regionSegCost: Regional segmentation cost matrix
In biomvRCNS: Copy Number study and Segmentation for multivariate biological data

Description Usage Arguments Details Value References See Also Examples

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

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

`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)

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.

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

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

regionSegAlphaNB

	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