seqgendiff (version 1.2.2)

thin_base: Base binomial thinning function.

Description

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).

Usage

thin_base(mat, designmat, coefmat, relative = TRUE, type = c("thin", "mult"))

Arguments

mat

A numeric matrix of RNA-seq counts. The rows index the genes and the columns index the samples.

designmat

A design matrix. The rows index the samples and the columns index the variables. The intercept should not be included.

coefmat

A matrix of coefficients. The rows index the genes and the columns index the samples.

relative

A logical. Should we apply relative thinning (TRUE) or absolute thinning (FALSE). Only experts should change the default.

type

Should we apply binomial thinning (type = "thin") or just naive multiplication of the counts (type = "mult"). You should always have this set to "thin".

Value

A matrix of new RNA-seq read-counts. This matrix has the signal added from designmat and coefmat.

References

• Gerard, D (2020). "Data-based RNA-seq simulations by binomial thinning." BMC Bioinformatics. 21(1), 206. doi: 10.1186/s12859-020-3450-9.

See Also

select_counts

For subsampling the rows and columns of your real RNA-seq count matrix prior to applying binomial thinning.

thin_diff

For the function most users should be using for general-purpose binomial thinning.

thin_2group

For the specific application of thinning in the two-group model.

thin_lib

For the specific application of library size thinning.

thin_gene

For the specific application of total gene expression thinning.

thin_all

For the specific application of thinning all counts uniformly.

Examples

# NOT RUN {
## 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)

# }