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Last data update: 2014.03.03

R: plot MMD distances

plotHistDists

R Documentation

plot MMD distances

Description

the generated plots are similar to MA plots, except that the computed distances (MMD) are shown on the y-axes instead of log fold change.

Usage

plotHistDists(DBA, method = "MMD", group1, group2, 
              field4X = "NormTotalCounts", bUsePval = FALSE,
              pnames = "combined", thresh = 0.05, 
              save2file = FALSE, fn.pics,  ftype = pdf,
              xlim = NULL, ylim = NULL)

Arguments

`DBA`	DBA object, after running compHistDists.
`method`	specify method used to determine distances between histograms (could be MMD, GMD or Pearson).
`group1`	sample ids of control group
`group2`	sample ids of treatment group
`field4X`	name of list element in DBA$MD that is used for pooling of peaks. (e.g. NormTotalCounts or RawTotalCounts)
`bUsePval`	logical indicating whether to use FDR (FALSE) or p-value (TRUE) for thresholding.
`pnames`	name of comparison used to generated p-values, eg. name1 vs name2
`thresh`	threshold to show significant peaks. (e.g. < 0.05)
`save2file`	if TRUE plot is saved to pdf file
`fn.pics`	file name, where the plot should be saved to.
`ftype`	file format for saving the plot (pdf, postcsript, png)
`xlim`	the x limits (x1, x2) of the plot. The default value, 'NULL', indicates that the range of the finite values to be plotted should be used.
`ylim`	the y limits of the plot.

Author(s)

Gabriele Schweikert

Examples


# load DBA objects with peak profiles and pairwise distances
data(Cfp1Dists)

# determine empirical p-values:
group1 <- c("WT.AB2", "Resc.AB2")
group2 <- c("Null.AB2")
Cfp1Pvals <- detPeakPvals(Cfp1Dists, group1=group1, group2=group2,
             name1='Wt/Resc', name2='Null')

# plot distances and peaks which are significantly different:

plotHistDists(Cfp1Pvals, group1=group1, group2=group2)

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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> library(MMDiff)
Loading required package: GenomicRanges
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: IRanges
Loading required package: GenomeInfoDb
Loading required package: DiffBind
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")'.


Loading required package: GMD
Loading required package: Rsamtools
Loading required package: Biostrings
Loading required package: XVector
Warning message:
Package 'MMDiff' is deprecated and will be removed from Bioconductor
  version 3.4 
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MMDiff/plotHistDists.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotHistDists
> ### Title: plot MMD distances
> ### Aliases: plotHistDists
> 
> ### ** Examples
> 
> 
> # load DBA objects with peak profiles and pairwise distances
> data(Cfp1Dists)
> 
> # determine empirical p-values:
> group1 <- c("WT.AB2", "Resc.AB2")
> group2 <- c("Null.AB2")
> Cfp1Pvals <- detPeakPvals(Cfp1Dists, group1=group1, group2=group2,
+              name1='Wt/Resc', name2='Null')
using NormTotalCounts for pooling
Using unfiltered Peaks
nPeaks = 1000 (of 1000)
nPeaks = 1000 (of 1000)
dN:
  0%   5%  10%  15%  20%  25%  30%  35%  40%  45%  50%  55%  60%  65%  70%  75% 
  32   78   96  110  124  139  155  169  191  210  236  264  297  335  385  453 
 80%  85%  90%  95% 100% 
 520  639  828 1176 3699 
Computing p-values 

> 
> # plot distances and peaks which are significantly different:
> 
> plotHistDists(Cfp1Pvals, group1=group1, group2=group2)
using FDR from combined
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>