## ----echo=F, message=F, warning=F--------------------------------------------- library(MDTS); library(BSgenome.Hsapiens.UCSC.hg19) setwd(system.file("extdata", package="MDTS")) load('pD.RData') pD ## ----echo=FALSE, message=FALSE------------------------------------------------ library(MDTS) ## ----eval=F, warning=F-------------------------------------------------------- # library(MDTS); library(BSgenome.Hsapiens.UCSC.hg19) # # Using the raw data from MDTSData # devtools::install_github("jmf47/MDTSData") # setwd(system.file("data", package="MDTSData")) # # # Importing the pedigree file that includes information on where to locate the # # raw bam files # pD <- getMetaData("pD.ped") # # # Information on the GC content and mappability to estimate GC and mappability # # for the MDTS bins # genome <- BSgenome.Hsapiens.UCSC.hg19; map_file = "chr1.map.bw" # # # This command now subsets 5 samples to determine MDTS bins # # pD is the metaData matrix from getMetaData() # # n is the number of samples to examine to calculate the bins # # readLength is the sequencing read length # # minimumCoverage is the minimum read depth for a location to be included # # in a proto region # # medianCoverage is the median number of reads across the n samples in a bin # bins <- calcBins(metaData=pD, n=5, readLength=100, minimumCoverage=5, # medianCoverage=150, genome=genome, mappabilityFile=map_file) ## ----eval=F------------------------------------------------------------------- # # pD is the phenotype matrix # # bins is the previously calculated MDTS bins # # rl is the sequencing read length # counts = calcCounts(pD, bins, rl=100) ## ----message=FALSE, warning=F------------------------------------------------- load(system.file("extdata", 'bins.RData', package = "MDTS")) load(system.file("extdata", 'counts.RData', package = "MDTS")) load(system.file("extdata", 'pD.RData', package = "MDTS")) ## ----------------------------------------------------------------------------- bins ## ----------------------------------------------------------------------------- head(counts) ## ----warning=F---------------------------------------------------------------- # counts is the raw read depth of [MDTS bins x samples] # bins is the previously calculated MDTS bins mCounts <- normalizeCounts(counts, bins) ## ----warning=F---------------------------------------------------------------- # mCounts is the normalized read depth of [MDTS bins x samples] # bins is the previously calculated MDTS bins # pD is the phenotype matrix md <- calcMD(mCounts, pD) ## ----warning=FALSE, message=FALSE, warning=F---------------------------------- # md is the Minimum Distance of [MDTS bins x trio] # bins is the previously calculated MDTS bins # mCounts is the normalized read depth of [MDTS bins x samples] cbs <- segmentMD(md=md, bins=bins) denovo <- denovoDeletions(cbs, mCounts, bins) ## ----------------------------------------------------------------------------- denovo ## ----------------------------------------------------------------------------- sessionInfo()