RNA-Seq Generation/Modification for Simulation
Binomial thinning for altering total gene expression levels
Binomial thinning for altering library size.
Binomial thinning for altering read-depth.
Base binomial thinning function.
Group assignment that is correlated with latent factors.
Estimates the effective correlation.
Binomial thinning for differential expression analysis.
Estimate the surrogate variables.
Fixes an invalid target correlation.
Permute the design matrix so that it is approximately correlated with ...
Apply Poisson thinning to a matrix of count data.
Subsample the rows and columns of a count matrix.
seqgendiff: RNA-Seq Generation/Modification for Simulation
Provide summary output of a ThinData S3 object.
Binomial thinning in the two-group model.
Converts a ThinData S3 object into a DESeqDataSet S4 object.
Converts a ThinData S3 object into a SummarizedExperiment S4 object.
Group assignment independent of anything.
Generates/modifies RNA-seq data for use in simulations. We provide a suite of functions that will add a known amount of signal to a real RNA-seq dataset. The advantage of using this approach over simulating under a theoretical distribution is that common/annoying aspects of the data are more preserved, giving a more realistic evaluation of your method. The main functions are select_counts(), thin_diff(), thin_lib(), thin_gene(), thin_2group(), thin_all(), and effective_cor(). See Gerard (2020) <doi:10.1186/s12859-020-3450-9> for details on the implemented methods.