cnpexample | R Documentation |
A table of genomic positions for DNA copy-number changing events, collected from genomes of 1203 individuals using Representational Oligonucleotide Microarray Analysis (ROMA) platform.
data(cnpexample)
a data.frame
with 19188 rows and 4 columns:
copy.num
a character
vector
indicating
whether an event is a gain ("amp") or a loss ("del").
chrom
a numeric
vector
used as an integer
indicating which chromosome the event is in.
chrom.start
a numeric
vector
, used as an
integer, representing the event start positions.
chrom.end
a numeric
vector
, used as an integer,
of event start positions.
a data.frame
with 19188 rows and 4 columns:
copy.num
a character
vector
indicating
whether an event is a gain ("amp") or a loss ("del").
chrom
a numeric
vector
used as an integer
indicating which chromosome the event is in.
chrom.start
a numeric
vector
, used as an
integer, representing the event start positions.
chrom.end
a numeric
vector
, used as an integer,
of event start positions.
Strong association of de novo copy number mutations with autism. Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C, Walsh T, Yamrom B, Yoon S, Krasnitz A, Kendall J, Leotta A, Pai D, Zhang R, Lee YH, Hicks J, Bregman J, Sutcliffe JS, Jobanputra V, Chung W, Warburton D, King MC, Skuse D, Geschwind DH, Gilliam TC, Ye K, Wigler M. Science. 2007 Apr 20;316(5823):445-9.
CNpreprocessing
for pre-processing DNA copy number
(CN) data for detection of CN events.
makeCNPmask
for creating a mask given a set of
copy number events.
applyCNPmask
for applying a mask to a table of
copy number events.
## Load datasets data(segexample) data(ratexample) data(normsegs) data(cnpexample) ## Create a table with segment information for profile WZ2 with help ## of normal samples segtable <- CNpreprocessing(segall = segexample[segexample[,"ID"] == "WZ2",], ratall = ratexample, idCol = "ID", startCol = "start", endCol = "end", chromCol = "chrom", bpStartCol = "chrom.pos.start", bpEndCol = "chrom.pos.end", blsize = 50, minJoin = 0.25, cWeight = 0.4, bsTimes = 50, chromRange = 1:22, nJobs = 1, modelNames = "E", normalLength = normsegs[,1], normalMedian = normsegs[,2]) ## Add an eventIndex column to segtable that identifies the ## amplication (marked as 1) and deletion (marked as -1) events eventIndex <- rep(0, nrow(segtable)) eventIndex[segtable[,"marginalprob"] < 1e-4 & segtable[,"negtail"] > 0.999 & segtable[,"mediandev"] < 0] <- -1 eventIndex[segtable[,"marginalprob"] < 1e-4 & segtable[,"negtail"] > 0.999 & segtable[,"mediandev"] > 0] <- 1 segtable <- cbind(segtable, eventIndex) ## Create a mask table using amplification and deletion regions as input namps17 <- cnpexample[cnpexample[,"copy.num"] == "amp",] aCNPmask <- makeCNPmask(imat=namps17, chromCol=2, startCol=3, endCol=4, nProf=1203, uThresh=0.02, dThresh=0.008) ndels17 <- cnpexample[cnpexample[,"copy.num"] == "del",] dCNPmask <- makeCNPmask(imat=ndels17, chromCol=2, startCol=3, endCol=4, nProf=1203, uThresh=0.02, dThresh=0.008) maskTable <- rbind(cbind(aCNPmask, cnpindex=1), cbind(dCNPmask, cnpindex=-1)) ## Apply a mask to a table of copy number events maskedCNP <- applyCNPmask(segTable=segtable, chrom="chrom", startPos="chrom.pos.start", endPos="chrom.pos.end", startProbe="start", endProbe="end", eventIndex="eventIndex", maskTable=maskTable, maskChrom="chrom", maskStart="start", maskEnd="end", maskIndex="cnpindex", minCover=0.005, indexVals=c(-1, 1)) ## Show some results tail(maskedCNP)
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