Auto detection of a fitted pcKeepComp param for filterFFT function

Description

This function tries to obtain the minimum number of components needed in a FFT filter to achieve or get as close as possible to a given correlation value. Usually you don't need to call directly this function, is used in filterFFT by default.

Usage

pcKeepCompDetect(data, pc.min=0.01, pc.max=0.1, max.iter=20, verbose=FALSE, 
			cor.target=0.98, cor.tol=1e-3, smpl.num=25, smpl.min.size=2^10, smpl.max.size=2^14)

Arguments

Details

This function predicts a suitable pcKeepComp value for filterFFT function. This is the recommended amount of components (in percentage) to keep in the filterFFT function to obtain a correlation of (or near of) cor.target.

The search starts from two given values pc.min, pc.max and uses linial interpolation to quickly reach a value that gives a corelation between the filtered and the original near cor.target within the specified tolerance cor.tol.

To allow a quick detection without an exhaustive search, this function uses a subset of the data by randomly sampling those regions with meaningful coverage values (i,e, different from 0 or NA) larger than smpl.min.size. If it's not possible to obtain smpl.max.size from this region (this could be due to flanking 0's, for example) at least smpl.min.size will be used to check correlation. Mean correlation between all sampled regions is used to test the performance of the pcKeepComp parameter.

If the number of meaningful bases in data is less than smpl.min.size * (smpl.num/2) all the data vector will be used instead of using sampling.

Value

Fitted pcKeepComp value

Author(s)

Oscar Flores oflores@mmb.pcb.ub.es, David Rosell david.rosell@irbbarcelona.org

Examples

	#Load dataset
	data(nucleosome_htseq)
	data = as.vector(coverage.rpm(nucleosome_htseq)[[1]])

	#Get recommended pcKeepComp value
	pckeepcomp = pcKeepCompDetect(data, cor.target=0.99)
	print(pckeepcomp)

	#call filterFFT
	f1 = filterFFT(data, pcKeepComp=pckeepcomp)

	#Also this can be called directly
	f2 = filterFFT(data, pcKeepComp="auto", cor.target=0.99)

	#Plot
	plot(data[1:2000], col="black", type="l", lwd=2)
	lines(f1[1:2000], col="red", lwd=2)
	lines(f2[1:2000], col="blue", lwd=2, lty=2)
	legend("bottom", c("original", "two calls", "one call"), col=c("black", "red", "blue"), lty=c(1,1,2), horiz=TRUE, bty="n")

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)

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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(nucleR)
Loading required package: ShortRead
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: BiocParallel
Loading required package: Biostrings
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: IRanges
Loading required package: XVector
Loading required package: Rsamtools
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
Loading required package: GenomicAlignments
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/nucleR/pcKeepCompDetect.Rd_%03d_medium.png", width=480, height=480)
> ### Name: pcKeepCompDetect
> ### Title: Auto detection of a fitted 'pcKeepComp' param for filterFFT
> ###   function
> ### Aliases: pcKeepCompDetect
> ### Keywords: attribute
> 
> ### ** Examples
> 
> 
> 	#Load dataset
> 	data(nucleosome_htseq)
> 	data = as.vector(coverage.rpm(nucleosome_htseq)[[1]])
> 
> 	#Get recommended pcKeepComp value
> 	pckeepcomp = pcKeepCompDetect(data, cor.target=0.99)
> 	print(pckeepcomp)
[1] 0.01
> 
> 	#call filterFFT
> 	f1 = filterFFT(data, pcKeepComp=pckeepcomp)
> 
> 	#Also this can be called directly
> 	f2 = filterFFT(data, pcKeepComp="auto", cor.target=0.99)
> 
> 	#Plot
> 	plot(data[1:2000], col="black", type="l", lwd=2)
> 	lines(f1[1:2000], col="red", lwd=2)
> 	lines(f2[1:2000], col="blue", lwd=2, lty=2)
> 	legend("bottom", c("original", "two calls", "one call"), col=c("black", "red", "blue"), lty=c(1,1,2), horiz=TRUE, bty="n")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>