This package is designed to take real RNA-seq data and alter it by adding a known amount of signal. You can then use this modified dataset in simulation studies for differential expression analysis, factor analysis, confounder adjustment, or library size adjustment. 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. For example, in the real world data are not normally (or negative binomially) distributed and unobserved confounding is a major issue. This package will simulate data that exhibit these characteristics. The methods used in this package are described in detail in Gerard (2020).
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.
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.
The specific application of thinning in the two-group model.
The specific application of library size thinning.
The specific application of total gene expression thinning.
The specific application of thinning all counts.
Returns an estimate of the actual correlation between surrogate variables and a user-specified design matrix.
Converts a ThinData object to a SummarizedExperiment object.
Converts a ThinData object to a DESeqDataSet object.
Gerard, D (2020). "Data-based RNA-seq simulations by binomial thinning." BMC Bioinformatics. 21(1), 206. doi: 10.1186/s12859-020-3450-9.
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