Bootstrapping cell type identifications (scRNA-seq)
I would use permutation:
i) Get the difference that you are interested in, i.e. number/proportion of monocytes in one group vs the other.
ii) Get the relevant columns (condition, cell label, ...) from the @meta.data slot of your seurat objects and rbind().
iii) Shuffle the cell labels randomly and split the data frame into two (per condition).
iv) Compare the difference between the cell counts/proportions of interest from the two permuted data frames and save this value.
iv) Repeat iii and iv for 100s of times and calculate how many times have you observed a difference at least as extreme as your real observation (i). Once divided by the number of trials, this would give you a p-value stating how likely it is that you get the observed value.
I have to add that batch effects are severe in scRNA-seq and hence a linear model where you might be able to account for batches could be more helpful.