knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

library(seqgendiff) library(limma) set.seed(31) ## for reproducibility

We demonstrate how to apply the different thinning functions available in seqgendiff. The following contains guidelines for the workflow of a single repetition in a simulation study.

The methods used here are described in Gerard (2020).

We will use simulated data for this vignette. Though in practice you would obtain the RNA-seq counts from a real dataset.

nsamp <- 100 ngene <- 10000 mat <- matrix(stats::rpois(n = nsamp * ngene, lambda = 100), nrow = ngene, ncol = nsamp)

Each repetition of a simulation study, you should randomly subset your RNA-seq
data so that your results are not dependent on the quirks of a few
individuals/genes. The function to do this is `select_counts()`

.

submat <- select_counts(mat = mat, nsamp = 6, ngene = 1000)

The default is to randomly select the samples and genes. Though there are
many options available on how to select the genes using the `gselect`

argument.

If the row and column names of the original matrix are `NULL`

, then the
row and column names of the returned submatrix contain the indices of the
selected rows and columns of the original matrix.

head(rownames(submat)) head(colnames(submat))

If you are exploring the effects of heterogeneous library sizes, use `thin_lib()`

.
You specify the thinning factor on the log-2 scale, so a value of 0 means
no thinning, a value of 1 means thin by half, a value of 2 means thin by
1/4, etc. You need to specify this scaling factor for all samples.

scaling_factor <- seq_len(ncol(submat)) scaling_factor thout <- thin_lib(mat = submat, thinlog2 = scaling_factor)

We can verify that thinning was performed correctly by looking at the empirical thinning amount.

## Empirical thinning colSums(thout$mat) / colSums(submat) ## Specified thinning 2 ^ -scaling_factor

A similar function exists to thin gene-wise rather than sample-wise:
`thin_gene()`

.

To uniformly thin all counts, use `thin_all()`

. This might be useful for
determining read-depth suggestions. It takes as input a single universal
scaling factor on the log-2 scale.

thout <- thin_all(mat = submat, thinlog2 = 1)

We can verify that we approximately halved all counts:

sum(thout$mat) / sum(submat)

Use `thin_diff()`

for general thinning. For this function, you need to
specify both the coefficient matrix and the design matrix.

designmat <- cbind(rep(c(0, 1), each = ncol(submat) / 2), rep(c(0, 1), length.out = ncol(submat))) designmat coefmat <- matrix(stats::rnorm(ncol(designmat) * nrow(submat)), ncol = ncol(designmat), nrow = nrow(submat)) head(coefmat)

Once we have the coefficient and design matrices, we can thin.

thout <- thin_diff(mat = submat, design_fixed = designmat, coef_fixed = coefmat)

We can verify that we thinned correctly using the voom-limma pipeline.

new_design <- cbind(thout$design_obs, thout$designmat) vout <- limma::voom(counts = thout$mat, design = new_design) lout <- limma::lmFit(vout) coefhat <- coef(lout)[, -1, drop = FALSE]

We'll plot the true coefficients against their estimates.

oldpar <- par(mar = c(2.5, 2.5, 1, 0) + 0.1, mgp = c(1.5, 0.5, 0)) plot(x = coefmat[, 1], y = coefhat[, 1], xlab = "True Coefficient", ylab = "Estimated Coefficient", main = "First Variable", pch = 16) abline(a = 0, b = 1, lty = 2, col = 2, lwd = 2) plot(x = coefmat[, 2], y = coefhat[, 2], xlab = "True Coefficient", ylab = "Estimated Coefficient", main = "Second Variable", pch = 16) abline(a = 0, b = 1, lty = 2, col = 2, lwd = 2) par(oldpar)

The difference between the `design_fixed`

and `design_perm`

arguments is that
the rows in `design_perm`

are permuted before applying thinning. Without
any other arguments, this makes the design variables independent of any
surrogate variables. With the additional specification of the `target_cor`

argument, we try to control the amount of correlation between the design
variables in `design_perm`

and any surrogate variables.

Let's target for a correlation of 0.9 between the first surrogate variable and the first design variable, and a correlation of 0 between the first surrogate variable and the second design variable.

target_cor <- matrix(c(0.9, 0), nrow = 2) target_cor thout_cor <- thin_diff(mat = submat, design_perm = designmat, coef_perm = coefmat, target_cor = target_cor)

The first variable is indeed more strongly correlated with the estimated surrogate variable:

cor(thout_cor$designmat, thout_cor$sv)

The actual correlation between the permuted design matrix and the surrogate
variables will not be the target correlation. But we can estimate what the
actual correlation is using the function `effective_cor()`

.

eout <- effective_cor(design_perm = designmat, sv = thout_cor$sv, target_cor = target_cor, iternum = 50) eout

I am only using 50 iterations here for speed reasons, but you should stick
to the defaults for `iternum`

.

For the special case when your design matrix is just a group indicator (that is,
you have two groups of individuals), you can use the function `thin_2group()`

.
Let's generate data from the two-group model where 90% of genes are null
and the non-null effects are gamma-distributed.

thout <- thin_2group(mat = submat, prop_null = 0.9, signal_fun = stats::rgamma, signal_params = list(shape = 1, rate = 1))

We can again verify that we thinned appropriately using the voom-limma pipeline:

new_design <- cbind(thout$design_obs, thout$designmat) new_design vout <- limma::voom(counts = thout$mat, design = new_design) lout <- limma::lmFit(vout) coefhat <- coef(lout)[, 2, drop = FALSE]

And we can plot the results

oldpar <- par(mar = c(2.5, 2.5, 1, 0) + 0.1, mgp = c(1.5, 0.5, 0)) plot(x = thout$coefmat, y = coefhat, xlab = "True Coefficient", ylab = "Estimated Coefficient", main = "First Variable", pch = 16) abline(a = 0, b = 1, lty = 2, col = 2, lwd = 2)

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

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