What happens if my pragmatic approach to data analysis gets reviewed by a card-carrying statistician? “The absence of competent statistical guidance in this MS is becoming painfully obvious.” Oh my!
It’s been almost a year since Trinh et al, “Practical and Robust Identification of Molecular Subtypes in Colorectal Cancer by Immunohistochemistry” came out in Clinical Cancer Research. Let me tell you a bit about the reviews we got back in summer 2016.
In a recent paper in Nature Reviews Cancer, Maley et al set out to define a consensus framework for classifying neoplasms. The paper’s premise is that such a theoretical framework is a necessary first step for developing new quantitative approaches. I disagree. I argue that the paper highlights the limited practical relevance of a purely intellectual exercise. Solid classification frameworks of clinical relevance need more detail and need to be grounded on applicability to real data in clinical practice.
TL;DR
For those of you in hurry, let me sum up what my claims are:
When I started my PI career, my first cover letter to a glamour journal emphatically pointed out that my cutting-edge, ground-breaking work was the first and firstest to do X.
Feedback from senior colleagues was: “Drop that blech! Better say what your insight into X actually is, and in what way it is profound.” — Good advice. Because novelty is overrated, insight rules.
So I wasn’t too surprised how Barak Cohen answered the question “How should novelty be valued in science?” in the last issue of eLife. I would never put a question mark into a title, if the answer is so clear:
Laborjournal.de just published a German translation of my opinion piece “All biology is computational biology” in PLoS Biology earlier this year.
Have a look at it here: http://www.laborjournal.de/rubric/essays/essays2017/e17_10.lasso
Luckily I didn’t have to translate it myself. My Deutsch has been getting pretty schlecht lately.
And this is reading quite well, don’t you think?
Almost a month has passed since I published an opinion piece called “All biology is computational biology” in PLoS Biology.
In my paper, I envisioned a biology that explicitly and clearly acknowledges how much it has changed over the last 20 years, how much its questions have changed, and how much the practice of doing biology has changed. I envisioned a biology that gives credit broadly and fairly to everybody who contributed to key insights – regardless of what tools they used.
As intended, my paper provoked many responses from the community, and in the following you find my thoughts on some particularly interesting comments.
I met Emma Ganley from PLOS Biology at the #scidata16 conference last year, and shortly afterwards she invited me to contribute to the PLOS Biology collection Research Matters:
In this series, we ask leading scientists in their respective fields to explain clearly and engagingly for a lay audience why the research carried out in their laboratories – and those of their collaborators and their colleagues – matters.
It wasn’t immediately clear to me, what I should write about. I tend to label myself a cancer researcher nowadays, but cancer research does not need any explanation why it matters – unfortunate as that is.
At the same time, I am a computational biologist – and here I thought was a much bigger need to explain why it matters. The question is not so much why computational biology and bioinformatics are useful (nobody seems to question that it’s handy to have the geeks around) but why is it biological research, rather than just a support and service activity.
Every citation is a good citation, right? So I was pleased to see that even the little pamphlet I wrote about my lab last year has a couple of citations now (ok, one is a self citation, please don’t tell anyone).
“You are not working for me; I am working with you” is what I said back then.
And my paper got cited here: “Are Leadership and Management Essential for Good Research? An Interview Study of Genetic Researchers” by Alison L. Antes, Adelina Mart and James M. DuBois in the Journal of Empirical Research on Human Research Ethics.
Leadership in Science – a topic I am definitely interested in.
Let’s see where and how they cite me ..
So exciting!
The latest post at Shit My Reviewers Say is “My first concern is that I don’t get it.”
And the obvious response is illustrated by a picture saying “Your problems with me are not my problems, those are your problems.“
What can you do as an author if the reviewer is just too stupid to understand your ingenuity?
But … and this is a big but … there are areas of research where I would use that reviewer’s comment myself. If, say, you are writing about probabilistic models in cancer genomics and I can’t make any sense of what you are saying, it is your problem, not mine.
Here is an example. The ABSOLUTE paper on “Absolute quantification of somatic DNA alterations in human cancer”.
In case you wondered what’s wrong with biomedical research, just read this editorial on data sharing by Longo and Drazen in the New England Journal of Medicine, a leading journal in the field. What you will find is a desperate attempt to take data hostage and to enforce co-authorships for people who didn’t make any intellectual contributions.
But let’s take it one step at a time. What did Longo and Drazen actually say? They think there are major problems with sharing data fully, timely and openly.
‘My professor demands to be listed as an author on many of my papers’ writes an anonymous scientist in the Guardian.
[T]here’s one instance where it’s acceptable for scientists to lie: when fraudulently claiming authorship of a paper.
Too often, researchers attach their names to reports when they have contributed nothing at all to the work.
The problem gets worse the higher up the academic ladder you go.
I think this is completely true.
The reasons are manifold:
You must have heard about the death of Prof Stefan Grimm, who apparently had been bullied by his departmental line managers at Imperial College London.
In case you have missed it, you can read the whole sad story at DC’ science: Publish and perish at Imperial College London: the death of Stefan Grimm.
ICL will of course claim that bullying is not endemic and this was a very sad but isolated case.
However, rather helpfully, a Mr John T Green has written a paper about the managerial mindset at ICL: Evidence-based decision making in academic research: The “Snowball” effect.
The paper appeared in 2013 in a journal called The Academic Executive Brief – welcome to the Dark Side!
“Do differences between biology and statistics explain some of our diverging attitudes regarding criticism and replication of scientific claims?” asks Andrew Gelman on his blog.
I was not very impressed with the post or the comments it received. Here is what I posted as a response:
Continue reading
The data never speak for themselves; and even Big Data doesn’t change that.
“The business of Big Data, which involves collecting large amounts of data and then searching it for patterns and new revelations, is the result of cheap storage, abundant sensors and new software. It has become a multibillion-dollar industry in less than a decade,”
writes Quentin Hardy at NYtimes.com. Big Data is everywhere, even in medicine. Just have a look at Atul Butte‘s presentation at TEDMED2012:
Clarity and lucidity are key strengths of scientists and writers. Jargon and cliches can make the best paper unreadable. This is why science writer Carl Zimmer keeps an index of banned words his students should avoid.
One of the words on the index is ‘breakthrough,’ which is overused, because the person reporting it doesn’t bother to think about how big the step forward really is. Using such cliches shows sloppy thinking and lack of scrutiny. This is why Zimmer bans ‘breakthrough‘ “unless you are covering Principia Mathematica”, in which case you are fine, regardless of whether you refer to Whitehead and Russell or Newton.
Not only science writers need to avoid cliches and enrich their texts for content – ‘real’ scientists also often use fancy buzz words with far too much levity. Just think of these three (in no particular order) that you can hear in almost every systems biology talk:
Rapid rise, steepest fall! Karl Theodor Maria Nikolaus Johann Jacob Philipp Franz Joseph Sylvester Freiherr von und zu Guttenberg was German minister of defence until March 2011, when his PhD thesis was proven to be riddled with plagiarism.
Emotions ran high this year in Germany.
Scientific B-sides 