Perform ACME calculation

Description

This function performs the moving window chi-square calculation. It is written in C, so is quite fast.

Usage

do.aGFF.calc(x, window, thresh)

Arguments

Details

A window size on the order of 2-3 times the average size of fragments from sonication, digestion, etc. and containing at least 8-10 probes is the recommended size. Larger size windows are probably more sensitive, but obviously reduce the accuracy with which boundaries of signal can be called.

A threshold of between 0.9 and 0.99 seems empirically to be adequate. If one plots the histogram of data values and there is an obvious better choice (such as a bimodal distribution, with one peak representing enrichment), a more data-driven approach may yield better results.

Value

An object of class aGFFCalc

Author(s)

Sean Davis <sdavis2@mail.nih.gov>

Examples

data(example.agff)
example.agffcalc <- do.aGFF.calc(example.agff,window=1000,thresh=0.9)
example.agffcalc

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.
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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(ACME)
Loading required package: Biobase
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

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/ACME/do.aGFF.calc.Rd_%03d_medium.png", width=480, height=480)
> ### Name: do.aGFF.calc
> ### Title: Perform ACME calculation
> ### Aliases: do.aGFF.calc
> ### Keywords: htest
> 
> ### ** Examples
> 
> data(example.agff)
> example.agffcalc <- do.aGFF.calc(example.agff,window=1000,thresh=0.9)
Working on sample 1
Working on chromosome: 
chr1  chr10  chr11  chr12  chr13  chr14  chr15  chr16  chr17  chr18  chr19  chr2  chr20  chr21  chr22  chr3  chr4  chr5  chr6  chr7  chr8  chr9  chrX  Working on sample 2
Working on chromosome: 
chr1  chr10  chr11  chr12  chr13  chr14  chr15  chr16  chr17  chr18  chr19  chr2  chr20  chr21  chr22  chr3  chr4  chr5  chr6  chr7  chr8  chr9  chrX  > example.agffcalc
ACMECalcSet (storageMode: lockedEnvironment)
assayData: 190181 features, 2 samples 
  element names: exprs, vals 
protocolData: none
phenoData
  sampleNames: testsamp1 testsamp2
  varLabels: fullfnames
  varMetadata: labelDescription
featureData
  featureNames: 74065 74066 ... 103913 (190181 total)
  fvarLabels: chromosome source ... comment (8 total)
  fvarMetadata: labelDescription
experimentData: use 'experimentData(object)'
Annotation:  
Threshold: 0.9 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>