estimate.sim.params {metaseqR} | 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

        estimate.sim.params(real.counts, libsize.gt = 3e+6,
            rowmeans.gt = 5,eps = 1e-11,
            restrict.cores = 0.8, seed = 42, draw = FALSE)

### Arguments

`real.counts` | a text tab-delimited file with real RNA-Seq data. 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/.
`libsize.gt` | a library size below which samples are excluded from parameter estimation (default: 3000000).
`rowmeans.gt` | 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.
`restrict.cores` | in case of parallel optimization, the fraction of the available cores to use.
`seed` | a seed to use with random number generation for reproducibility.
`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).

### Value

A named list with two members: `mu.hat` which contains negative binomial mean estimates and `phi.hat` which contains dispersion estimates.

### Author(s)

Panagiotis Moulos

### Examples

    # Dowload locally the 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")
    # Estimate simulation parameters
    par.list <- estimate.sim.params("~/bottomly_count_table.txt")

[Package metaseqR version 1.26.0 Index]