Package 'AllelicImbalance'

Description

Package AllelicImbalance has functions for importing, filtering and plotting high-throughput data to make an allele specific expression analysis. A major aim of this package is to provide functions to collect as much information as possible from regions of choice, and to be able to explore the allelic expression of that region in detail.

Details

Overview - standard procedure

Start out creating a GRange object defining the region of interest. This can also be done using getAreaFromGeneNames providing gene names as arguments. Then use BamImpGAList to import reads from that reagion and find potential SNPs using scanForHeterozygotes. Then retrieve the allele counts of heterozygote sites by the function getAlleleCount. With this data create an ASEset. At this point all pre-requisites for a 'basic' allele specific expression analysis is available. Two ways to go on could be to apply chisq.test or barplot on this ASEset object.

Author(s)

Author: Jesper Robert Gadin Author: Lasse Folkersen

Maintainer: Jesper Robert Gadin <[email protected]>

References

Reference to published application note (work in progress)

See Also

• code?ASEset

Description

These functions acts as wrappers to retrieve information from annotation database objects (annotationDb objects) or (transcriptDb objects)

Usage

getGenesFromAnnotation(
  OrgDb,
  GR,
  leftFlank = 0,
  rightFlank = 0,
  getUCSC = FALSE,
  verbose = FALSE
)

getGenesVector(OrgDb, GR, leftFlank = 0, rightFlank = 0, verbose = FALSE)

getExonsFromAnnotation(
  TxDb,
  GR,
  leftFlank = 0,
  rightFlank = 0,
  verbose = FALSE
)

getExonsVector(TxDb, GR, leftFlank = 0, rightFlank = 0, verbose = FALSE)

getTranscriptsFromAnnotation(
  TxDb,
  GR,
  leftFlank = 0,
  rightFlank = 0,
  verbose = FALSE
)

getTranscriptsVector(TxDb, GR, leftFlank = 0, rightFlank = 0, verbose = FALSE)

getCDSFromAnnotation(TxDb, GR, leftFlank = 0, rightFlank = 0, verbose = FALSE)

getCDSVector(TxDb, GR, leftFlank = 0, rightFlank = 0, verbose = FALSE)

getAnnotationDataFrame(
  GR,
  strand = "+",
  annotationType = NULL,
  OrgDb = NULL,
  TxDb = NULL,
  verbose = FALSE
)

Arguments

Details

These functions retrieve regional annotation from OrgDb or TxDb objects, when given GRanges objects.

Value

GRanges object with ranges over the genes in the region.

The getGenesVector function will return a character vector where each element are gene symbols separated by comma

GRanges object with ranges over the exons in the region.

The getTranscriptsFromAnnotation function will return a GRanges object with ranges over the transcripts in the region.

The getCDSFromAnnotation function will return a GRanges object with ranges over the CDSFs in the region.

The getExonsVector function will return a character vector where each element are exons separated by comma

The getTranscriptsVector function will return a character vector where each element are transcripts separated by comma

The getCDSVector function will return a character vector where each element are CDSs separated by comma

The getAnnotationDataFrame function will return a data.frame with annotations. This function is used internally by i.e. the barplot-function

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

data(ASEset)
  require(org.Hs.eg.db)
  require(TxDb.Hsapiens.UCSC.hg19.knownGene)
  OrgDb <- org.Hs.eg.db
  TxDb <- TxDb.Hsapiens.UCSC.hg19.knownGene

  #use for example BcfFiles as the source for SNPs of interest
  GR <- rowRanges(ASEset)
  #get annotation
  g <- getGenesFromAnnotation(OrgDb,GR)
  e <- getExonsFromAnnotation(TxDb,GR)
  t <- getTranscriptsFromAnnotation(TxDb,GR)
  c <- getCDSFromAnnotation(TxDb,GR)

Description

adds a customizable annotation functionality for AllelicImbalance barplots.

Usage

annotationBarplot(
  strand,
  snp,
  lowerLeftCorner,
  annDfPlus,
  annDfMinus,
  cex = 0.7,
  ypos = 0,
  interspace = 1
)

Arguments

Details

the function is preferably called from within the AllelicImbalance barplot method.

Author(s)

Jesper R. Gadin

Examples

#code placeholders
#< create a barplot without annotation >
#< add annotation >

Description

Generates barplots for ASEset objects. Two levels of plotting detail are provided: a detailed barplot of read counts by allele useful for fewer samples and SNPs, and a less detailed barplot of the fraction of imbalance, useful for more samples and SNPs.

Usage

barplot(height, ...)

## S4 method for signature 'ASEset'
barplot(
  height,
  type = "count",
  sampleColour.top = NULL,
  sampleColour.bot = NULL,
  legend = TRUE,
  pValue = TRUE,
  strand = "*",
  testValue = NULL,
  testValue2 = NULL,
  OrgDb = NULL,
  TxDb = NULL,
  annotationType = c("gene", "exon", "transcript"),
  main = NULL,
  ylim = NULL,
  yaxis = TRUE,
  xaxis = FALSE,
  ylab = TRUE,
  ylab.text = NULL,
  xlab.text = "samples",
  xlab = TRUE,
  legend.colnames = "",
  las.ylab = 1,
  las.xlab = 2,
  cex.main = 1,
  cex.pValue = 0.7,
  cex.ylab = 0.7,
  cex.xlab = 0.7,
  cex.legend = 0.6,
  add = FALSE,
  lowerLeftCorner = c(0, 0),
  size = c(1, 1),
  addHorizontalLine = 0.5,
  add.frame = TRUE,
  filter.pValue.fraction = 0.99,
  legend.fill.size = 1,
  legend.interspace = 1,
  verbose = FALSE,
  top.fraction.criteria = "maxcount",
  cex.annotation = 0.7,
  ypos.annotation = 0,
  annotation.interspace = 1,
  ...
)

Arguments

Details

filter.pValue.fraction is intended to remove p-value annotation with very large difference in frequency, which could just be a sequencing mistake. This is to avoid p-values like 1e-235 or similar.

sampleColourUser specified colours, either given as named colours ('red', 'blue', etc) or as hexadecimal code. Can be either length 1 for all samples, or else of a length corresponding to the number of samples for individual colouring.

Author(s)

Jesper R. Gadin, Lasse Folkersen

See Also

• The ASEset class which the barplot function can be called up on.

Examples

data(ASEset)
barplot(ASEset[1])

Description

Object that holds allele counts, genomic positions and map-bias for a set of SNPs

Usage

alleleCounts(x, strand = "*", return.class = "list")

## S4 method for signature 'ASEset'
alleleCounts(x, strand = "*", return.class = "list")

alleleCounts(x, ...) <- value

## S4 replacement method for signature 'ASEset'
alleleCounts(x, strand = "*", return.class = "array", ...) <- value

mapBias(x, ...)

## S4 method for signature 'ASEset'
mapBias(x, return.class = "list")

fraction(x, ...)

## S4 method for signature 'ASEset'
fraction(
  x,
  strand = "*",
  top.fraction.criteria = "maxcount",
  verbose = FALSE,
  ...
)

arank(x, return.type = "names", return.class = "list", strand = "*", ...)

frequency(x, ...)

genotype(x, ...)

## S4 method for signature 'ASEset'
genotype(x, return.class = "matrix")

genotype(x) <- value

## S4 replacement method for signature 'ASEset'
genotype(x) <- value

countsPerSnp(x, ...)

## S4 method for signature 'ASEset'
countsPerSnp(x, return.class = "matrix", return.type = "mean", strand = "*")

countsPerSample(x, ...)

## S4 method for signature 'ASEset'
countsPerSample(x, return.class = "matrix", return.type = "mean", strand = "*")

phase(x, ...)

## S4 method for signature 'ASEset'
phase(x, return.class = "matrix")

phase(x) <- value

## S4 replacement method for signature 'ASEset'
phase(x) <- value

mapBias(x) <- value

## S4 replacement method for signature 'ASEset'
mapBias(x) <- value

refExist(x)

## S4 method for signature 'ASEset'
refExist(x)

ref(x)

## S4 method for signature 'ASEset'
ref(x)

ref(x) <- value

## S4 replacement method for signature 'ASEset,ANY'
ref(x) <- value

altExist(x)

## S4 method for signature 'ASEset'
altExist(x)

alt(x)

## S4 method for signature 'ASEset'
alt(x)

alt(x) <- value

## S4 replacement method for signature 'ASEset,ANY'
alt(x) <- value

aquals(x, ...)

## S4 method for signature 'ASEset'
aquals(x)

aquals(x) <- value

## S4 replacement method for signature 'ASEset'
aquals(x) <- value

maternalAllele(x, ...)

## S4 method for signature 'ASEset'
maternalAllele(x)

paternalAllele(x, ...)

## S4 method for signature 'ASEset'
paternalAllele(x)

Arguments

Details

An ASEset object differs from a regular RangedSummarizedExperiment object in that the assays contains an array instead of matrix. This array has ranges on the rows, sampleNames on the columns and variants in the third dimension.

It is possible to use the commands barplot and locationplot on an ASEset object see more details in barplot and locationplot.

Three different alleleCount options are available. The simples one is the * option, and is for experiments where the strand information is not known e.g. non-stranded data. The unknown strand could also be for strand specific data when the aligner could not find any strand associated with the read, but this should normally not happen, and if it does probably having an extremely low mapping quality. Then there are an option too add plus and minus stranded data. When using this, it is essential to make sure that the RNA-seq experiment under analysis has in fact been created so that correct strand information was obtained. The most functions will by default have their strand argument set to '*'.

The phase information is stored by the convention of 'maternal chromosome|paternal chromosome', with 0 as reference allele and 1 as alternative allele. '|' when the phase is known and '/' when the phase is unknown. Internally the information will be stored as an three dimensional array, dim 1 for SNPs, dim 2 for Samples and dim 3 which is fixed and stores maternal chromosome, paternal chromosome and phased (1 equals TRUE).

Value

An object of class ASEset containing location information and allele counts for a number of SNPs measured in a number of samples on various strand, as well as mapBias information. All data is stored in a manner similar to the SummarizedExperiment class.

Table

table(...)

Arguments:

...

An ASEset object that contains the variants of interest

The generics for table does not easily allow more than one argument so in respect to the different strand options, table will return a SimpleList with length 3, one element for each strand.

Frequency

frequency(x, return.class = "list", strand = "*", threshold.count.sample = 15)

Arguments:

x

An ASEset object that contains the variants of interest

x

threshold.count.samples

if sample has fewer counts the function return NA.

Constructor

ASEsetFromCountList(rowRanges, countListNonStranded = NULL, countListPlus = NULL, countListMinus = NULL, countListUnknown = NULL, colData = NULL, mapBiasExpMean = array(), verbose=FALSE, ...)

Arguments:

rowRanges

A GenomicRanges object that contains the variants of interest

countListNonStranded

A list where each entry is a matrix with allele counts as columns and sample counts as rows

countListPlus

A list where each entry is a matrix with allele counts as columns and sample counts as rows

countListMinus

A list where each entry is a matrix with allele counts as columns and sample counts as rows

countListUnknown

A list where each entry is a matrix with allele counts as columns and sample counts as rows

colData

A DataFrame object containing sample specific data

mapBiasExpMean

A 3D array describing mapping bias. The SNPs are in the 1st dimension, samples in the 2nd dimension and variants in the 3rd dimension.

verbose

Makes function more talkative

...

arguments passed on to SummarizedExperiment constructor

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#make example countList
set.seed(42)
countListPlus <- list()
snps <- c('snp1','snp2','snp3','snp4','snp5')
for(snp in snps){
  count<-matrix(rep(0,16),ncol=4,dimnames=list(
c('sample1','sample2','sample3','sample4'),
c('A','T','G','C')))
  #insert random counts in two of the alleles 
  for(allele in sample(c('A','T','G','C'),2)){
count[,allele]<-as.integer(rnorm(4,mean=50,sd=10))
  }
  countListPlus[[snp]] <- count
}

#make example rowRanges
rowRanges <- GRanges(
  seqnames = Rle(c('chr1', 'chr2', 'chr1', 'chr3', 'chr1')),
  ranges = IRanges(1:5, width = 1, names = head(letters,5)),
  snp = paste('snp',1:5,sep='')
)

#make example colData
colData <- DataFrame(Treatment=c('ChIP', 'Input','Input','ChIP'), 
 row.names=c('ind1','ind2','ind3','ind4'))

#make ASEset 
a <- ASEsetFromCountList(rowRanges, countListPlus=countListPlus, 
colData=colData)


#example phase matrix (simple form)
p1 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p2 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p <- matrix(paste(p1,sample(c("|","|","/"), size=nrow(a)*ncol(a), replace=TRUE), p2, sep=""),
	nrow=nrow(a), ncol(a))

phase(a) <- p


#generate ASEset from array
snps <- 999
samples <-5
ar <-array(rep(unlist(lapply(1:snps,
			function(x){(sample(c(TRUE,FALSE,TRUE,FALSE), size = 4))})), samples), 
			dim=c(4,snps,samples))
ar2 <- array(sample(50:300, 4*snps*samples,replace=TRUE), dim=c(4,snps,samples))
ar2[ar] <- 0
ar2 <- aperm(ar2, c(2, 3, 1))
dimnames(ar2) <- list(paste("snp",1:snps,sep=""),paste("sample",1:samples,sep=""),
						c("A","C","G","T"))
gr <- GRanges(seqnames=c("chr2"), ranges=IRanges(start=1:dim(ar2)[1], width=1), strand="*")
a <- ASEsetFromArrays(gr, countsUnknown=ar2)

Description

useful genotype filters

Usage

hetFilt(x, ...)

## S4 method for signature 'ASEset'
hetFilt(x, source = "genotype", ...)

Arguments

Details

hetFilt returns TRUE if the samples is heterozygote, based on stored genotype information present in the phase data.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load example data
data(ASEset)
a <- ASEset

genotype(a) <- inferGenotypes(a)
hets <- hetFilt(a)

Description

Generates gbarplots for ASEset objects. Two levels of plotting detail are provided: a detailed gbarplot of read counts by allele useful for fewer samples and SNPs, and a less detailed gbarplot of the fraction of imbalance, useful for more samples and SNPs.

Usage

gbarplot(x, type = "count", strand = "*", verbose = FALSE, ...)

Arguments

Details

This function serves the same purpose as the normal barplot, but with trellis graphics using lattice, to be able to integrate well with Gviz track functionality.

Author(s)

Jesper R. Gadin

See Also

• The ASEset class which the gbarplot function can be called up on.

• The barplot non trellis barplot.

Examples

data(ASEset)
gbarplot(ASEset[1])

Description

plotting ASE effects over a specific genomic region using Gviz functionality

Usage

glocationplot(
  x,
  type = "fraction",
  strand = "*",
  BamGAL = NULL,
  GenomeAxisTrack = FALSE,
  trackNameDeAn = paste("deTrack", type),
  TxDb = NULL,
  sizes = NULL,
  add = FALSE,
  verbose = FALSE,
  ...
)

Arguments

Details

The glocationplot methods visualises the distribution of ASE over a larger region on one chromosome. It takes and ASEset object as well as additional information on plot type (see gbarplot), strand type (see getAlleleCounts), Annotation tracks are created from the Gviz packageh. It is obviously important to make sure that the genome build used is set correctly, e.g. 'hg19'.

sizes has to be of the same length as the number of tracks used.

Author(s)

Jesper R. Gadin

See Also

• The ASEset class which the glocationplot function can be called up on.

Examples

data(ASEset)
genome(ASEset) <- 'hg19'

glocationplot(ASEset,strand='+')

#for ASEsets with fewer SNPs the 'count' type plot is useful 
glocationplot(ASEset,type='count',strand='+')

Description

plotting ASE effects over a specific genomic region

Usage

ASEDAnnotationTrack(
  x,
  GR = rowRanges(x),
  type = "fraction",
  strand = "*",
  trackName = paste("deTrack", type),
  verbose = TRUE,
  ...
)

## S4 method for signature 'ASEset'
ASEDAnnotationTrack(
  x,
  GR = rowRanges(x),
  type = "fraction",
  strand = "*",
  trackName = paste("deTrack", type),
  verbose = TRUE,
  ...
)

CoverageDataTrack(
  x,
  GR = rowRanges(x),
  BamList = NULL,
  strand = NULL,
  start = NULL,
  end = NULL,
  trackNameVec = NULL,
  meanCoverage = FALSE,
  verbose = TRUE,
  ...
)

Arguments

Details

For information of how to use these tracks in more ways, visit the Gviz package manual.

Author(s)

Jesper R. Gadin

See Also

• The ASEset class which the functions can be called up on.

Examples

data(ASEset)
x <- ASEset[,1:2]
r <- reads[1:2]
genome(x) <- 'hg19'
seqlevels(r) <- seqlevels(x)

GR <- GRanges(seqnames=seqlevels(x),
		ranges=IRanges(start=min(start(x)),end=max(end(x))),
		strand='+', genome=genome(x))

deTrack <- ASEDAnnotationTrack(x, GR=GR, type='fraction',strand='+')
covTracks <- CoverageDataTrack(x,BamList=r,strand='+') 

lst <- c(deTrack,covTracks)

sizes <- c(0.5,rep(0.5/length(covTracks),length(covTracks)))
#temporarily do not run this function 
#plotTracks(lst, from=min(start(x)), to=max(end(x)), 
#sizes=sizes, col.line = NULL, showId = FALSE, main='mainText', 
#cex.main=1, title.width=1, type='histogram')

Description

plotting ASE effects over a specific genomic region

Usage

locationplot(x, ...)

## S4 method for signature 'ASEset'
locationplot(
  x,
  type = "fraction",
  strand = "*",
  yaxis = TRUE,
  xaxis = FALSE,
  xlab = FALSE,
  ylab = TRUE,
  xlab.text = "",
  ylab.text = "",
  legend.colnames = "",
  size = c(0.8, 1),
  main = NULL,
  pValue = FALSE,
  cex.main = 0.7,
  cex.ylab = 0.6,
  cex.legend = 0.5,
  OrgDb = NULL,
  TxDb = NULL,
  verbose = TRUE,
  top.fraction.criteria = "maxcount",
  allow.whole.chromosome = FALSE,
  ...
)

Arguments

Details

The locationplot methods visualises how fractions are distributed over a larger region of genes on one chromosome. It takes and ASEset object as well as additional information on plot type (see barplot), strand type (see getAlleleCounts), colouring, as well as annotation. The annotation is taken either from the bioconductor OrgDb-sets, the TxDb sets or both. It is obviously important to make sure that the genome build used is the same as used in aligning the RNA-seq data.

Author(s)

Jesper R. Gadin, Lasse Folkersen

See Also

• The ASEset class which the locationplot function can be called up on.

Examples

data(ASEset)
locationplot(ASEset)

#SNPs are plotted in the order in which they are found. 
#This can be sorted according to location as follows:
locationplot(ASEset[order(start(rowRanges(ASEset))),])

#for ASEsets with fewer SNPs the 'count' type plot is
# useful for detailed visualization.
locationplot(ASEset,type='count',strand='*')

Description

Identifies the positions of SNPs found in BamGR reads.

Usage

scanForHeterozygotes(BamList, ...)

## S4 method for signature 'GAlignmentsList'
scanForHeterozygotes(
  BamList,
  minimumReadsAtPos = 20,
  maximumMajorAlleleFrequency = 0.9,
  minimumMinorAlleleFrequency = 0.1,
  minimumBiAllelicFrequency = 0.9,
  verbose = TRUE,
  ...
)

Arguments

Details

This function scans all reads stored in a GAlignmentsList for possible heterozygote positions. The user can balance the sensitivity of the search by modifying the minimumReadsAtPos, maximumMajorAlleleFrequency and minimumBiAllelicFrequency arguments.

Value

scanForHeterozygotes returns a GRanges object with the SNPs for the BamList object that was used as input.

Author(s)

Jesper R. Gadin, Lasse Folkersen

See Also

• The getAlleleCounts which is a function that count the number of reads overlapping a site.

Examples

data(reads)
s <- scanForHeterozygotes(reads,verbose=FALSE)

Description

old version of an ASEset which needs to be updated

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

##load eample data (Not Run)  
#data(ASEset.old)

Description

ASEset with simulated data with SNPs within the first 200bp of chromosome 17, which is required to have example data for the refAllele function.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

##load eample data (Not Run)  
#data(ASEset.sim)

Description

count alleles and create an ASEset direct from bam file instead of reading into R first.

Usage

ASEsetFromBam(gr, ...)

## S4 method for signature 'GRanges'
ASEsetFromBam(
  gr,
  pathToDir,
  PE = TRUE,
  flagsMinusStrand = c(83, 163),
  flagsPlusStrand = c(99, 147),
  strandUnknown = FALSE,
  ...
)

Arguments

Details

counts the alleles in a bam file based on GRanges positions.

Author(s)

Jesper R. Gadin

Examples

data(GRvariants)
gr <- GRvariants

##no execution at the moment
#pathToDir <- system.file('inst/extdata/ERP000101_subset', package='AllelicImbalance')
#a <- ASEsetFromBam(gr, pathToDir)

Description

Generates lattice barplots for ASEset objects. Two levels of plotting detail are provided: a detailed barplot of read counts by allele useful for fewer samples and SNPs, and a less detailed barplot of the fraction of imbalance, useful for more samples and SNPs.

Usage

barplotLatticeFraction(identifier, ...)

barplotLatticeCounts(identifier, ...)

Arguments

Details

filter.pValue.fraction is intended to remove p-value annotation with very large difference in frequency, which could just be a sequencing mistake. This is to avoid p-values like 1e-235 or similar.

sampleColourUser specified colours, either given as named colours ('red', 'blue', etc) or as hexadecimal code. Can be either length 1 for all samples, or else of a length corresponding to the number of samples for individual colouring.

Author(s)

Jesper R. Gadin, Lasse Folkersen

See Also

• The ASEset class which the barplot function can be called up on.

Examples

a <- ASEset
name <- rownames(a)[1]

barplotLatticeFraction(identifier=name, x=a, astrand="+") 
barplotLatticeCounts(identifier=name,  x=a, astrand="+")

Description

Performs a binomial test on an ASEset object.

Usage

## S4 method for signature 'ASEset'
binom.test(x, n = "*")

Arguments

Details

the test can only be applied to one strand at the time.

Value

binom.test returns a matrix

Author(s)

Jesper R. Gadin, Lasse Folkersen

See Also

• The chisq.test which is another test that can be applied on an ASEset object.

Examples

#load example data
data(ASEset)

#make a binomial test
binom.test(ASEset,'*')

Description

Performs a chisq.test on an ASEset object.

Usage

## S4 method for signature 'ASEset'
chisq.test(x, y = "*")

Arguments

Details

The test is performed on one strand in an ASEset object.

Value

chisq.test returns a matrix with the chisq.test P-value for each SNP and sample

Author(s)

Jesper R. Gadin, Lasse Folkersen

See Also

• The binom.test which is another test that can be applied on an ASEset object.

Examples

#load example data
data(ASEset)

#make a chi-square test on default non-stranded strand 
chisq.test(ASEset)

Description

count alleles before creating ASEse.

Usage

countAllelesFromBam(gr, ...)

## S4 method for signature 'GRanges'
countAllelesFromBam(
  gr,
  pathToDir,
  flag = NULL,
  scanBamFlag = NULL,
  return.class = "array",
  verbose = TRUE,
  ...
)

Arguments

Details

counts the alleles in a bam file based on GRanges positions.

Important excerpt from the details section of the internal applyPileups function: Regardless of 'param' values, the algorithm follows samtools by excluding reads flagged as unmapped, secondary, duplicate, or failing quality control.

Author(s)

Jesper R. Gadin

Examples

data(GRvariants)
gr <- GRvariants

##not run at the moment
#pathToDir <- system.file('inst/extdata/ERP000101_subset', package='AllelicImbalance')
#ar <- countAllelesFromBam(gr, pathToDir)

Description

Get coverage per nucleotide for reads covering a region

Usage

coverageMatrixListFromGAL(BamList, ...)

## S4 method for signature 'GAlignmentsList'
coverageMatrixListFromGAL(BamList, strand = "*", ignore.empty.bam.row = TRUE)

Arguments

Details

a convenience function to get the coverage from a list of reads stored in GAlignmnetsList, and returns by default a list with one matrix, and information about the genomic start and stop positions.

Author(s)

Jesper R. Gadin

Examples

r <- reads
seqlevels(r) <- '17'
covMatList <- coverageMatrixListFromGAL(BamList=r, strand='+')

Description

Create mapbias array from genotype matrix requires genotype information

Usage

defaultMapBias(x, ...)

## S4 method for signature 'ASEset'
defaultMapBias(x, return.class = "array")

Arguments

Details

Default mapbias will be 0.5 for bi-allelic snps and 1 for homozygots. For genotypes with NA, 0.5 will be placed on all four alleles. Therefore tri-allelic can not be used atm. Genotype information has to be placed in the genotype(x) assay.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load example data
data(ASEset.sim)

fasta <- system.file('extdata/hg19.chr17.subset.fa', package='AllelicImbalance')
refAllele(ASEset.sim,fasta=fasta)
a <- refAllele(ASEset.sim,fasta=fasta)

Description

used to populate the phase slot in an ASEset object

Usage

defaultPhase(i, ...)

## S4 method for signature 'numeric'
defaultPhase(i, j, ...)

Arguments

Details

will set everything to 0

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

i <- 5
j <- 10
defaultPhase(i,j)

Description

detection of AllelicImbalance

Usage

detectAI(x, ...)

## S4 method for signature 'ASEset'
detectAI(
  x,
  return.class = "DetectedAI",
  strand = "*",
  threshold.frequency = 0,
  threshold.count.sample = 1,
  threshold.delta.frequency = 0,
  threshold.pvalue = 0.05,
  inferGenotype = FALSE,
  random.ref = FALSE,
  function.test = "binom.test",
  verbose = TRUE,
  gc = FALSE,
  biasMatrix = FALSE
)

Arguments

Details

threshold.frequency is the least fraction needed to classify as bi tri or quad allelic SNPs. If 'all' then all of bi tri and quad allelic SNPs will use the same threshold. Everything under the treshold will be regarded as noise. 'all' will return a matrix with snps as rows and uni bi tri and quad will be columns. For this function Anything that will return TRUE for tri-allelicwill also return TRUE for uni and bi-allelic for the same SNP an Sample.

return.type 'ref' return only AI when reference allele is more expressed. 'alt' return only AI when alternative allele is more expressed or 'all' for both 'ref' and 'alt' alleles. Reference allele is the one present in the reference genome on the forward strand.

threshold.delta.frequency and function.test will use the value in mapBias(x) as expected value.

function.test will use the two most expressed alleles for testing. Make therefore sure there are no tri-allelic SNPs or somatic mutations among the SNPs in the ASEset.

inferGenotype(), set TRUE it should be used with as much samples as possible. If you split up the samples and run detectAI() on each sample separately, please make sure you have inferred the genotypes in before hand, alternatively used the genotypes detected by another variantCaller or chip-genotypes. Use ONLY biallelic genotypes.

Author(s)

Jesper R. Gadin

Examples

#load example data
data(ASEset)
a <- ASEset

dai <- detectAI(a)

Description

Object that holds results from AI detection.

Usage

referenceFrequency(x, ...)

## S4 method for signature 'DetectedAI'
referenceFrequency(x, return.class = "array")

thresholdFrequency(x, ...)

## S4 method for signature 'DetectedAI'
thresholdFrequency(x, return.class = "array")

thresholdCountSample(x, ...)

## S4 method for signature 'DetectedAI'
thresholdCountSample(x, return.class = "array")

thresholdDeltaFrequency(x, ...)

## S4 method for signature 'DetectedAI'
thresholdDeltaFrequency(x, return.class = "array")

thresholdPvalue(x, ...)

## S4 method for signature 'DetectedAI'
thresholdPvalue(x, return.class = "array")

Arguments

Details

The DetectedAI-class contains

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

data(ASEset)
a <- ASEset
dai <- detectAI(a)

#summary(gba)
#write.tables(dai)

Description

plot functions for the DetectedAI-class

Usage

frequency_vs_threshold_variable_plot(x, ...)

## S4 method for signature 'DetectedAI'
frequency_vs_threshold_variable_plot(
  x,
  var = "threshold.count.sample",
  hetOverlay = TRUE,
  smoothscatter = FALSE
)

detectedAI_vs_threshold_variable_plot(x, ...)

## S4 method for signature 'DetectedAI'
detectedAI_vs_threshold_variable_plot(
  x,
  var = "threshold.count.sample",
  summaryOverSamples = "sum",
  hetOverlay = TRUE,
  smoothscatter = FALSE
)

reference_frequency_density_vs_threshold_variable_plot(x, ...)

## S4 method for signature 'DetectedAI'
reference_frequency_density_vs_threshold_variable_plot(
  x,
  var = "threshold.count.sample"
)

detectedAI_vs_threshold_variable_multigraph_plot(x, ...)

## S4 method for signature 'DetectedAI'
detectedAI_vs_threshold_variable_multigraph_plot(x, ncol = 2, ...)

frequency_vs_threshold_variable_multigraph_plot(x, ...)

## S4 method for signature 'DetectedAI'
frequency_vs_threshold_variable_multigraph_plot(x, ncol = 2, ...)

reference_frequency_density_vs_threshold_variable_multigraph_plot(x, ...)

## S4 method for signature 'DetectedAI'
reference_frequency_density_vs_threshold_variable_multigraph_plot(
  x,
  ncol = 2,
  ...
)

Arguments

Details

plot helper functions. The documentation will be improved before next release.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#some example code here
#generate example
data(ASEset)
a <- ASEset
dai <- detectAI(a, 
			threshold.count.sample=1:50,
			threshold.frequency=seq(0,0.5,by=0.01),
			threshold.delta.frequency=seq(0,0.5,by=0.01),
			threshold.pvalue=rev(seq(0.001,0.05, by=0.005))
)

frequency_vs_threshold_variable_plot(dai)
detectedAI_vs_threshold_variable_plot(dai)
detectedAI_vs_threshold_variable_multigraph_plot(dai)
frequency_vs_threshold_variable_multigraph_plot(dai)

Description

Summary helper functions for the DetectedAI-class

Usage

frequency_vs_threshold_variable_summary(x, ...)

## S4 method for signature 'DetectedAI'
frequency_vs_threshold_variable_summary(
  x,
  var = "threshold.count.sample",
  return.class = "matrix",
  ...
)

detectedAI_vs_threshold_variable_summary(x, ...)

## S4 method for signature 'DetectedAI'
detectedAI_vs_threshold_variable_summary(x, var = "threshold.count.sample")

usedSNPs_vs_threshold_variable_summary(x, ...)

## S4 method for signature 'DetectedAI'
usedSNPs_vs_threshold_variable_summary(x, var = "threshold.count.sample")

Arguments

Details

Summary helper functions. The documentation will be improved before next release.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#some example code here
#generate example
data(ASEset)
a <- ASEset
dai <- detectAI(a, 
			threshold.count.sample=1:50,
			threshold.frequency=seq(0,0.5,by=0.01),
			threshold.delta.frequency=seq(0,0.5,by=0.01),
			threshold.pvalue=rev(seq(0.001,0.05, by=0.005))
)

frequency_vs_threshold_variable_summary(dai)

Description

Summarizes information to ease creating plots

Usage

fractionPlotDf(x, snp, strand = "*", top.fraction.criteria = "maxcount", ...)

## S4 method for signature 'ASEset'
fractionPlotDf(x, snp, strand = "*", top.fraction.criteria = "maxcount", ...)

Arguments

Details

Main purpose is to reduce the amount of overall code and ease maintenance.

top.fraction.criteria can take three options, maxcount, ref and phase. The top allele will be every second row in the data frame, with start from row 2. The maxcount argument will put the allele with most reads on top of the bivariate fraction. Similarly the ref argument will put always the reference allele on top. The phase arguments puts the maternal phase always on top. The top.fraction.criteria for the ref or phase arguments requires that both ref and alt is set in mcols(ASEset).

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#test on example ASEset
data(ASEset)
a <- ASEset
df <- fractionPlotDf(a, 1, strand="+")

Description

A wrapper to make a global analysis based on paths for BAM, VCF and GFF files

Usage

gba(pathBam, ...)

## S4 method for signature 'character'
gba(pathBam, pathVcf, pathGFF = NULL, verbose)

Arguments

Author(s)

Jesper R. Gadin

Examples

#empty as function doesn't exist

Description

genomatrix is an example of a matrix with genotypes

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

##load eample data (Not Run)  
#data(genomatrix)

Description

used to convert the genomatrix from the visually friendly matrix to phase array.

Usage

genotype2phase(x, ...)

## S4 method for signature 'matrix'
genotype2phase(
  x,
  ref = NULL,
  return.class = "array",
  levels = c("A", "C", "G", "T"),
  ...
)

Arguments

Details

To not introduce redundant information in the ASEset object, the genotype matrix is translated to a phase matrix, containing the same information. Does not allow tri-allelic or multi-allelic SNPs, and if present the multi-allelic SNPs will lose the least occuring genotype.

This function can handle indels, but if the reference allele is not provided, the rank matrix which is temporary created might use lots of memory, depending on the amount of indels among the genotypes. As conclusion, it is preferable to send in reference genome when converting to phase.

levels information is only important if the reference allele has to be guessed, and so if reference information is provided, the levels argument can be ignored.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load example data
data(genomatrix)
data(ASEset)
p <- genotype2phase(genomatrix, ref(ASEset))

Description

Given the positions of known SNPs, this function returns allele counts from a BamGRL object

Usage

getAlleleCounts(BamList, ...)

## S4 method for signature 'GAlignmentsList'
getAlleleCounts(
  BamList,
  GRvariants,
  strand = "*",
  return.class = "list",
  verbose = TRUE,
  ...
)

Arguments

Details

This function is used to retrieve the allele counts from specified positions in a set of RNA-seq reads. The BamList argument will typically have been created using the impBamGAL function on bam-files. The GRvariants is either a GRanges with user-specified locations or else it is generated through scanning the same bam-files as in BamList for heterozygote locations (e.g. using scanForHeterozygotes). The GRvariants will currently only accept locations having width=1, corresponding to bi-allelic SNPs. In the strand argument, specifying '*' is the same as retrieving the sum count of '+' and '-' reads (and unknown strand reads in case these are found in the bam file). '*' is the default behaviour and can be used when the RNA-seq experiments strand information is not available.

Value

getAlleleCounts returns a list of several data.frame objects, each storing the count data for one SNP.

Author(s)

Jesper R. Gadin, Lasse Folkersen

See Also

• The scanForHeterozygotes which is a function to find possible heterozygote sites in a GenomicAlignments object

Examples

#load example data
data(reads)
data(GRvariants)


#get counts at the three positions specified in GRvariants
alleleCount <- getAlleleCounts(BamList=reads,GRvariants,
strand='*')

#if the reads had contained stranded data, these two calls would
#have given the correct input objects for getAlleleCounts
alleleCountPlus <- getAlleleCounts(BamList=reads,GRvariants,
strand='+')
alleleCountMinus <- getAlleleCounts(BamList=reads,GRvariants,
strand='-')

Description

Given the positions of known SNPs, this function returns allele quality from a BamGRL object

Usage

getAlleleQuality(BamList, ...)

## S4 method for signature 'GAlignmentsList'
getAlleleQuality(
  BamList,
  GRvariants,
  fastq.format = "illumina.1.8",
  return.class = "array",
  verbose = TRUE,
  ...
)

Arguments

Details

This function is used to retrieve the allele quality strings from specified positions in a set of RNA-seq reads. The BamList argument will typically have been created using the impBamGAL function on bam-files. The GRvariants is either a GRanges with user-specified locations or else it is generated through scanning the same bam-files as in BamList for heterozygote locations (e.g. using scanForHeterozygotes). The GRvariants will currently only accept locations having width=1, corresponding to bi-allelic SNPs. The strand type information will be kept in the returned object. If the strand is marked as unknown "*", it will be forced to the "+" strand.

quaity information is extracted from the BamList object, and requires the presence of mcols(BamList)[["qual"]] to contain quality sequences.

Value

getAlleleQuality returns a list of several data.frame objects, each storing the count data for one SNP.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load example data
data(reads)
data(GRvariants)

#get counts at the three positions specified in GRvariants
alleleQualityArray <- getAlleleQuality(BamList=reads,GRvariants)

#place in ASEset object
alleleCountsArray <- getAlleleCounts(BamList=reads,GRvariants,
                     strand='*', return.class="array")

	a <- ASEsetFromArrays(GRvariants, countsUnknown = alleleCountsArray)
	aquals(a) <- alleleQualityArray

Description

Given a character vector with genesymbols and an OrgDb object, this function returns a GRanges giving the coordinates of the genes.

Usage

getAreaFromGeneNames(genesymbols, ...)

## S4 method for signature 'character'
getAreaFromGeneNames(
  genesymbols,
  OrgDb,
  leftFlank = 0,
  rightFlank = 0,
  na.rm = FALSE,
  verbose = TRUE
)

Arguments

Details

This function is a convenience function that can be used to determine which genomic coordinates to specify to e.g. impBamGAL when retrieving reads.

The function cannot handle genes that do not exist in the annotation. To remove these please set the na.rm=TRUE.

Value

getAreaFromGeneNames returns a GRanges object with genomic coordinates around the specified genes

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load example data
data(ASEset)

#get counts at the three positions specified in GRvariants
library(org.Hs.eg.db )
searchArea<-getAreaFromGeneNames(c('PAX8','TLR7'), org.Hs.eg.db)

Description

an allele frequency array

Usage

getDefaultMapBiasExpMean(alleleCountList, ...)

getDefaultMapBiasExpMean3D(alleleCountList, ...)

## S4 method for signature 'list'
getDefaultMapBiasExpMean(alleleCountList)

## S4 method for signature 'ANY'
getDefaultMapBiasExpMean3D(alleleCountList)

Arguments

Details

This function will assume there is no bias that comes from the mapping of reads, and therefore create a matrix with expected frequency of 0.5 for each allele.

Value

getDefaultMapBiasExpMean returns a matrix with a default expected mean of 0.5 for every element.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load example data
data(ASEset)
#access SnpAfList
alleleCountList <- alleleCounts(ASEset)
#get default map bias exp mean
matExpMean <- getDefaultMapBiasExpMean(alleleCountList)

Description

Given a GRanges object of SNPs and a SNPlocs annotation, this function attempts to replace the names of the GRanges object entries with rs-IDs.

Usage

getSnpIdFromLocation(GR, ...)

## S4 method for signature 'GRanges'
getSnpIdFromLocation(GR, SNPloc, return.vector = FALSE, verbose = TRUE)

Arguments

Details

This function is used to try to identify the rs-IDs of SNPs in a GRanges object.

Value

getSnpIdFromLocation returns the same GRanges object it was given with, but with updated with rs.id information.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

is_32bit_windows <- .Platform$OS.type == "windows" &&
                  .Platform$r_arch == "i386"
if (!is_32bit_windows && require(SNPlocs.Hsapiens.dbSNP144.GRCh37)) {
	#load example data
	data(ASEset)

  #get counts at the three positions specified in GRvariants
  updatedGRanges <- getSnpIdFromLocation(rowRanges(ASEset),
    SNPlocs.Hsapiens.dbSNP144.GRCh37)
}

Description

Object that holds results from a global AI analysis including reference bias estimations and AI detection.

Arguments

Details

The GlobalAnalysis-class contains summaries and "pre-configured and pre-calculated lattice plots" needed to create an AI-report

Value

An object of class GlobalAnalysis containing all data to make report.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

data(ASEset)
#a <- ASEset
#gba <- gba(a)

#report(gba)
#write.tables(gba)
#graphs(gba)
#as.list(gba)

Description

this data is a GRanges object that contains the ranges for three example SNPs.

Author(s)

Jesper R. Gadin, Lasse Folkersen

See Also

• The reads which is another example object

Examples

#load example data
data(GRvariants)

Description

uses base graphics hist plot

Usage

## S4 method for signature 'ASEset'
hist(x, strand = "*", type = "mean", log = 1, ...)

Arguments

Details

The histogram will show the density over frequencies for each sample

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

##load example data

#data(ASEset)
#a <- ASEset
#hist(a)

Description

apply on list of variables to be put in the local environment

Usage

implodeList.old(x)

Arguments

Details

help the propagation of e.g. graphical paramters

Author(s)

Jesper R. Gadin

Examples

lst <- list(hungry='yes', thirsty='no')
implodeList.old(lst)
#the check ls()
 ls()

Description

Imports a specified genomic region from a bam file using a GRanges object as search area.

Usage

impBamGAL(UserDir, ...)

## S4 method for signature 'character'
impBamGAL(
  UserDir,
  searchArea,
  files = NULL,
  XStag = FALSE,
  verbose = TRUE,
  ...
)

Arguments

Details

If the sequence data is strand-specific you may want to set XStag=TRUE. The strand specific information has then to be stored in the meta columns with column name 'XS'. If the aligner did not set the XS-tag and the data is strand- specific it is still be possible to infer the strand from the bit flags after importing the reads to R. Depending on the strand-specific protocol different combinations of the flags will have to be used. For illumina fr-secondstrand, 83 and 163 are minus strand reads and 99 and 147 are plus strand reads.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#Declare searchArea
searchArea <- GRanges(seqnames=c('17'), ranges=IRanges(79478301,79478361))

#Relative or full path
pathToFiles <- system.file('extdata/ERP000101_subset', package='AllelicImbalance')

#all files in directory
reads <- impBamGAL(pathToFiles,searchArea,verbose=FALSE)
#specified files in directory
reads <- impBamGAL(pathToFiles,searchArea,
			files=c("ERR009160.bam", "ERR009167.bam"),verbose=FALSE)

Description

Imports bla bal bal a specified genomic region from a bam file using a GenomicRanges object as search area.

Usage

impBamGRL.old(UserDir, searchArea, verbose = TRUE)

Arguments

Details

These functions are right on tahea wrappers to import bam files into R and store them into either GRanges, GAlignments or GappedAlignmentpairs objects.

It is recommended to use the impBamGAL() which takes information of gaps into account. It is also possible to use the other variants as well, but then pre-filtering becomes important keps to understand because gapped, intron-spanning reads will cause problems. This is because the GRanges objects can not handle if gaps are present and will then give a wrong result when calculating the allele (SNP) count table.

Value

impBamGRL returns a GRangesList object containing the RNA-seq reads in the region defined by the searchArea argument. impBamGAL returns a list with GAlignments objects containing the RNA-seq reads in the region defined by the searchArea argument. funImpBamGAPL returns a list with GappedAlignmentPairs object containing the RNA-seq reads in the region defined by the searchArea argument.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#Declare searchArea
searchArea <- GRanges(seqnames=c('17'), ranges=IRanges(79478301,79478361))

#Relative or full path
pathToFiles <- system.file('extdata/ERP000101_subset', package='AllelicImbalance')

Description

Imports a selection of a bcf file or files specified by a GenomicRanges object as search area.

Usage

impBcfGRL(UserDir, ...)

## S4 method for signature 'character'
impBcfGRL(UserDir, searchArea = NULL, verbose = TRUE, ...)

impBcfGR(UserDir, ...)

## S4 method for signature 'character'
impBcfGR(UserDir, searchArea = NULL, verbose = TRUE, ...)

Arguments

Details

A wrapper to import bcf files into R in the form of GenomicRanges objects.

Value

BcfImpGRList returns a GRangesList object. BcfImpGR returns one GRanges object of all unique entries from one or more bcf files.

Note

Make sure there is a complementary index file *.bcf.csi for each bcf file in UserDir. If there is not, then the functions impBcfGRL and impBcfGR will try to create them.

Author(s)

Jesper R. Gadin, Lasse Folkersen

See Also

• The impBamGRL for importing bam files

• The getAlleleCounts for how to get allele(SNP) counts

• The scanForHeterozygotes for how to find possible heterozygote positions

Examples

#Declare searchArea
searchArea <- GRanges(seqnames=c('17'), ranges=IRanges(79478301,79478361))

#Relative or full path
pathToFiles <- system.file('extdata/ERP000101_subset', package='AllelicImbalance')

#import
reads <- impBcfGRL(pathToFiles, searchArea, verbose=FALSE)

Description

inference of SNPs

Usage

inferAlleles(
  x,
  strand = "*",
  return.type = "bi",
  threshold.frequency = 0,
  threshold.count.sample = 1,
  inferOver = "eachSample",
  allow.NA = FALSE
)

Arguments

Details

threshold.frequency is the least fraction needed to classify as bi tri or quad allelic SNPs. If 'all' then all of bi tri and quad allelic SNPs will use the same threshold. Everything under the treshold will be regarded as noise. 'all' will return a matrix with snps as rows and uni bi tri and quad will be columns. For this function Anything that will return TRUE for tri-allelicwill also return TRUE for uni and bi-allelic for the same SNP an Sample.

Author(s)

Jesper R. Gadin

Examples

data(ASEset)
i <- inferAlleles(ASEset)

Description

inference of the alternate allele based on count data

Arguments

Details

The inference essentially ranks all alleles and the most expressed allele not declared as reference will be inferred as the alternative allele. At the moment only inference of bi-allelic alternative alleles are available.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load data
data(ASEset)

alt <- inferAltAllele(ASEset)

Description

inference of genotypes

Usage

inferGenotypes(
  x,
  strand = "*",
  return.class = "matrix",
  return.allele.allowed = "bi",
  threshold.frequency = 0,
  threshold.count.sample = 1
)

Arguments

Details

Oftern necessary information to link AI to SNPs outside coding region

Author(s)

Jesper R. Gadin

Examples

data(ASEset)
g <- inferGenotypes(ASEset)

Description

Functions to construct ASEset objects

Usage

ASEsetFromCountList(
  rowRanges,
  countListUnknown = NULL,
  countListPlus = NULL,
  countListMinus = NULL,
  colData = NULL,
  mapBiasExpMean = NULL,
  phase = NULL,
  aquals = NULL,
  verbose = FALSE,
  ...
)

ASEsetFromArrays(
  rowRanges,
  countsUnknown = NULL,
  countsPlus = NULL,
  countsMinus = NULL,
  colData = NULL,
  mapBiasExpMean = NULL,
  phase = NULL,
  genotype = NULL,
  aquals = NULL,
  verbose = FALSE,
  ...
)

Arguments

Details

The resulting ASEset object is based on the RangedSummarizedExperiment class, and will therefore inherit the same accessors and ranges operations.

If both countListPlus and countListMinus are given they will be used to calculate countListUnknown, which is the sum of the plus and minus strands.

countListPlus, countListMinus and countListUnknown are i.e. the outputs from the getAlleleCounts function.

aquals is new for the devel branch and will be changed slighly before the relase to include better granularity.

Value

ASEsetFromCountList returns an ASEset object.

Note

ASEsetFromCountList requires the same input data as a RangedSummarizedExperiment, but with minimum one assay for the allele counts.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#make example alleleCountListPlus
set.seed(42)
countListPlus <- list()
snps <- c('snp1','snp2','snp3','snp4','snp5')
for(snp in snps){
count<-matrix(rep(0,16),ncol=4,dimnames=list(
c('sample1','sample2','sample3','sample4'),
c('A','T','G','C')))
#insert random counts in two of the alleles 
for(allele in sample(c('A','T','G','C'),2)){
count[,allele]<-as.integer(rnorm(4,mean=50,sd=10))
}
countListPlus[[snp]] <- count
}

#make example alleleCountListMinus
countListMinus <- list()
snps <- c('snp1','snp2','snp3','snp4','snp5')
for(snp in snps){
count<-matrix(rep(0,16),ncol=4,dimnames=list(
c('sample1','sample2','sample3','sample4'),
c('A','T','G','C')))
#insert random counts in two of the alleles 
for(allele in sample(c('A','T','G','C'),2)){
count[,allele]<-as.integer(rnorm(4,mean=50,sd=10))
}
countListMinus[[snp]] <- count
}


#make example rowRanges
rowRanges <- GRanges(
seqnames = Rle(c('chr1', 'chr2', 'chr1', 'chr3', 'chr1')),
         ranges = IRanges(1:5, width = 1, names = head(letters,5)),
         snp = paste('snp',1:5,sep='')
         )
#make example colData
colData <- DataFrame(Treatment=c('ChIP', 'Input','Input','ChIP'), 
 row.names=c('ind1','ind2','ind3','ind4'))

#make ASEset 
a <- ASEsetFromCountList(rowRanges, countListPlus=countListPlus, 
countListMinus=countListMinus, colData=colData)

Description

Functions to construct DetectedAI objects

Usage

DetectedAIFromArray(
  x = "ASEset",
  strand = "*",
  reference.frequency = NULL,
  threshold.frequency = NULL,
  threshold.count.sample = NULL,
  threshold.delta.frequency = NULL,
  threshold.pvalue = NULL,
  threshold.frequency.names = NULL,
  threshold.count.sample.names = NULL,
  threshold.delta.frequency.names = NULL,
  threshold.pvalue.names = NULL,
  ...
)

Arguments

Details

produces a class container for reference bias calculations

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

data(ASEset)
a <- ASEset
dai <- detectAI(a)

Description

Functions to construct GlobalAnalysis objects

Usage

GAnalysis(x = "ASEset", ...)

Arguments

Details

produces a class container for a global analysis

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

data(ASEset)
a <- ASEset
# gba <- gba(a)

Description

Functions to construct RiskVariant objects

Usage

RiskVariantFromGRangesAndPhaseArray(x, phase, ...)

Arguments

Details

produces a class container for reference bias calculations

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

data(ASEset)
#p <- getPhaseFromSomewhere
#rv <- RiskVariantFromGRangesAndPhaseArray(x=GRvariants, phase=p)

Description

adds a very customizable legend function for AllelicImbalance barplots.

Usage

legendBarplot(
  lowerLeftCorner,
  size,
  rownames,
  colnames,
  boxsize = 1,
  boxspace = 1,
  fgCol,
  bgCol,
  ylegendPos = 1,
  xlegendPos = 0.96,
  cex = 1
)

Arguments

Details

the function is preferably called from within the AllelicImbalance barplot method.

Author(s)

Jesper R. Gadin

Examples

#code placeholders
#< create a barplot with legend >
#< add legend >

Description

Object that holds results from AI detection.

Usage

pvalue(x, ...)

## S4 method for signature 'LinkVariantAlmlof'
pvalue(x)

Arguments

Details

The LinkVariantAlmlof-class contains

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#some code

Description

plot an object of type LinkVariantAlmlof

Usage

plot(x, y, ...)

## S4 method for signature 'LinkVariantAlmlof,ANY'
plot(x, y, ...)

Arguments

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

data(ASEset) 
a <- ASEset
# Add phase
set.seed(1)
p1 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p2 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p <- matrix(paste(p1,sample(c("|","|","/"), size=nrow(a)*ncol(a), replace=TRUE), p2, sep=""),
	nrow=nrow(a), ncol(a))

phase(a) <- p

#add alternative allele information
mcols(a)[["alt"]] <- inferAltAllele(a)

#init risk variants
p.ar <- phaseMatrix2Array(p)
rv <- RiskVariantFromGRangesAndPhaseArray(x=GRvariants, phase=p.ar)

#colnames has to be samea and same order in ASEset and RiskVariant
colnames(a) <- colnames(rv)

# in this example each and every snp in the ASEset defines a region
r1 <- granges(a)

# in this example two overlapping subsets of snps in the ASEset defines the region
r2 <- split(granges(a)[c(1,2,2,3)],c(1,1,2,2))

# link variant almlof (lva)
lv1 <- lva(a, rv, r1)
lv2 <- lva(a, rv, r2)
plot(lv2[1])

Description

make an almlof regression for arrays

Usage

lva(x, ...)

## S4 method for signature 'ASEset'
lva(
  x,
  rv,
  region,
  settings = list(),
  return.class = "LinkVariantAlmlof",
  type = "lm",
  verbose = FALSE,
  covariates = matrix(),
  ...
)

Arguments

Details

internal method that takes one array with results from regionSummary and one matrix with group information for each risk SNP (based on phase)

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

data(ASEset) 
a <- ASEset
# Add phase
set.seed(1)
p1 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p2 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p <- matrix(paste(p1,sample(c("|","|","/"), size=nrow(a)*ncol(a), replace=TRUE), p2, sep=""),
	nrow=nrow(a), ncol(a))

phase(a) <- p

#add alternative allele information
mcols(a)[["alt"]] <- inferAltAllele(a)

#init risk variants
p.ar <- phaseMatrix2Array(p)
rv <- RiskVariantFromGRangesAndPhaseArray(x=GRvariants, phase=p.ar)

#colnames has to be samea and same order in ASEset and RiskVariant
colnames(a) <- colnames(rv)

# in this example each and every snp in the ASEset defines a region
r1 <- granges(a)

#use GRangesList to merge and use regions defined by each element of the
#GRangesList
r1b <- GRangesList(r1)
r1c <- GRangesList(r1, r1)

# in this example two overlapping subsets of snps in the ASEset defines the region
r2 <- split(granges(a)[c(1,2,2,3)],c(1,1,2,2))

# link variant almlof (lva)
lva(a, rv, r1)
lva(a, rv, r1b)
lva(a, rv, r1c)
lva(a, rv, r2)

# Use covariates (integers or nuemric)
cov <- data.frame(age=sample(20:70, ncol(a)), sex=rep(c(1,2), each=ncol(a)/2),  
row.names=colnames(a))
lva(a, rv, r1, covariates=cov)
lva(a, rv, r1b, covariates=cov)
lva(a, rv, r1c, covariates=cov)
lva(a, rv, r2, covariates=cov)

# link variant almlof (lva), using nlme
a2 <- a
ac <- assays(a2)[["countsPlus"]]
jit <- sample(c(seq(-0.10,0,length=5), seq(0,0.10,length=5)), size=length(ac) , replace=TRUE)
assays(a2, withDimnames=FALSE)[["countsPlus"]] <- round(ac * (1+jit),0)
ab <- cbind(a, a2)
colData(ab)[["subject.group"]] <- c(1:ncol(a),1:ncol(a))
rv2 <- rv[,c(1:ncol(a),1:ncol(a))]
colnames(ab) <- colnames(rv2)

lva(ab, rv2, r1, type="nlme")
lva(ab, rv2, r1b, type="nlme")
lva(ab, rv2, r1c, type="nlme")
lva(ab, rv2, r2, type="nlme")

Description

make an almlof regression for arrays (internal function)

Usage

lva.internal(x, ...)

## S4 method for signature 'array'
lva.internal(
  x,
  grp,
  element = 3,
  type = "lm",
  subject = NULL,
  covariates = matrix(),
  ...
)

Arguments

Details

internal method that takes one array with results from regionSummary and one matrix with group information for each risk SNP (based on phase). Input and output objects can change format slightly in future.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

data(ASEset) 
a <- ASEset
# Add phase
set.seed(1)
p1 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p2 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p <- matrix(paste(p1,sample(c("|","|","/"), size=nrow(a)*ncol(a), replace=TRUE), p2, sep=""),
	nrow=nrow(a), ncol(a))

phase(a) <- p

#add alternative allele information
mcols(a)[["alt"]] <- inferAltAllele(a)

# in this example two overlapping subsets of snps in the ASEset defines the region
region <- split(granges(a)[c(1,2,2,3)], c(1,1,2,2))
rs <- regionSummary(a, region, return.class="array", return.meta=FALSE)

# use  (change to generated riskSNP phase later)
phs <- array(c(phase(a,return.class="array")[1,,c(1, 2)], 
			 phase(a,return.class="array")[2,,c(1, 2)]), dim=c(20,2,2))
grp <- matrix(2, nrow=dim(phs)[1], ncol=dim(phs)[2])		 
grp[(phs[,,1] == 0) & (phs[,,2] == 0)] <- 1
grp[(phs[,,1] == 1) & (phs[,,2] == 1)] <- 3
#only use mean.fr at the moment, which is col 3
lva.internal(x=assays(rs)[["rs1"]],grp=grp, element=3)

Description

Replaces all selected positions in a fasta file with the character N

Usage

makeMaskedFasta(fastaIn, ...)

## S4 method for signature 'character'
makeMaskedFasta(
  fastaIn,
  fastaOut,
  posToReplace,
  splitOnSeqlevels = TRUE,
  verbose = TRUE
)

Arguments

Author(s)

Jesper R. Gadin

Examples

data(ASEset.sim)
gr <- rowRanges(ASEset.sim)
fastaIn <- system.file('extdata/hg19.chr17.subset.fa', package='AllelicImbalance')
makeMaskedFasta(fastaIn=fastaIn, fastaOut="fastaOut",posToReplace=gr)

Description

Create a matrix of bias for the reference allele

Usage

mapBiasRef(x, ...)

## S4 method for signature 'ASEset'
mapBiasRef(x)

Arguments

Details

select the expected frequency for the reference allele

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load example data
data(ASEset)
a <- ASEset

mat <- mapBiasRef(a)

Description

filter on minCountFilt snps

Usage

minCountFilt(x, ...)

## S4 method for signature 'ASEset'
minCountFilt(
  x,
  strand = "*",
  threshold.counts = 1,
  sum = "all",
  replace.with = "zero",
  return.class = "ASEset"
)

Arguments

Details

Description info here

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load example data
data(ASEset)
a <- ASEset

minCountFilt(a)

Description

filter on minFreqFilt snps

Usage

minFreqFilt(x, ...)

## S4 method for signature 'ASEset'
minFreqFilt(
  x,
  strand = "*",
  threshold.frequency = 0.1,
  replace.with = "zero",
  return.class = "ASEset",
  sum = "all"
)

Arguments

Details

Description info here

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load example data
data(ASEset)
a <- ASEset

minFreqFilt(a)

Description

filter on multiallelic snps

Usage

multiAllelicFilt(x, ...)

## S4 method for signature 'ASEset'
multiAllelicFilt(
  x,
  strand = "*",
  threshold.count.sample = 10,
  threshold.frequency = 0.1,
  filterOver = "eachSample"
)

Arguments

Details

based on the allele counts for all four variants A, T, G and C and returns true if there is counts enough suggesting a third or more alleles. The sensitivity can be specified using 'threshold.count.sample' and 'threshold.frequency'.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load example data
data(ASEset)
a <- ASEset

multiAllelicFilt(a)

Description

Convert the phase from the internally stored phase, ref and alt information

Usage

phase2genotype(x, ...)

## S4 method for signature 'array'
phase2genotype(x, ref, alt, return.class = "matrix", ...)

Arguments

Details

To not introduce redundant information in the ASEset object, the genotype matrix is accessed from the phase matrix, which together with ref and alt allele information contains the same information(not taken into account three-allelic or more SNPs).

The genotype matrix retrieved from an ASEset object can differ from the genotype matrix stored in the object if reference and alternative alleles were not used or has changed since the phase genotype matrix was stored. Basically, it is preferable to provide reference and alternative information when storing the genotype matrix.

If possible, it is better to not use a genotype matrix, but instead relying completely on storing a phase matrix(or array) together with reference and alternative allele information.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load example data
data(ASEset)
data(genomatrix)
p <- genotype2phase(genomatrix, ref(ASEset), return.class="array")
ref <- ref(ASEset)
alt <- inferAltAllele(ASEset)

gt <- phase2genotype(p, ref, alt, return.class="matrix")

Description

used to convert the phase from the visually friendly matrix to array.

Usage

phaseArray2phaseMatrix(x, ...)

## S4 method for signature 'array'
phaseArray2phaseMatrix(x, ...)

Arguments

Details

A more effectice way of store the phase data in the ASEset object

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load data
data(ASEset)
a <- ASEset

#example phase matrix
p1 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p2 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p <- matrix(paste(p1,sample(c("|","|","/"), size=nrow(a)*ncol(a), replace=TRUE), p2, sep=""),
	nrow=nrow(a), ncol(a))

ar <- phaseMatrix2Array(p)

#Convert back
mat <- phaseArray2phaseMatrix(ar)

Description

used to convert the phase from the visually friendly matrix to array.

Usage

phaseMatrix2Array(x, ...)

## S4 method for signature 'matrix'
phaseMatrix2Array(x, dimnames = NULL, ...)

Arguments

Details

A more effectice way of store the phase data in the ASEset object

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load data
data(ASEset)
a <- ASEset

#example phase matrix
p1 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p2 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p <- matrix(paste(p1,sample(c("|","|","/"), size=nrow(a)*ncol(a), replace=TRUE), p2, sep=""),
	nrow=nrow(a), ncol(a))

ar <- phaseMatrix2Array(p)

Description

Create a vector of random reference alleles

Usage

randomRef(x, ...)

## S4 method for signature 'ASEset'
randomRef(x, source = "alleleCounts", ...)

Arguments

Details

Randomly shuffles which of the two alleles for each genotype that is indicated as reference allele, based on either allele count information or previous ref and alt alleles.

When the source is 'alleleCounts', the two most expressed alleles are taken as reference and alternative allele.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load example data
data(ASEset.sim)
a <- ASEset.sim

ref(a) <- randomRef(a, source = 'alleleCounts')

Description

This data set corresponds to the BAM-file data import illustrated in the vignette. The data set consists of a chromosome 17 region from 20 RNA-seq experiments of HapMap samples.

Author(s)

Jesper R. Gadin, Lasse Folkersen

References

Montgomery SB et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature. 2010 Apr 1;464(7289):773-7.

See Also

• The GRvariants which is another example object

Examples

##load eample data (Not Run)  
#data(reads)

Description

Extract the allele based on SNP location from the reference fasta file

Usage

refAllele(x, fasta)

Arguments

Details

The alleles will be placed in the rowRanges() meta column 'ref'

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

#load example data
data(ASEset.sim)

fasta <- system.file('extdata/hg19.chr17.subset.fa', package='AllelicImbalance')
a <- refAllele(ASEset.sim,fasta=fasta)

Description

Gives a summary of AI-consistency for a transcript

Usage

regionSummary(x, ...)

## S4 method for signature 'ASEset'
regionSummary(x, region, strand = "*", return.class = "RegionSummary", ...)

Arguments

Details

From a given set of e.g. transcripts exon ranges the function will return a summary for the sum of all exons. Phase information, reference and alternative allele is required.

A limitation comes to the strand-specificness. At the moment it is not possible to call over more than one strand type using the strands in region. This will be improved before going to release.

to calculate the direction and binomial p-values of AI the mapbias stored in the ASEset is used. see '?mapBias'.

Author(s)

Jesper R. Gadin, Lasse Folkersen

Examples

data(ASEset)
a <- ASEset
# Add phase
set.seed(1)
p1 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p2 <- matrix(sample(c(1,0),replace=TRUE, size=nrow(a)*ncol(a)),nrow=nrow(a), ncol(a))
p <- matrix(paste(p1,sample(c("|","|","/"), size=nrow(a)*ncol(a), replace=TRUE), p2, sep=""),
	nrow=nrow(a), ncol(a))

phase(a) <- p

#add alternative allele information
mcols(a)[["alt"]] <- inferAltAllele(a)

# in this example each and all snps in the ASEset defines the region
region <- granges(a)
t <- regionSummary(a, region)

# in this example two overlapping subsets of snps in the ASEset defines the region
region <- split(granges(a)[c(1,2,2,3)],c(1,1,2,2))
t <- regionSummary(a, region)