Normalization and log1p

1. illico does not care about your data being normalized or not, it is up to you to apply the preprocessing of your choice before running the tests. It is expected that illico is slower if ran on total-count normalized data by a factor ~2. This is because if applied on non total-count normalized data, sorting relies on radix sort which is faster than the usual quicksort (that is used if testing total-count normalized data).

2. In order to avoid any unintended conversion, or rely on failure-prone rules of thumb, illico requires the user to indicate if the input data is log1p or not. This is only used to compute appropriate fold-change, and does not impact test (p-value and statistic) results. Note also that unlike scanpy, if data is log1p, fold-change is computed by exponentiating before aggregation by default. This behavior can be changed by passing exp_post_agg=False to asymptotic_wilcoxon. See the results section for more details on that.