Package 'MDTS'

The MDTS package for Detection of Denovo Deletions from Targeted Sequencing Data Using Minimum-Distance

Description

The MDTS package for Detection of Denovo Deletions from Targeted Sequencing Data Using Minimum-Distance

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Sample driven calculation of bins for counting

Description

This function will randomly select a sample of bam files to calculate dynamic MDTS bins for subsequent read-depth analysis.

Usage

calcBins(metaData, n, readLength, medianCoverage, minimumCoverage, genome,
  mappabilityFile, seed = 1337)

Arguments

metaData	A table in the format of the output of getMetaData().
n	The number of subsamples to use.
readLength	The read length of the experiment.
medianCoverage	The median number of reads across sub-samples to reach before creating a new bin.
minimumCoverage	The miminum number of coverage across all sub-samples required to create the proto-region.
genome	The BSGenome object that assists in calculations of the GC content of the bins.
mappabilityFile	A path to the bigwig file of 100mer mappability of the corresponding genome.
seed	Sets the seed so results are reproducible. Defaults to 1337.

Value

Returns a GRanges object depicting the dynamic bins that MDTS calculates.

Examples

load(system.file("extdata", 'bins.RData', package = "MDTS"))
bins

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Creating the raw count matrix

Description

This function will return a matrix of read counts where ecah column is a sample, and each row is a bin.

Usage

calcCounts(metaData, bins, rl, mc.cores = 1)

Arguments

metaData	A table in the format of the output of getMetaData().
bins	The set of bins determined by calcBins().
rl	The read length of the experiment.
mc.cores	The number of cores to use for multi-threaded analysis. Defaults to 1.

Value

A data.frame that contains the counts for each sample in the metaData input that fall into each segment of bins.

Examples

## Not run: 
pD <- getMetaData(
'https://raw.githubusercontent.com/JMF47/MDTSData/master/data/pD.ped')
genome = BSgenome.Hsapiens.UCSC.hg19
map_file <- 
"https://raw.githubusercontent.com/JMF47/MDTSData/master/data/chr1.map.bw"
bins = calcBins(pD, n=5, rl=100, med=150, min=5, genome, map_file)

## End(Not run)
load(system.file("extdata", 'bins.RData', package = "MDTS"))
load(system.file("extdata", 'counts.RData', package = "MDTS"))
counts

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Calculating the Minimum Distance matrix

Description

This function will return a matrix of minimum distances where ecah column is a family, and each row is a bin.

Usage

calcMD(mCounts, metaData)

Arguments

mCounts	A matrix of normalized coverage output by normalizedCounts().
metaData	A table in the format of the output of metaData().

Value

A data.frame of minimum distances. Each column is a trio, while each row is an entry in bins

Examples

load(system.file("extdata", 'bins.RData', package = "MDTS"))
load(system.file("extdata", 'counts.RData', package = "MDTS"))
load(system.file("extdata", 'pD.RData', package = "MDTS"))
mCounts <- normalizeCounts(counts, bins)
md <- calcMD(mCounts, pD)

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Denovo Deletion Calling

Description

This function will return a single GRanges object containing all denovo deletions that passed filtering from a Circular Binary Segmentation object with supplementary information.

Usage

denovoDeletions(cbs, mCounts, bins)

Arguments

cbs	The output from segmentMD().
mCounts	The normalized counts matrix output by normalizeCounts().
bins	The set of bins determined by calcBins().

Value

A GRanges object that reports all detected denovo deletions passing requite filters.

Examples

load(system.file("extdata", 'bins.RData', package = "MDTS"))
load(system.file("extdata", 'counts.RData', package = "MDTS"))
load(system.file("extdata", 'pD.RData', package = "MDTS"))
mCounts = normalizeCounts(counts, bins)
md = calcMD(mCounts, pD)
cbs = segmentMD(md, bins)
denovo = denovoDeletions(cbs, mCounts, bins)

---

Constructor for metadata

Description

This function allows constructor of phenotype information necessary for downstream analysis. See format of required fields. Function will also rearrange the rows such that trios are grouped together - with proband first, mother second, and father third.

Usage

getMetaData(path, id = "subj_id", familyId = "family_id",
  fatherId = "father_id", motherId = "mother_id", bamPath = "bam_path")

Arguments

path	The path pointing to the file that contains information on each subject in the dataset.
id	The column name that identifies each sample. Defaults to 'subj_id'.
familyId	The column name that identifies which family the sample belongs to. Defaults to 'family_id'.
fatherId	The column name that identifies the id of the father. Defaults to 'father_id'.
motherId	The column name that identifies the id of the mother. Defaults to 'mother_id'.
bamPath	The column name that identifies where to find the bam file for each subject. Defaults to 'bam_path'.

Value

Returns a data.frame of required sample information for running MDTS.

Examples

meta <- getMetaData(
'https://raw.githubusercontent.com/JMF47/MDTSData/master/data/pD.ped')

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Calculating the normalized M scores

Description

This function will return a matrix of normalized M scores where ecah column is a sample, and each row is a bin.

Usage

normalizeCounts(counts, bins, GC = TRUE, map = TRUE, mc.cores = 1)

Arguments

counts	A matrix of raw coverage output by calcCounts().
bins	The set of bins determined by calcBins().
GC	Whether to loess adjust for GC. Defaults to TRUE.
map	Whether to loess adjust for mappability. Defaults to TRUE. Defaults to 1.
mc.cores	The number of cores to use for multi-threaded analysis.

Value

A data.frame of normalized counts. Each column is a sample, and each row is a entry of bins.

Examples

load(system.file("extdata", 'bins.RData', package = "MDTS"))
load(system.file("extdata", 'counts.RData', package = "MDTS"))
load(system.file("extdata", 'pD.RData', package = "MDTS"))
mCounts <- normalizeCounts(counts, bins)

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Circular Binary Segmentation on Minimum Distances

Description

This function will return a GRanges object containing the copy number segments of all families in the input minimum distance matrix. It calls segment() from DNAcopy (alpha=0.001, undo.splits="sdundo", undo.SD=4).

Usage

segmentMD(md, bins, alpha = 0.001, undo.splits = "sdundo", undo.SD = 4,
  mc.cores = 1)

Arguments

md	The minimum distance matrix produced by calcMD.
bins	The set of bins determined by calcBins.
alpha	Controls the alpha option in calling DNAcopy::segment()
undo.splits	Controls the undo.splits option in DNAcopy::segment()
undo.SD	Controls the undo.SD option in calling DNAcopy::segment()
mc.cores	The number of cores to use for multi-threaded analysis. Defaults to 1.

Value

A data.frame containing the segmented regions based to be parsed by denovoDeletions() minimum distance.

Examples

load(system.file("extdata", 'bins.RData', package = "MDTS"))
load(system.file("extdata", 'counts.RData', package = "MDTS"))
load(system.file("extdata", 'pD.RData', package = "MDTS"))
mCounts <- normalizeCounts(counts, bins)
md <- calcMD(mCounts, pD)
cbs <- segmentMD(md, bins)

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Visualization for deletions

Description

This function plots the raw read information from the location of interest for a trio.

Usage

visualizeDeletion(deletion, bins, metaData, mCounts, md, save = FALSE)

Arguments

deletion	A GRanges object in the format of the output of denovoDeletions().
bins	The set of bins determined by calcBins().
metaData	A table in the format of the output of getMetaData().
mCounts	A matrix of normalized coverage output by normalizedCounts().
md	The minimum distance matrix output by calcMD()
save	If TRUE will save plot to current working directory instead of rendering.

Value

The file name if the plot was saved.

Examples

## Not run: 
load(system.file("extdata", 'bins.RData', package = "MDTS"))
load(system.file("extdata", 'counts.RData', package = "MDTS"))
load(system.file("extdata", 'pD.RData', package = "MDTS"))
mCounts <- normalizeCounts(counts, bins)
md <- calcMD(mCounts, pD)
cbs <- segmentMD(md, bins)
denovo <- denovoDeletions(cbs, mCounts, bins)
visualizeDeletion(denovo[1], bins, pD, mCounts, md)

## End(Not run)

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