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This package 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 ageneralized linear model with a log (base-2) link function under aPoisson / negative binomial / mixture of negative binomialsdistribution. The advantage of this way of simulating data is that youcan see how your method behaves when the simulated data exhibit common(and annoying) features of real data. This is without you having tospecify these featuresa 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-seqcount matrix. You would then feed this sub-matrix into one of thethinning functions below.
• thin_diff(): The function most users should be using forgeneral-purpose binomial thinning. For the special applications of thetwo-group model or library/gene thinning, see the functions listedbelow.
• thin_2group(): The specific application of thinning in the two-groupmodel.
• thin_lib(): The specific application of library size thinning.
• thin_gene(): The specific application of total gene expressionthinning.
• thin_all(): The specific application of thinning all counts.
• effective_cor(): Returns an estimate of the actual correlationbetween the surrogate variables and a user-specified design matrix.
• ThinDataToSummarizedExperiment(): Converts aThinData object to aSummarizedExperiment() object.
• ThinDataToDESeqDataSet(): Converts aThinData object to aDESeqDataSet object.

If you find a bug or want a new feature, please submit anissue.

Check outNEWS for updates.

To install from CRAN, run the following code in R:

install.packages("seqgendiff")

To install the latest version of seqgendiff, run the following code inR:

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 binomialthinning.”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}}

Please note that the ‘seqgendiff’ project is released with aContributor Code ofConduct.By contributing to this project, you agree to abide by its terms.