Both the methods and David's twitter clearly describe the linear regression as the method and how it works with spike-in. In practice, it is a lot more complicated, as the signal-to-noise ratio can interfere with the regression and primer N-1 synthesis errors need to be accounted for. That is why we are willing to help other laboratories with our bioinformatics pipeline.

It looks like David answered that here: https://news.ycombinator.com/item?id=22808525. I agree that it's better not to use buzzwords, and have edited the top text to say "linear regression" instead of "machine learning".

(This is actually part of the standard advice we give to YC startups who are launching on HN: don't talk like you would to investors—talk as you do to fellow implementors.)