Permute celldex ref labels and compare true cell assignment to distribution of assignments

We will need some way to measure the confidence in the label assignment obtained from running `SingleR` with a given reference. One way of doing this would be to shuffle the sample labels of the reference dataset prior to training/ identifying marker genes and classifying cell types in the test dataset. This should be done over a set number of permutations to obtain a distribution of cell type assignments for each cell in the test dataset. We can then compare the true label to the distribution of assignments to obtain a p-value.

Before we can do this we will need to figure out the following:
- The run time for training and classifying to see an estimate of how long we think this will take.
- Figure out either a score or some sort of output value that is comparable across runs of `SingleR` to use for comparing the true label to the distribution. For example, if we can use the score computed by `SingleR`, we would compare the score for a given cell corresponding to the true label to the score for that cell for that same true label across each permutation.

We will probably want to have a function that takes as input the sce object of interest and the reference data to be used. Then within that function, the permutations will be performed prior to running `SingleR`. We should also use parallelization whenever possible.

Start by benchmarking the runtime of training and classifying with SingleR, then determine whether its scores are comparable across runs. Design a function accepting the SCE object and reference data, performing repeated label permutations before SingleR classification, with parallelization where possible. Done means the true-label assignment can be compared with the permutation distribution to obtain a p-value.

Written by the indexing model from the issue text.