estimateSimParams {metaseqR2} | R Documentation

## Estimate negative binomial parameters from real data

### Description

This function reads a read counts table containing real RNA-Seq data (preferebly with more than 20 samples so as to get as much accurate as possible estimations) and calculates a population of count means and dispersion parameters which can be used to simulate an RNA-Seq dataset with synthetic genes by drawing from a negative binomial distribution. This function works in the same way as described in (Soneson and Delorenzi, BMC Bioinformatics, 2013) and (Robles et al., BMC Genomics, 2012).

### Usage

        estimateSimParams(realCounts, libsizeGt = 3e+6,
            rowmeansGt = 5,eps = 1e-11, rc = NULL, draw = FALSE)

### Arguments

`realCounts` | a text tab-delimited file with real RNA-Seq data. See Details.
`libsizeGt` | a library size below which samples are excluded from parameter estimation (default: `3000000`).
`rowmeansGt` | a row means (mean counts over samples for each gene) below which genes are excluded from parameter estimation (default: 5).
`eps` | the tolerance for the convergence of `optimize` function. Defaults to 1e-11.
`rc` | in case of parallel optimization, the fraction of the available cores to use.
`draw` | boolean to determine whether to plot the estimated simulation parameters (mean and dispersion) or not. Defaults to `FALSE` (do not draw a mean-dispersion scatterplot).

### Details

Regarding `realCounts`, the file should strictly contain a unique gene name (e.g. Ensembl accession) in the first column and all other columns should contain read counts for each gene. Each column must be named with a unique sample identifier. See examples in the ReCount database http://bowtie-bio.sourceforge.net/recount/.

Also, the parameter estimation 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: `muHat` which contains negative binomial mean estimates and `phiHat` which contains dispersion estimates.

### Author(s)

Panagiotis Moulos

### Examples

    dataMatrix <- metaseqR2:::exampleCountData(2000)
    parList <- estimateSimParams(dataMatrix,libsizeGt=3e+4)

[Package metaseqR2 version 1.0.11 Index]