates using methylEst will nevertheless be produced for these bins.

Usage

maskOut(x, ranges)

Arguments

Value

A BayMethList object where the slot maskout is filled with a boolean vector indicating which bins will be excluded in empBayes.

Author(s)

Andrea Riebler

Examples

if(require(BSgenome.Hsapiens.UCSC.hg18)){
        windows <- genomeBlocks(Hsapiens, chrs="chr21", width=100, spacing=100)
        cpgdens <- cpgDensityCalc(windows, organism=Hsapiens, 
            w.function="linear", window=700)  
        co <- matrix(rnbinom(length(windows), mu=10, size=2), ncol=1)
        sI <- matrix(rnbinom(2*length(windows), mu=5, size=2), ncol=2)
        bm <- BayMethList(windows=windows, control=co, 
            sampleInterest=sI, cpgDens=cpgdens)

        # mask out unannotated high copy number regions
        # see Pickrell et al. (2011), Bioinformatics 27: 2144-2146.
        data(hcRegions)

        bm <- maskOut(bm, hcRegions)
   }

Description

A lane of next generation sequencing data can be stored as a GRanges object. Sometimes, a GRangesList of various lanes can have experimental replicates. This function allows the merging of such elements.

Usage

## S4 method for signature 'GRangesList'
mergeReplicates(reads, types, verbose = TRUE)

Arguments

Details

The experiment type that each element of the merged list is of, is stored in the first element of the metadata list.

Value

A GRangesList with one element per experiment type.

Author(s)

Dario Strbenac

Examples

library(GenomicRanges)
  grl <- GRangesList(GRanges("chr1", IRanges(5, 10)),
                     GRanges("chr18", IRanges(25, 50)),
                     GRanges("chr22", IRanges(1, 100)))
  antibody <- c("MeDIP", "MeDIP", "H3K4me3")
  mergeReplicates(grl, antibody)

Description

Posterior mean and variance for the regional methylation level are derived for all genomic regions. Credible intervals can be computed either numerically from the posterior marginal distribution or by computing them on logit scale and transferring them back.

Usage

methylEst(x, verbose=FALSE, controlCI = list(compute = FALSE, method = "Wald", 
    level = 0.95, nmarg = 512, ncpu = NULL))

Arguments

Details

The posterior mean and the variance are analytically available and therefore straightforward to efficiently compute; Wald-based credible intervals are obtained on logit scale and then back-transferred to ensure values withing 0 and 1. HPD and quantile-based credible intervals are computed by numerical integration of the posterior marginal distribution.

Value

A BayMethList object where the slot methEst is filled with a list containing the following elements:

Author(s)

Andrea Riebler

Examples

if(require(BSgenome.Hsapiens.UCSC.hg18)){
        windows <- genomeBlocks(Hsapiens, chrs="chr21", width=100, spacing=100)
        cpgdens <- cpgDensityCalc(windows, organism=Hsapiens, 
            w.function="linear", window=700)  
        co <- matrix(rnbinom(length(windows), mu=10, size=2), ncol=1)
        sI <- matrix(rnbinom(2*length(windows), mu=5, size=2), ncol=2)
        bm <- BayMethList(windows=windows, control=co, 
            sampleInterest=sI, cpgDens=cpgdens)

        bm <- determineOffset(bm)
        # should take about 3 minutes for both samples of interests with 2 CPUs.
        bm <- empBayes(bm) 
        bm <- methylEst(bm, controlCI = list(compute = FALSE, method = "Wald", 
            level = 0.95, nmarg = 512, ncpu = NULL))
    }

Description

This function takes a list of matrices and plots heatmaps for each one. There are several features for the spacing (X and Y), colour scales, titles and label sizes. If a matrix has row and/or column names, these are added to the plot.

Usage

multiHeatmap(dataList, colourList, titles = NULL, main = "", showColour = TRUE, xspace = 1, cwidth = 0.5, ystarts = c(0.05, 0.9, 0.925, 0.95, 0.98), rlabelcex = 1, clabelcex = 1, titlecex = 1.2, maincex = 1.5, scalecex = 0.7, offset=.001)

Arguments

Value

This function is called for its output, a plot in the current device.

Author(s)

Mark Robinson

Examples

library(gplots)

cL <- NULL
br <- seq(-3,3,length=101)
col <- colorpanel(low="blue",mid="grey",high="red",n=101)
cL[[1]] <- list(breaks=br,colors=col)
br <- seq(-2,2,length=101)
col <- colorpanel(low="green",mid="black",high="red",n=101)
cL[[2]] <- list(breaks=br,colors=col)
br <- seq(0,20,length=101)
col <- colorpanel(low="black",mid="grey",high="white",n=101)
cL[[3]] <- list(breaks=br,colors=col)

testD <- list(matrix(runif(400),nrow=20),matrix(rnorm(100),nrow=20),matrix(rpois(100,lambda=10),nrow=20))
colnames(testD[[1]]) <- letters[1:20]
rownames(testD[[1]]) <- paste("row",1:20,sep="")

multiHeatmap(testD,cL,xspace=1)

Description

Given an annotation of gene positions that has a score column, the function will make a series of bar chart plots, one for each cluster.

Usage

## S4 method for signature 'data.frame'
plotClusters(x, s.col = NULL, non.cl = NULL, ...)
  ## S4 method for signature 'GRanges'
plotClusters(x, s.col = NULL, non.cl = NULL, ...)

Arguments

Value

A plot for each cluster is made. Therefore, the PDF device should be opened before this function is called.

Author(s)

Dario Strbenac

Examples

library(GenomicRanges)
  g.summary <- GRanges("chr1",
                       IRanges(seq(1000, 10000, 1000), width = 100),
                       rep(c('+', '-'), 5),
                       `t-statistic` = rnorm(10, 8, 2),
                       cluster = c(0, 0, 0, 0, 0, 1, 1, 1, 1, 0),
                       name = paste("Gene", 1:10))
  plotClusters(g.summary, 1, 0, ylim = c(4, 12), lwd = 5)

Description

Given groupings of relative CNV state, this function produces M-A (log-fold-change versus log-average) plots to compare two samples relative read densities. In addition, it calculates a scaling factor at a specified quantile and plots the median M value across all the groups.

Usage

plotQdnaByCN(obj, cnv.group, idx.ref = 1, idx.sam = 2, min.n = 100, quantile = 0.99, ylim = c(-5, 5), ...)

Arguments

Value

a plot to the current graphics device

Author(s)

Mark Robinson

References

http://imlspenticton.uzh.ch/robinson_lab/ABCD-DNA/ABCD-DNA.html

See Also

QdnaData, ~~~

Examples

# library(Repitools)
# qd <- QdnaData(counts=counts, regions=gb, design=design, 
#                cnv.offsets=cn, neutral=(regs=="L=4 P=2"))
# plotQdnaByCN(qd,cnv.group=regs,idx.ref=3,idx.sam=2)

Description

Reads a Nimblegen microarray design file (NDF file) which describes positions and sequences of probes on a Nimblegen microarray.

Usage

processNDF(filename = NULL, ncols = 768)

Arguments

Details

Reads in a Nimblegen microarray design file. This enables the reading in and annotation of Nimblegen microarray data files (pair files).

Value

a data frame containing

Author(s)

Aaron Statham

See Also

loadSampleDirectory, loadPairFile

Examples

# Not run
#
## Read in the NDF file 
# ndfAll <- processNDF("080310_HG18_chr7RSFS_AS_ChIP.ndf")
#
## Subset the NDF to only probes against chromosomes
# ndf <- ndfAll[grep("^chr", ndfAll$chr),]

Description

Creates a plot where the average signal across a promoter of supplied gene lists is compared to random samplings of all genes, with a shaded confidence area.

Usage

## S4 method for signature 'ScoresList'
profilePlots(x, summarize = c("mean", "median"), gene.lists,
   n.samples = 1000, confidence = 0.975, legend.plot = "topleft", cols = rainbow(length(gene.lists)),
   verbose = TRUE, ...)

Arguments

Details

For each table of scores in x, a plot is created showing the average signal of the genes specified in each list element of gene.lists compared to n.samples random samplings of all genes, with confidence % intervals shaded. If an element of gene.lists is a logical vector, its length must be the same as the number of rows of the score tables.

Value

A series of plots.

Author(s)

Aaron Statham

Examples

# See examples in manual.

Description

QdnaData objects form the basis for differential analyses of quantitative DNA sequencing data(i.e. ABCD-DNA). A user is required to specify the minimum elements: a count table, a list of regions and a design matrix. For copy-number-aware analyses, a table of offsets and the set of neutral regions needs to be given.

Usage

QdnaData(counts, regions, design, cnv.offsets = NULL, neutral = NULL)

Arguments

Details

QdnaData objects are geared for general differential analyses of qDNA-seq data. If CNV is present and prominent, the objects and methods available with QdnaData perform adjustments and spot checks before the differential analysis.

Value

a QdnaData object (effectively a list) is returned

Author(s)

Mark Robinson

References

http://imlspenticton.uzh.ch/robinson_lab/ABCD-DNA/ABCD-DNA.html

See Also

getSampleOffsets, plotQdnaByCN, setCNVOffsets

Examples

require(GenomicRanges)
cnt <- matrix(rpois(20,lambda=10),ncol=4)
gr <- GRanges("chr1",IRanges(seq(2e3,6e3,by=1e3), width=500))
des <- model.matrix(~c(0,0,1,1))
qd <- QdnaData( counts=cnt, regions=gr, design=des)

Description

The function finds the highest smoothed score cutoff for a pre-specified FDR. Smoothing is performed over a specified number of basepairs, and regions must have a minimum number of qualifying probes to be considered significant. The FDR is calculated as the ratio of the number of significant regions found in a permutation-based test, to the number found in the actual experimental microarray data.

Usage

## S4 method for signature 'matrix'
regionStats(x, design = NULL, maxFDR=0.05, n.perm=5, window=600, mean.trim=.1, min.probes=10, max.gap=500, two.sides=TRUE, ndf, return.tm = FALSE, verbose=TRUE)
  ## S4 method for signature 'AffymetrixCelSet'
regionStats(x, design = NULL, maxFDR=0.05, n.perm=5, window=600, mean.trim=.1, min.probes=10, max.gap=500, two.sides=TRUE, ind=NULL, return.tm = FALSE, verbose=TRUE)

Arguments