biomvRmgmr {biomvRCNS} | R Documentation |
This is a wrapper function for batch processing multiple sequences and samples using max-gap-min-run algorithm for 2 states segmentation
biomvRmgmr(x, xPos=NULL, xRange=NULL, usePos='start', cutoff=NULL, q=0.9, high=TRUE, minrun=5, maxgap=2, splitLen=Inf, poolGrp=FALSE, grp=NULL, cluster.m=NULL, avg.m='median', trim=0,na.rm=TRUE)
x |
input data matrix, or a |
xPos |
a vector of positions for each |
xRange |
a |
usePos |
character value to indicate whether the 'start', 'end' or 'mid' point position should be used |
cutoff |
threshold level above which is considered extreme |
q |
relative quantile threshold level instead of absolute value for the cutoff |
high |
TRUE if the |
minrun |
minimum run length for the resulting segments |
maxgap |
maximum genomic distance below which two adjacent qualified tiles can be joined |
splitLen |
numeric value, maximum length of segments, split if too long |
poolGrp |
TRUE if samples within the same group should be pooled using median for each feature |
grp |
vector of group assignment for each sample, with a length the same as columns in the data matrix, samples within each group would be processed simultaneously if a multivariate emission distribution is available |
cluster.m |
clustering method for prior grouping, possible values are 'ward','single','complete','average','mcquitty','median','centroid' |
avg.m |
method to calculate average value for each segment, 'median' or 'mean' possibly trimmed |
trim |
the fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed. Values of trim outside that range are taken as the nearest endpoint. |
na.rm |
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This is the batch function to apply maxGapminRun
multiple sequence.
A biomvRCNS-class
object:
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Object of class |
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Object of class |
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Object of class |
Yang Du
data(coriell) xgr<-GRanges(seqnames=paste('chr', coriell[,2], sep=''), IRanges(start=coriell[,3], width=1, names=coriell[,1])) values(xgr)<-DataFrame(coriell[,4:5], row.names=NULL) xgr<-xgr[order(xgr)] resseg<-biomvRmgmr(x=xgr, minrun=3000, maxgap=1500, q=0.9, grp=c(1,2))