Given a matrix of real RNA-seq counts, this function will apply a
thinning factor uniformly to every count in this matrix. This uniformly
lowers the read-depth for the entire dataset. The thinning factor should
be provided on the log2-scale. This is a specific application of the
binomial thinning approach in
thin_diff. Though this particular
form of thinning was used by Robinson and Storey (2014) in the context
of deriving read-depth suggestions. It is also
described in detail in Gerard (2020).
thin_all(mat, thinlog2, type = c("thin", "mult"))
A numeric matrix of RNA-seq counts. The rows index the genes and the columns index the samples.
A numeric scalar. This is the amount to shrink each count
Should we apply binomial thinning (
A list-like S3 object of class
Components include some or all of the following:
The modified matrix of counts.
The design matrix of variables used to simulate
signal. This is made by column-binding
design_fixed and the
permuted version of
A matrix of coefficients corresponding to
Additional variables that should be included in
your design matrix in downstream fittings. This is made by
column-binding the vector of 1's with
A matrix of estimated surrogate variables. In simulation studies you would probably leave this out and estimate your own surrogate variables.
A matrix of target correlations between the
surrogate variables and the permuted variables in the design matrix.
This might be different from the
target_cor you input because
we pass it through
fix_cor to ensure
positive semi-definiteness of the resulting covariance matrix.
A matrix of simulated variables used to
design_perm if the
target_cor is not
Gerard, D (2020). "Data-based RNA-seq simulations by binomial thinning." BMC Bioinformatics. 21(1), 206. doi: 10.1186/s12859-020-3450-9.
Robinson, David G., and John D. Storey. "subSeq: determining appropriate sequencing depth through efficient read subsampling." Bioinformatics 30, no. 23 (2014): 3424-3426. doi: 10.1093/bioinformatics/btu552.
For subsampling the rows and columns of your real RNA-seq count matrix prior to applying binomial thinning.
For the more general thinning approach.
For thinning sample-wise.
For thinning gene-wise.
For converting a ThinData object to a SummarizedExperiment object.
For converting a ThinData object to a DESeqDataSet object.
## Generate count data and set thinning factor ## In practice, you would obtain mat from a real dataset, not simulate it. set.seed(1) n <- 10 p <- 1000 lambda <- 1000 mat <- matrix(lambda, ncol = n, nrow = p) thinlog2 <- 1 ## Thin read-depths thout <- thin_all(mat = mat, thinlog2 = thinlog2) ## Compare empirical and theoretical proportions mean(thout$mat) / lambda 2 ^ -thinlog2
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