Given a matrix of counts (Y) where log_2(E[Y]) = Q,
a design matrix (X), and a matrix of coefficients (B),
thin_diff will generate a new matrix of counts such that
log_2(E[Y]) = BX' + u1' + Q, where u is some vector
of intercept coefficients. This function is used by all other
thinning functions. The method is
described in detail in Gerard (2020).
thin_base(mat, designmat, coefmat, relative = TRUE, type = c("thin", "mult"))
A numeric matrix of RNA-seq counts. The rows index the genes and the columns index the samples.
A design matrix. The rows index the samples and the columns index the variables. The intercept should not be included.
A matrix of coefficients. The rows index the genes and the columns index the samples.
A logical. Should we apply relative thinning (
Should we apply binomial thinning (
A matrix of new RNA-seq read-counts. This matrix has the signal
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 function most users should be using for general-purpose binomial thinning.
For the specific application of thinning in the two-group model.
For the specific application of library size thinning.
For the specific application of total gene expression thinning.
For the specific application of thinning all counts uniformly.
## Simulate data from given matrix of counts ## In practice, you would obtain Y from a real dataset, not simulate it. set.seed(1) nsamp <- 10 ngene <- 1000 Y <- matrix(stats::rpois(nsamp * ngene, lambda = 100), nrow = ngene) X <- matrix(rep(c(0, 1), length.out = nsamp)) B <- matrix(seq(3, 0, length.out = ngene)) Ynew <- thin_base(mat = Y, designmat = X, coefmat = B) ## Demonstrate how the log2 effect size is B Bhat <- coefficients(lm(t(log2(Ynew)) ~ X))["X", ] plot(B, Bhat, xlab = "Coefficients", ylab = "Coefficient Estimates") abline(0, 1, col = 2, lwd = 2)
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