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Last data update: 2014.03.03

R: Draw a near-cis coverage plot for 4C-seq data

visualizeViewpoint

R Documentation

Draw a near-cis coverage plot for 4C-seq data

Description

This method creates a plot of near-cis 4C-seq fragment data around the experiment's viewpoint. Fragment-based raw data is visualized as grey dots, interpolated data (running median / running mean) as coloured dots. Trend line and quantiles are loess-smoothed; the trend line is shown as colored line whereas the quantiles are depicted as light-grey bands. A corresponding quantile legend is added in an extra plot.

Usage

visualizeViewpoint(expData, poi = data.frame(chr = character(), start = character(), end = character(), name = character(), colour = character()), plotFileName = "", windowLength = 5, interpolationType = "median", picDim = c(9, 5), maxY = -1, minQuantile = 0.2, maxQuantile = 0.8, mainColour = "blue", plotTitle = "4C-seq plot", loessSpan = 0.1, xAxisIntervalLength = 50000, yAxisIntervalLength = 500, useFragEnds = TRUE)

Arguments

`expData`	Experiment data of class `Data4Cseq` with information on the 4C-seq experiment, including normalized near-cis fragment data for visualization
`poi`	Points of interest that will be marked in the plot
`plotFileName`	Name for the 4C-seq plot file
`windowLength`	Length of the window for running median / running mean that is used to smooth the trend line
`interpolationType`	Type of interpolation, either running median or running mean
`picDim`	Dimensions of the plot
`maxY`	Maximum y-value to plot. If no maximum is given, the maximum running median / mean value is used
`minQuantile`	Minimum quantile to draw
`maxQuantile`	Maximum quantile to draw
`mainColour`	Main colour of the plot
`plotTitle`	Title of the 4C-seq plot, depicted above the main plot
`loessSpan`	Span value for the loess curve; smaller values mean a tighter fit to the data points, but a value that is too small may produce errors
`xAxisIntervalLength`	Length of the x axis intervals in the plot
`yAxisIntervalLength`	Length of the y axis intervals in the plot
`useFragEnds`	Indicates whether fragment end data is used directly or interpolated on fragment level

Value

A near-cis coverage plot and a corresponding quantile legend

Note

PDF export and output as TIFF format are supported. The export format is chosen depending on the plot file name's ending. If no plot file name is provided, the result is plotted on screen.

Author(s)

Carolin Walter

Examples

  data(liverData)
  file <- system.file("extdata", "fetalLiverVP.bed", package="Basic4Cseq")
    visualizeViewpoint(liverData, readPointsOfInterestFile(file), plotFileName = "", mainColour = "red", plotTitle = "Fetal Liver Near-Cis Plot", loessSpan = 0.1, maxY = 6000, xAxisIntervalLength = 50000, yAxisIntervalLength = 1000)

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 'contributors()' for more information and
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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(Basic4Cseq)
Loading required package: Biostrings
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: XVector
Loading required package: GenomicAlignments
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
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: Rsamtools
Loading required package: caTools

Attaching package: 'caTools'

The following object is masked from 'package:IRanges':

    runmean

The following object is masked from 'package:S4Vectors':

    runmean

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/Basic4Cseq/visualizeViewpoint.Rd_%03d_medium.png", width=480, height=480)
> ### Name: visualizeViewpoint
> ### Title: Draw a near-cis coverage plot for 4C-seq data
> ### Aliases: visualizeViewpoint visualizeViewpoint,data.frame-method
> ###   visualizeViewpoint,Data4Cseq-method
> ### Keywords: visualizeViewpoint
> 
> ### ** Examples
> 
>   data(liverData)
>   file <- system.file("extdata", "fetalLiverVP.bed", package="Basic4Cseq")
>     visualizeViewpoint(liverData, readPointsOfInterestFile(file), plotFileName = "", mainColour = "red", plotTitle = "Fetal Liver Near-Cis Plot", loessSpan = 0.1, maxY = 6000, xAxisIntervalLength = 50000, yAxisIntervalLength = 1000)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>