Build the synthetic rates shaped on a dataset

Description

Build the synthetic rates shaped on a dataset

This method allow the creation of synthesis, degradation and processing rates for a certain number of genes. The rates are created according to the distributions of the real data-set which is given as an input of the method. Different proportions of constant varying rates can be set and a new vector of time points can be provided. This method has to be used before the makeSimDataset method.

Usage

makeSimModel(object, nGenes, newTpts = NULL, probs = c(constant = 0.5,
  sigmoid = 0.3, impulse = 0.2), na.rm = TRUE, seed = NULL)

## S4 method for signature 'INSPEcT'
makeSimModel(object, nGenes, newTpts = NULL,
  probs = c(constant = 0.5, sigmoid = 0.3, impulse = 0.2), na.rm = TRUE,
  seed = NULL)

Arguments

Details

The method makeSimModel generates an object of class INSPEcT_model that stores the parametric functions to genrate clean rates of a time-course. To any of the rates also a noise variance is associate but not used yet. In a typical workflow the output of makeSimModel is the input of the method makeSimDataset, that build the noisy rates and concentrations, given a specified number of replicates.

Value

An object of class INSPEcT_model with synthetic rates

See Also

makeSimDataset

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())
## generate a synthtic data-set of 10 genes based on the real data-set
simRates <- makeSimModel(mycerIds, 10)
simData <- makeSimDataset(simRates, tpts, 1)
## measure sensitivity/sensibility of synthesis, degradation and processing
## rates identification
data('simRates', package='INSPEcT')
data('simData3rep', package='INSPEcT')
rocCurve(simRates, simData3rep)
## measure classification with a different threshold for the chi-suared 
## test acceptance of models
rocCurve(simRates, simData3rep, cTsh=.2)
## generate a synthtic data-set of 10 genes based on the real data-set
## with more replicates and more time points
## Not run: 
newTpts <- c(0, 1/6, 1/3, 1/2, 1, 1.5, 2, 4, 8, 12, 16, 24)
simRates <- makeSimModel(mycerIds, 10, newTpts=newTpts)
simData <- makeSimDataset(simRates, newTpts, 3)

## End(Not run)

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/makeSimModel.Rd_%03d_medium.png", width=480, height=480)
> ### Name: makeSimModel
> ### Title: Build the synthetic rates shaped on a dataset
> ### Aliases: makeSimModel makeSimModel,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...
> ## generate a synthtic data-set of 10 genes based on the real data-set
> simRates <- makeSimModel(mycerIds, 10)
sampling means from rates distribution...
sampling fold changes from rates distribution...
generating rates time course...
evaluating noise for simulated alpha, total and pre...
> simData <- makeSimDataset(simRates, tpts, 1)
Number of genes with introns and exons:  10
Estimating degradation rates...
Estimating processing rates...
> ## measure sensitivity/sensibility of synthesis, degradation and processing
> ## rates identification
> data('simRates', package='INSPEcT')
> data('simData3rep', package='INSPEcT')
> rocCurve(simRates, simData3rep)
> ## measure classification with a different threshold for the chi-suared 
> ## test acceptance of models
> rocCurve(simRates, simData3rep, cTsh=.2)
> ## generate a synthtic data-set of 10 genes based on the real data-set
> ## with more replicates and more time points
> ## Not run: 
> ##D newTpts <- c(0, 1/6, 1/3, 1/2, 1, 1.5, 2, 4, 8, 12, 16, 24)
> ##D simRates <- makeSimModel(mycerIds, 10, newTpts=newTpts)
> ##D simData <- makeSimDataset(simRates, newTpts, 3)
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>