Gene names, data corruption and Excel: a 2021 update

It’s an old favourite of this blog, isn’t it. We had Gene name errors and Excel: lessons not learned (2012). Followed by Data corruption using Excel: 12+ years and counting (2016). Perhaps most depressingly of all, the conclusion of the trilogy, When your tools are broken, just change the data (2019-20).

Well, I’m happy (?) to see the publication of the latest instalment, inspired in part by the title of my first post: Gene name errors: Lessons not learned, from Mark Ziemann’s group. Here’s the accompanying Twitter thread. Summary: it’s even worse than we thought.

Tagging this one with the R tag, because the group are publishing monthly RMarkdown reports here. Congratulations Nature Communications!

As a footnote: you don’t escape this kind of thing when you leave bioinformatics. I listened to a colleague in a data science meeting yesterday declare that “we won’t be putting anything into production that relies on data supplied to us as spreadsheets”.

Oops: taxonomy #fail

My journey from bench scientist to bioinformatician began with archaeal genomes. So I was somewhat startled to read The catalytic mechanism for aerobic formation of methane by bacteria, in which we learn about the “ocean-dwelling bacterium Nitrosopumilus maritimus“.

So was Jonathan Eisen of course and you should go and read why. Every top hit in a Web search for that organism tells us that Nitrosopumilus maritimus is an archaeon.

Looking forward to a rapid correction and apology from Nature.

Title edited from “phylogeny” to “taxonomy” at the insistence of @BioinfoTools ;)

Gene name errors and Excel: lessons not learned

June 23, 2004. BMC Bioinformatics publishes “Mistaken Identifiers: Gene name errors can be introduced inadvertently when using Excel in bioinformatics”. We roll our eyes. Do people really do that? Is it really worthy of publication? However, we admit that if it happens then it’s good that people know about it.

October 17, 2012. A colleague on our internal Yammer network writes:
Read the rest…

We really don’t care what statistical method you used

Update: as pointed out in the comments, the amusing error in this article has been “corrected” (or at least, “edited away”). Thanks for your interest.
Update: I note that this article is now “Highly Accessed” ;)

An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver
BMC Systems Biology 2011, 5(Suppl 3):S4

(insert statistical method here). No, really.

With thanks to Simon J Greenhill and Dave Winter.

Trust no-one: errors and irreproducibility in public data

Just when I was beginning to despair at the state of publicly-available microarray data, someone sent me an article which…increased my despair.

The article is:

Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology (2009)
Keith A. Baggerly and Kevin R. Coombes
Ann. Appl. Stat. 3(4): 1309-1334

It escaped my attention last year, in part because “Annals of Applied Statistics” is not high on my journal radar. However, other bloggers did pick it up: see posts at Reproducible Research Ideas and The Endeavour.

In this article, the authors examine several papers in their words “purporting to use microarray-based signatures of drug sensitivity derived from cell lines to predict patient response.” They find that not only are the results difficult to reproduce but in several cases, they simply cannot be reproduced due to simple, avoidable errors. In the introduction, they note that:

…a recent survey [Ioannidis et al. (2009)] of 18 quantitative papers published in Nature Genetics in the past two years found reproducibility was not achievable even in principle for 10.

You can get an idea of how bad things are by skimming through the sub-headings in the article. Here’s a selection of them:
Read the rest…