Last data update: 2014.03.03

 R: Batch process multiple sequences and samples using...
biomvRmgmrR Documentation

Batch process multiple sequences and samples using max-gap-min-run algorithm for 2 states segmentation

Description

This is a wrapper function for batch processing multiple sequences and samples using max-gap-min-run algorithm for 2 states segmentation

Usage

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)

Arguments

x

input data matrix, or a GRanges object with input stored in the meta DataFrame, assume ordered.

xPos

a vector of positions for each x row

xRange

a IRanges/GRanges obejct, same length as x rows

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 cutoff or q here is the lower bound and values greater than the threshold are considered

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

TRUE if NA value should be ignored

Details

This is the batch function to apply maxGapminRun multiple sequence.

Value

A biomvRCNS-class object:

x:

Object of class "GRanges", with range information either from real positional data or just indices, with input data matrix stored in the meta columns.

res:

Object of class "GRanges" , each range represent one continuous segment identified, with sample name slot 'SAMPLE' and segment mean slot 'MEAN' stored in the meta columns

param:

Object of class "list", list of all parameters used in the model run.

Author(s)

Yang Du

See Also

biomvRhsmm maxGapminRun

Examples

	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))

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(biomvRCNS)
Loading required package: IRanges
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: Gviz
Loading required package: grid
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/biomvRCNS/biomvRmgmr.Rd_%03d_medium.png", width=480, height=480)
> ### Name: biomvRmgmr
> ### Title: Batch process multiple sequences and samples using
> ###   max-gap-min-run algorithm for 2 states segmentation
> ### Aliases: biomvRmgmr
> 
> ### ** Examples
> 
> 	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))
Processing sequence chr1

Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
Building segmentation model for group 2 ...
Building segmentation model for group 2 complete.
Processing sequence chr1 complete.
Processing sequence chr2

Building segmentation model for group 1 ...
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Building segmentation model for group 1 ...
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Building segmentation model for group 1 ...
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Processing sequence chr5

Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
Building segmentation model for group 2 ...
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Processing sequence chr6

Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
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Building segmentation model for group 1 ...
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Building segmentation model for group 1 ...
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Building segmentation model for group 1 ...
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Building segmentation model for group 1 ...
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Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
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Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
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Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
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Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
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Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
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Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
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Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
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Building segmentation model for group 2 complete.
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Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
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Building segmentation model for group 2 complete.
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Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
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Building segmentation model for group 2 complete.
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Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
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Building segmentation model for group 1 ...
Building segmentation model for group 1 complete.
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Building segmentation model for group 1 ...
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> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and IMSBIO