I posted a package to CRAN that will take real RNA-seq data (either single-cell or bulk) and alter it by adding signal to it. This signal is in the form of a generalized linear model with a log (base-2) link function under a Poisson / negative binomial / mixture of negative binomials distribution. The advantage of this way of simulating data is that you can see how your method behaves when the simulated data exhibit common (and annoying) features of real data. This is without you having to specify these features a priori. We call the way we add signal “binomial thinning”.
The main functions are:
select_counts(): Subsample the columns and rows of a real RNA-seq
count matrix. You would then feed this sub-matrix into one of the
thinning functions below.
thin_diff(): The function most users should be using for
general-purpose binomial thinning. For the special applications of
the two-group model or library/gene thinning, see the functions
listed below.
thin_2group(): The specific application of thinning in the two-group
model.
thin_lib(): The specific application of library size thinning.
thin_gene(): The specific application of total gene expression
thinning.
thin_all(): The specific application of thinning all counts.
effective_cor(): Returns an estimate of the actual correlation
between the surrogate variables and a user-specified design matrix.
ThinDataToSummarizedExperiment(): Converts a ThinData object to a
SummarizedExperiment object.
ThinDataToDESeqDataSet(): Converts a ThinData object to a
DESeqDataSet object.
To install from CRAN, run the following code in R:
install.packages("seqgendiff")
To install the latest version of seqgendiff, run the following code in R:
install.packages("devtools")
devtools::install_github("dcgerard/seqgendiff")
To get started, check out the vignettes by running the following in R:
library(seqgendiff)
browseVignettes(package = "seqgendiff")
Or you can check out the vignettes I post online: https://dcgerard.github.io/seqgendiff/.
If you use this package, please cite:
Gerard D (2020). “Data-based RNA-seq simulations by binomial thinning.” BMC Bioinformatics. 21(1), 206. doi: 10.1186/s12859-020-3450-9.
A BibTeX entry for LaTeX users is
@article{gerard2020data,
author = {Gerard, David},
title = {Data-based {RNA}-seq simulations by binomial thinning},
year = {2020},
volume={21},
number={1},
pages={206},
doi = {10.1186/s12859-020-3450-9},
publisher = {BioMed Central Ltd},
journal = {BMC Bioinformatics}
}