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Last data update: 2014.03.03

R: Regional segmentation cost matrix

regionSegCost

R Documentation

Regional segmentation cost matrix

Description

To calculate regional cost matrix for the initial stage and second merging stage of the segmentation model.

Usage

regionSegCost(x, maxk = NULL, segs = NULL, family = NULL, alpha = NULL, useSum = TRUE, useMC = FALSE, comVar = TRUE)

Arguments

`x`	The input data matrix or vector
`maxk`	Maximum number of index to search forward
`segs`	Starting indices (excluding 1) for the candidate segments, for the second stage model, `maxk` will be overridden with `length(segs)+1`.
`family`	which exponential family the data belongs to, possible values are 'norm', 'pois' and 'nbinom'
`alpha`	alpha matrix for negative binomial cost calculation, estimated from `regionSegAlphaNB`
`useSum`	TRUE if using grand sum across sample / x columns, like in the `tilingArray` solution
`useMC`	TRUE if `mclapply` should be used to speed up
`comVar`	TRUE if assuming common variance across samples (x columns)

Details

Preparing the cost matrix for the follow-up segmentation. Using residual sum of squares for 'norm' data, and negative log-likelihood for 'pois' and 'nbinom' data. Extension of the costMatrix function in tilingArray.

Value

Matrix with maxk rows and nrow(x) columns, or a length(segs)+1 square matrix for the second stage model.

References

Piegorsch, W. W. (1990). Maximum likelihood estimation for the negative binomial dispersion parameter. Biometrics, 863-867.

Picard,F. et al. (2005) A statistical approach for array CGH data analysis. BMC Bioinformatics, 6, 27.

Huber,W. et al. (2006) Transcript mapping with high density oligonucleotide tiling arrays. Bioinformatics, 22, 1963-1970. .

Robinson MD and Smyth GK (2008). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332

Examples

	x<-matrix(rnorm(120), ncol=3)
	Ca<-regionSegCost(x, maxk=20, family='norm')
	dim(Ca) # [1] 20 40
	Cb<-regionSegCost(x, segs=as.integer(c(3, 6, 12, 30)), family='norm')
	dim(Cb) # [1] 5 5

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Platform: x86_64-pc-linux-gnu (64-bit)

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> 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/regionSegCost.Rd_%03d_medium.png", width=480, height=480)
> ### Name: regionSegCost
> ### Title: Regional segmentation cost matrix
> ### Aliases: regionSegCost
> 
> ### ** Examples
> 
> 	x<-matrix(rnorm(120), ncol=3)
> 	Ca<-regionSegCost(x, maxk=20, family='norm')
> 	dim(Ca) # [1] 20 40
[1] 20 40
> 	Cb<-regionSegCost(x, segs=as.integer(c(3, 6, 12, 30)), family='norm')
> 	dim(Cb) # [1] 5 5
[1] 5 5
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>