Expanded cell filtering for datasets with protein data

When citeseq = True and both adata (gene counts) + adata_prot (antibody counts) are loaded, we should include some cell filtering based on the protein counts metric. I already spotted 2 scenarios requiring filtering:

1) If a cell has 0 counts for all antibodies, normalization function `bc.st.clr_normalize(adata_prot, os.path.join(results_folder_citeseq, 'citeseq'))` will raise this error:
```
ValueError: Input matrix cannot have rows with all zeros
```
Suggestion: add a param `min_protein_counts` = 1, so that cells with antibody_counts sum = 0 will be excluded

2) During sample prep antibodies can aggregate leading to exceptionally high counts in few cells (see this [10X note](https://kb.10xgenomics.com/hc/en-us/articles/360042247271-High-fraction-of-reads-coming-from-barcodes-with-very-high-UMI-counts)). 1 solution is to work with the filtered cellranger matrix (which already excludes such cells), another solution (not exclusive) is to include a parameter `max_protein_counts` to exclude cells above a threshold.

**Add in this issue other possible scenarios to be considered.**

_Note: the filtering should be performed at the beginning of besca, so that the gene analysis part has the same cells than the protein analysis._

Start by tracing the beginning of besca and the call to bc.st.clr_normalize(adata_prot, os.path.join(results_folder_citeseq, 'citeseq')). Determine where protein-based filtering must occur so gene and protein analyses use the same cells. Done means the requested minimum and maximum protein-count handling, plus any agreed additional scenarios, are defined and applied consistently.

Written by the indexing model from the issue text.