A nice plot to see scaling factors used for RNA-seq and 4sU-seq libraries

Description

A nice plot to see scaling factors used for RNA-seq and 4sU-seq libraries

This method generates a plot that immediately shows the scaling factors used to scale RNA- and 4sU-seq libraries and the possible relations between them. The ratio between the RNA- and the 4sU-seq scaling can be in fact considered as a yield of the synthesis within the cells.

Usage

sfPlot(object)

## S4 method for signature 'INSPEcT'
sfPlot(object)

Arguments

Value

None

Examples

data('rpkms', package='INSPEcT')
tpts <- c(0, 1/6, 1/3, 1/2, 1, 2, 4, 8, 16)
tL <- 1/6
mycerIds <- newINSPEcT(tpts, tL, rpkms$foursu_exons, rpkms$total_exons, 
	rpkms$foursu_introns, rpkms$total_introns, BPPARAM=SerialParam())
sfPlot(mycerIds)

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.
You are welcome to redistribute it under certain conditions.
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(INSPEcT)
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")'.

Loading required package: BiocParallel
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/INSPEcT/sfPlot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: sfPlot
> ### Title: A nice plot to see scaling factors used for RNA-seq and 4sU-seq
> ###   libraries
> ### Aliases: sfPlot sfPlot,INSPEcT-method
> 
> ### ** Examples
> 
> data('rpkms', package='INSPEcT')
> tpts <- c(0, 1/6, 1/3, 1/2, 1, 2, 4, 8, 16)
> tL <- 1/6
> mycerIds <- newINSPEcT(tpts, tL, rpkms$foursu_exons, rpkms$total_exons, 
+ 	rpkms$foursu_introns, rpkms$total_introns, BPPARAM=SerialParam())
For some genes only synthesis and degradation will be evaluated because they have zero valued features in more than 2/3 of the time points in their intronic features: 333193; 94067; 230866; 68961; 100042464; 667250; 59288; 100038734; 100113398; 100040591
Some genes have only exons RPKMs, on them only synthesis and degradation will be evaluated.
Number of genes with introns and exons:  490
Calculating scaling factor between total and 4su libraries...
Estimating degradation rates...
Estimating processing rates...
Number of genes with only exons:  10
Estimating degradation rates...
> sfPlot(mycerIds)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>