There is a new Nature NoMethods article on linear modeling of multi-plex TMT data (yes, TMT plexes are like batches). If you think that sounds interesting, you can learn all about how to analyze multi-plex (and single plex) TMT data at my GitHub site: https://github.com/pwilmart/Start_Here.

Interestingly (irritatingly?), internal reference scaling (https://www.sciencedirect.com/science/article/pii/S1535947620323938) is not mentioned in the new paper at all. It was published in 2017 in MCP and has over 250 citations at Google Scholar…

#proteomics

GitHub - pwilmart/Start_Here: Navigation links (and brief descriptions) to my repositories.

Navigation links (and brief descriptions) to my repositories. - GitHub - pwilmart/Start_Here: Navigation links (and brief descriptions) to my repositories.

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@pwilmart

But this one came from Calico!

I remember a really cool ~1k sample study (mouse) they had in JPR some years ago, but yeah, this oversight stinks. I can’t tell if during editing CNS makes people write this way (look how new we are!) or they write this way to get in. Assume it’s the latter.

(You can see I have a track record of tons of glam journal pubs, so I totally know what’s happening. /s)

@neely There is a reason only a handful of proteomics labs get highly cited. No one pays any attention to any work outside of those labs. We had “Extended Multiplexing of Tandem Mass Tags (TMT)…” in the title to make it easier to find on PubMed, etc. I can live with 260 citations of a paper that describes a concept for experimental design and data analysis.
@pwilmart And more than the MCP paper’s impressive citation counts, I think the blog has helped even more people adopt it who then go and spread the good news (people like @UCDProteomics ). But yeah, I hear you on the citations.
@pwilmart crazy ! Unfortunately this isn’t surprising