Given a matrix of real RNA-seq counts, this function will apply a
separate, user-provided thinning factor to each gene. This uniformly
lowers the counts for all samples in a gene. The thinning factor
should be provided on the log2-scale. This is a specific application
of the binomial thinning approach in
thin_diff. The method is
described in detail in Gerard (2020).
thin_gene(mat, thinlog2, relative = FALSE, type = c("thin", "mult"))
A numeric matrix of RNA-seq counts. The rows index the genes and the columns index the samples.
A vector of numerics. Element i is the amount to thin (on the log2 scale) for gene i. For example, a value of 0 means that we do not thin, a value of 1 means that we thin by a factor of 2, a value of 2 means we thin by a factor of 4, etc.
A logical. Should we apply relative thinning (
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.
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 instead of gene-wise.
For thinning all counts uniformly.
For converting a ThinData object to a SummarizedExperiment object.
For converting a ThinData object to a DESeqDataSet object.
## Generate count data and thinning factors ## 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 <- rexp(n = p, rate = 1) ## Thin total gene expressions thout <- thin_gene(mat = mat, thinlog2 = thinlog2) ## Compare empirical thinning proportions to specified thinning proportions empirical_propvec <- rowMeans(thout$mat) / lambda specified_propvec <- 2 ^ (-thinlog2) plot(empirical_propvec, specified_propvec, xlab = "Empirical Thinning Proportion", ylab = "Specified Thinning Proportion") abline(0, 1, col = 2, lwd = 2)
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