estimateSimParams | R Documentation |
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).
estimateSimParams(realCounts, libsizeGt = 3e+6,
rowmeansGt = 5,eps = 1e-11, rc = NULL, draw = FALSE)
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: |
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 |
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
|
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
.
A named list with two members: muHat
which contains negative binomial mean
estimates and phiHat
which contains
dispersion estimates.
Panagiotis Moulos
dataMatrix <- metaseqR2:::exampleCountData(2000)
parList <- estimateSimParams(dataMatrix,libsizeGt=3e+4)
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