highly deviant genes implementation

[ktpolanski]

An implementation of highly deviant gene identification from the 2019 GLMPCA paper. I'm rather fond of the method, as it's a straightforward statistical measure, and comes with significance testing as a form of data-driven cutoff.

I put it in a new highly_deviant_genes() function, as:

• it comes with a number of unique parameters, and there's only so many different algorithms highly_variable_genes() can house
• the paper argues that highly deviant is different from highly variable

I acknowledge that there are no tests, I'm hoping to get some assistance with that if possible.

[codecov]

Codecov Report

Merging #1765 (3569f57) into master (560bd5d) will decrease coverage by 0.38%.
The diff coverage is 19.27%.

@@            Coverage Diff             @@
##           master    #1765      +/-   ##
==========================================
- Coverage   71.18%   70.80%   -0.39%     
==========================================
  Files          92       93       +1     
  Lines       11190    11273      +83     
==========================================
+ Hits         7966     7982      +16     
- Misses       3224     3291      +67     

Impacted Files	Coverage Δ
scanpy/preprocessing/_highly_deviant_genes.py	18.29% <18.29%> (ø)
scanpy/preprocessing/__init__.py	100.00% <100.00%> (ø)

[ivirshup]

I like that this method is fairly simple, and could have a meaningful cutoff, but I think I'd like more evidence of it's usefulness before thinking about including it.

I have two main points of concern:

• Are there examples of this method being used outside of the glmPCA paper? I would at least like to know that reasonable results can be found downstream of this.
• In the glmPCA paper, the identified genes are highly correlated (1) with highly expressed genes, and lowly correlated (.3 with highly variable gene selection. While I'm not sure which highly variable gene method they compared against, should the low correlation with common practice give us pause?

@giovp