I would like to simulate a dataset with two distinct groups of cells, and with the desired amount of DE genes if possible. In my case, I just want to test a method whether its performance is sensitive with a series of the increasing number of DE genes. For example, I would like to simulate a dataset of 500 cells and 5000 genes which has only two groups, with 150 DE genes and other non-DE genes. The number of non-DE genes is not necessarily equal to 5000 - 150 = 4850. According to the partition of genes, the dataset can be divided into two smaller datasets, which share the same cells. A serial number of DE genes, i.e., 50,60,70,...150 genes are gradually planted to the other dataset that only contains non-DE genes, to check whether the performance of a given method is sensitive to the varying DE genes.

My question is how to use splatter to solve this problem? If not, can I use other packages combined with splatter to get my purpose?

Hi chenxofhit

I'm not quite sure I understand the design you want but here is an example that might get us started.

library(splatter)

sim <- splatSimulate(nGenes = 5000,
                     batchCells = c(250, 250),
                     group.prob = c(0.5, 0.5),
                     de.prob = c(0.01, 0.01),
                     method = "groups")

This would generate a dataset with:

• 5000 genes
• Two technical batches with 250 cells in each batch
• Two groups (approximating cell types), each of which is equally likely (~250 cells in each, or ~125 from each batch)
• Around 1 % of genes (~50) differentially expressed in each group

Is that something along the lines of what you are looking for? This is using the Splat simulation but there are several other simulations in Splatter that might be useful and of course more in other packages.