Career, Science

You Are Not Working for Me; I Am Working with You

Scientific labs are like black boxes. You rarely get a glimpse how someone else has organised their group, what strategies they use to manage their team and how they keep everyone motivated.

This is why I think the PloS Comp Bio Collection “About My Lab” is a great resource.

The collection ‘About My Lab’ was launched with the mission to share knowledge about lab organization and scientific management. Each Perspective article represents an interview with a Principal Investigator, who shares his or her experience of running a lab by discussing selected topics in an informal and personal style. By creating this collection at PLOS Computational Biology, a journal committed to open knowledge, the collection editors hope to create a dialog through which we all can learn from each other.

I feel very honoured they asked me to contribute and my article just came out yesterday. It’s called `You are not working for me; I am working with you.’

It wasn’t an interview, though. I had to write the whole thing myself. Here are the first few paragraphs, you can read the rest on at the PLOS Comp Bio webpage.

Since 2009, I have led a cancer research group at the University of Cambridge; the current group includes ten scientists (five postdocs, five PhD students). In the following, I will share with you some of the lessons I learned over the years and some of the leadership strategies that work well for me. Key topics will be the integration of new lab members and the communication in the lab (in particular, how to make expectations explicit).

How the Lab Started

One of the papers that impressed me most as a PhD student was Eran Segal’s paper on module networks in yeast [1]. When I prepared to start my own lab, at the end of my postdoc in 2008, I realised there had been almost no follow-up, and certainly nothing in cancer research. What an opportunity, I thought! So I wrote up a series of projects, which could easily have kept two postdocs busy for three years, on how to extend module networks and use them for data integration in cancer genomics. It was a great plan. Then I went to Intelligent Systems in Molecular Biology (ISMB) 2009 and heard Daphne Koller’s keynote. What a shocker—point by point, I could tick things off of my to-do list. Not only had Daphne’s lab thought of all my ideas for module networks, but they had implemented, tested, and improved them, and the papers had already begun to be published [2]. Well, I thought, at least they are not doing this in the field of cancer. But then I saw one of Dana Pe’er’s publications [3], which killed my research program for good. I could have added some marginal improvements, sure, but that wouldn’t have been too exciting. So more than a year into my Principal Investigator (PI) position, I stood there empty-handed, without much of an idea what to do next. I had hit the ground, but I wasn’t running. What rescued me was the people in my group.

Read on at the PLOS Comp Bio webpage.



Inferring tumour evolution 7 – the roots of metastasis

Series on Tumour Evolution

What makes a cancer deadly is not necessarily the growth at the location where it started (the primary tumour) but its spread through the body to other organs and tisses (called metastasis). Better understanding the metastatic process is one of main reasons we are interested in inferring cancer evolution.

Today I would like to summarize and discuss two recent papers on cancer phylogenetics and metastasis. The first paper is the comprehensive review by Naxerova and Jain in Nature Reviews Clinical Oncology titled “Using tumour phylogenetics to identify the roots of metastasis in humans.” The second paper is an Opinion paper by Hong, Shpak and Townsend in Cancer Research  titled “Inferring the origin of metastases from cancer phylogenies.”

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Sustaining reproducibility

Our paper on tumor evolution in ovarian cancer (see here) came with a nice knitR file to reproduce the survival results, which I used as an example in my recent talk about reproducibility (see here).

I thought that was a nice test scenario to see if I could reproduce the results I got more than a year ago.

How reproducible am I?

Downloading the Rnw from the journal webpage (link) was easy, but -of course- it didn’t run through smoothly.

LaTeX failed and there were several R error messages.

The joys and frustrations of reproducibility

First of all, I had linked to a BibTeX file instead of just copying the bibliography in to the Rnw as I should have done.

Second, I ran into problems with the survival analysis, because one of the packages had changed.

rms::survplot() used to allow plotting a survfit object through survplot.survfit() function. However, this function has been deprecated as of version 4.2.

Luckily I found an easy workaround, just use npsurv() instead of survfit().

The updated Rnw is here on my webpage:

Together with a PDF so you can see what the output should look like.

Take-home message for me: Even with a knitR file I did myself, reproducibility is not a one-click thing.

To make reproducibility sustainable I would have to check all published analysis scripts in regular intervals (e.g. once every year or every 6 months). Am I prepared to do this? And for how long?



Inferring tumour evolution – clones, again

Series on Tumor Evolution
How do you know the leaders in your field? Because they get invited by
Nature Medicine and Nature Biotechnology to a fancy place owned by the Volkswagen Foundation and write a report about it. For example, this one in the latest issue of Nature Medicine titled Toward understanding and exploiting tumor heterogeneity.

What did they discuss? Lots of things. But I got stuck already in the very first topic:

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Is there an alternative to ‘Excel is the devil’?

In my last post I shared the slides for my talk  “5 selfish reasons to work reproducibly”.

In my talk I stressed the importance of using scripts and code to make analyses reproducible. Instead of clicking, cutting and pasting as you would have to do in a tool like Excel.

I had also submitted my slides to the F1000 slides collection and after a few days got a very polite email back, asking me to rethink the keywords I had chosen in the submission:

Thank you for your slides submission: “5 selfish reasons to work reproducibly”.

Just a quick note to say that keywords are displayed alongside your presentation and are often how users will find your submission, by searching our site.

With this in mind, we were wondering if you had an alternative to “Excel is the devil” which might be more likely to appear on search results. [my emphasis]

First of all, I am impressed by how serious they take curating slides at F1000.

And, yeah, I might come up with some other keywords, even though I think ‘Excel is the devil’ remains quite accurate.

You can find the slides together with the new keywords (quite boring: Reproducible research, knitr, Sweave, Successful lab, Career advice) here:

Markowetz F.
Five selfish reasons to work reproducibly [v1; not peer reviewed].
F1000Research 2015, 4:207 (slide presentation)
(doi: 10.7490/f1000research.1000179.1)



Five selfish reasons for working reproducibly

And so, my fellow scientists: Ask not what you can do for reproducibility — ask what reproducibility can do for you!

The following is a summary of a talk I gave in my institute and at the Gurdon in Cambridge. My job was to motivate why working reproducibly is a good strategy for ambitious scientists. Right after my talk, Gordon Brown (CRUK CI) and Stephen Eglen (Cambridge DAMTP)  presented tools and case studies of reproducible work.

All materials are on github and below are my slides, thanks to slideshare:

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Slides for my ISMB network SIG keynote

Wohoo! My first set of citable slides.

I submitted the talk I gave at the ISMB 2015 network SIG to F1000. And they made it citable:

Markowetz F.
Functional analysis of interaction networks [v1; not peer reviewed].
F1000Research 2015, 4:221 (slide presentation)
(doi: 10.7490/f1000research.1000198.1)

To make sure that the animations are all visible I duplicated some slides several times and because they contain network plots the file is a massive 43 MB. I need to think of a better way to do this next time …


Books, Science

New Book! SYSTEMS GENETICS by Markowetz and Boutros at Cambridge University Press

The book on Systems Genetics I have edited with Michael Boutros just came out! Wohoo!

You can get a look at the first copies at ISMB this year and I will shamelessly promote it in my talk at the Networks SIG on July 10th.

You can download the first introductory chapter written by Michael and me here.

Here is the promo text for your pleasure and enjoyment:

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Duty Calls, Science

“Superstar professors with massive research groups are bad for science.” I agree.

‘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:

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At the movies: “My career in genomics: evolution “

Former postdoc Roland Schwarz of MEDICC fame has become a movie star.
Or at least the face of computational biology for the Wellcome Genome Campus:

In this film Roland Schwarz talks about his research using computers to model and understand evolution. This is one of a series of films providing a unique insight into different careers in the field of genomics.

Go here or watch it directly:


Career, Science

Forget about your animal friends – how to draft a recommendation letter

Marcia McNutt, Editor-in-Chief of Science, wrote a thoughtful Editorial about recommendation letters:

I noted an overall bias in the language used to describe the male candidates versus some of the female candidates. In some letters, women were described as “friendly,” “kind,” “pleasant,” “humble,” and frequently, “nice.”

[O]ne letter described how the candidate was so good to her elderly mother, yet still enjoyed life, spending time in nature with her husband and her animal friends.

Another letter reflected amazement that the candidate managed to balance so efficiently being a student, a scientist, and a mother.

But isn’t it good being nice, humble and having lots of animal friends?

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A unifying theory of cancer evolution: genomes in context

Finally a review article on cancer evolution that I really enjoyed. Maybe because it’s not a Review but an Opinion piece: “Evolutionary dynamics of carcinogenesis and why targeted therapy does not work” by Gillies, Verduzco and Gatenby (GVG for short).

Extra brownie points for a provocative title.

The first publication on tumor heterogeneity

First of all, GVG extended my knowledge of the history of tumor heterogeneity by citing a paper from 1930:

Ö. Winge, Zytologische Untersuchungen über die Natur maligner Tumoren, Zeitschrift für Zellforschung und Mikroskopische Anatomie, 6. JUNI 1930, Volume 10, Issue 4, pp 683-735,

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Evolution in cancer: Yes! Darwin: No?

Series on Tumor Evolution
Evolution is a fancy word for gradual change. In this general sense, all kinds of things evolve. The universe evolves, societies evolve, finches evolve.

The mechanisms and principles of these three evolutions are all different. For example, the finches change by Darwinian evolution, which is one particular type of evolution based on natural selection: There is diversity in traits between individuals in a population; because of their traits some individuals have more offspring than others; the traits are heritable and can be passed on to offspring. Over time the favorable traits will become dominant in the population – and with them the genotypes underlying them.

This is how it works for finches. How about cancer? Is that developing by Darwinian evolution too? Not so fast, say Sidow and Spies:

The forced application of terms and concepts from organismal population genetics can distract from the fundamental simplicity of cancer evolution,

write Sidow and Spies in their review ‘Concepts in solid tumor evolution‘ (TiG 2015) and they plan to set the record straight.

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Hypnotizing the reader into accepting the authors’ conclusions

I just had a quick look at last week’s Nature Genetics Editorial Cause, correlation, conjecture.

[W]e have been struck again by the amount of repetition of claims and arguments in most research articles.

The main claims of the paper are detailed in the title, abstract, introduction, results, figures and discussion as well as in the methods as if to hypnotize the reader into accepting the authors’ conclusions. [My emphasis]

Repetitiveness shows lack of confidence. One more reason to remove it.

And the Nat Gen editors even mention a tool to better structure a paper:

Our recommendation in planning a research paper is to lay out the claims together with the supporting evidence and methods in a three-column table. The rows follow one another logically as one experiment or analysis follows necessarily from its predecessor.

It is unfortunate that the short article doesn’t contain an example of such a table. But I like the idea and might just try it next time we write a paper.


Cause, correlation, conjecture. Nature Genetics 47, 305(2015) doi:10.1038/ng.3271