makeSimDataSd: Create simulated counts using the Soneson-Delorenzi method

In pmoulos/metaseqR2: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms

Create simulated counts using the Soneson-Delorenzi method

Description

This function creates simulated RNA-Seq gene expression datasets using the method presented in (Soneson and Delorenzi, BMC Bioinformatics, 2013). For the time being, it creates only simulated datasets with two conditions.

Usage

    makeSimDataSd(N, param, samples = c(5, 5),
        ndeg = rep(round(0.1*N), 2), fcBasis = 1.5,
        libsizeRange = c(0.7, 1.4), libsizeMag = 1e+7,
        modelOrg = NULL, simLengthBias = FALSE)

Arguments

N	the number of genes to produce.
param	a named list with negative binomial parameter sets to sample from. The first member is the mean parameter to sample from (muHat) and the second the dispersion (phiHat). This list can be created with the estimateSimParams function.
samples	a vector with 2 integers, which are the number of samples for each condition (two conditions currently supported).
ndeg	a vector with 2 integers, which are the number of differentially expressed genes to be produced. The first element is the number of up-regulated genes while the second is the number of down-regulated genes.
fcBasis	the minimum fold-change for deregulation.
libsizeRange	a vector with 2 numbers (generally small, see the default), as they are multiplied with libsizeMag. These numbers control the library sized of the synthetic data to be produced.
libsizeMag	a (big) number to multiply the libsizeRange to produce library sizes.
modelOrg	the organism from which the real data are derived from. It must be one of the supported organisms (see the main metaseqr2 help page). It is used to sample real values for GC content.
simLengthBias	a boolean to instruct the simulator to create genes whose read counts is proportional to their length. This is achieved by sorting in increasing order the mean parameter of the negative binomial distribution (and the dispersion according to the mean) which will cause an increasing gene count length with the sampling. The sampled lengths are also sorted so that in the final gene list, shorter genes have less counts as compared to the longer ones. The default is FALSE.

Details

The simulated data generation involves a lot of random sampling. For guaranteed reproducibility, be sure to use set.seed prior to any calculations. By default, when the metaseqR2 package is loaded, the seed is set to 42.

Value

A named list with two members. The first member (simdata) contains the synthetic dataset

Author(s)

Panagiotis Moulos

Examples

dataMatrix <- metaseqR2:::exampleCountData(2000)
## File "bottomly_read_counts.txt" from the ReCount database
#download.file(paste("http://bowtie-bio.sourceforge.net/recount/",
#    "countTables/bottomly_count_table.txt",sep=""),
#    destfile="~/bottomly_count_table.txt")
N <- 2000
#parList <- estimateSimParams("~/bottomly_read_counts.txt")
parList <- estimateSimParams(dataMatrix,libsizeGt=3e+4)
sim <- makeSimDataSd(N,parList)
synthData <- sim$simdata
trueDeg <- which(sim$truedeg!=0)

pmoulos/metaseqR2 documentation built on May 20, 2024, 5:48 a.m.