Our approach to replication in computational science

I'm pretty proud of our most recently posted paper, which is on a sequence analysis concept we call digital normalization. I think the paper is pretty kick-ass, but so is the way in which we're approaching replication. This blog post is about the latter.

(Quick note re "replication" vs "reproduction": The distinction between replication and reproducibility is, from what I understand, that "replicable" means "other people get exactly the same results when doing exactly the same thing", while "reproducible" means "something similar happens in other people's hands". The latter is far stronger, in general, because it indicates that your results are not merely some quirk of your setup and may actually be right.)

So what did we do to make this paper extra super replicable?

If you go to the paper Web site, you'll find:

  • a link to the paper itself, in preprint form, stored at the arXiv site;
  • a tutorial for running the software on a Linux machine hosted in the Amazon cloud;
  • a git repository for the software itself (hosted on github);
  • a git repository for the LaTeX paper and analysis scripts (also hosted on github), including an ipython notebook for generating the figures (more about that in my next blog post);
  • instructions on how to start up an EC2 cloud instance, install the software and paper pipeline, and build most of the analyses and all of the figures from scratch;
  • the data necessary to run the pipeline;
  • some of the output data discussed in the paper.

(Whew, it makes me a little tired just to type all that...)

What this means is that you can regenerate substantial amounts (but not all) of the data and analyses underlying the paper from scratch, all on your own, on a machine that you can rent for something like 50 cents an hour. (It'll cost you about \$4 -- 8 hours of CPU -- to re-run everything, plus some incidental costs for things like downloads.)

Not only can you do this, but if you try it, it will actually work. I've done my best to make sure the darn thing works, and this is the actual pipeline we ourselves ran to produce the figures in the paper. All the data is there, and all of the code used to process the data, analyze the results, and produce the figures is also there. In version control.

When you combine that with the ability to run this on a specific EC2 instance -- a combination of a frozen virtual machine installation and a specific set of hardware -- I feel pretty confident that at least this component of our paper is something that can be replicated.

A few thoughts on replicability, and effort

Why did I go to all this trouble??

Wasn't it a lot of work?

Well, interestingly enough, it wasn't that much work. I already use version control for everything, including paper text; posting it all to github was a matter of about three commands.

Writing the code, analysis scripts, and paper was an immense amount of work. But I had to do that anyway.

The most extra effort I put in was making sure that the big data files were available. I didn't want to add the the 2gb E. coli resequencing data set to git, for example. So I ended up tarballing those files sticking them on S3.

The Makefile and analysis scripts are ugly, but suffice to remake everything from scratch; they were already needed to make the paper, so in order to post them all I had to do was put in a teensy bit of effort to remove some unintentional dependencies.

The ipython notebook used to generate the figures (again -- next blog post) was probably the most effort, because I had to learn how to use it, which took about 20 minutes. But it was one of the smoothest transitions into using a new tool I've ever experienced in my ~25 years of coding.

Overall, it wasn't that much extra effort on my part.

Why bother in the first place??

The first and shortest answer is, because I could, and because I believe in replication and reproducibility, and wanted to see how tough it was to actually do something like this. (It's a good deal above and beyond what most bioinformaticians do.)

Perhaps the strongest reason is that our group has been bitten a lot in recent months by irreplicable results. I won't name names, but several Science and PNAS and PLoS One papers of interest to us turned out to be basically impossible for us to replicate. And, since we are engaged in developing new computational methods that must be compared to previous work, an inability to regenerate exactly the results in those other papers meant we had to work harder than we should have, simply to reproduce what they'd done.

A number of these problems came from people discarding large data sets after publishing, under the mistaken belief that their submission to the Short Read Archive could be used to regenerate their results. (Often SRA submissions are unfiltered, and no one keeps the filtering parameters around...right?) In some cases, I got the right data sets from the authors and could replicate (kudos to Brian Haas of Trinity for this!), but in most cases, ixnay on the eplicationre.

Then there were the cases where authors clearly were simply being bad computational scientists. My favorite example is a very high profile paper (coauthored by someone I admire greatly), in which the script they sent to us -- a script necessary for the initial analyses -- had a syntax error in it. In that case, we were fairly sure that the authors weren't sending us the script they'd actually used... (It was Perl, so admittedly it's hard to tell a syntax error from legitimate code, but even the Perl interpreter was choking on this.)

(A few replication problems came from people using closed or unpublished software, or being hand-wavy about the parameters they used, or using version X of some Web-hosted pipeline for which only version Y was now available. Clearly these are long-term issues that need to be discussed with respect to replication in comp. bio., but that's another topic.)

Thus, my group has wasted a lot of time replicating other people's work. I wanted to avoid making other people go through that.

A third reason is that I really, really, really want to make it easy for people to pick up this tool and use it. Digital normalization is super ultra awesome and I want as little as possible to stand in the way of others using it. So there's a strong element of self-interest in doing things this way, and I hope it makes diginorm more useful. (I know about a dozen people that have already tried it out in the week or so since I made the paper available, which is pretty cool. But citations will tell.)

What use is replication?

Way back when, Jim Graham politely schooled me in the true meaning of reproducibility, as opposed to replication. He was about 2/3 right, but then he went a bit too far and said

But let's drop the idea that I'm going to take your data and your code and "reproduce" your result. I'm not. First, I've got my own work to do. More importantly, the odds are that nobody will be any wiser when I'm done."

Well, let's take a look at that concern, shall we?

With the benefit of about two years of further practice, I can tell you this is a dangerously wrong way to think, at least in the field of bioinformatics. My objections hinge on a few points:

First, based on our experiences so far, I'd be surprised if the authors themselves could replicate their own computational results -- too many files and parameters are missing. We call that "bad science".

Second, odds are, the senior professor has little or no detailed understanding of what bioinformatic steps were taken in processing the data, and moreover is uninterested in the details; that's why they're not in the Methods. Why is that a problem? Because the odds are quite good that many biological analyses hinge critically on such points. So the peer reviewers and the community at large need to be able to evaluate them (see this RNA editing kerfuffle for an excellent example of reviewer fail). Yet most bioinformatic pipelines are so terribly described that even with some WAG I can't figure out what, roughly speaking, is going on. I certainly couldn't replicate it, and generating specific critiques is quite difficult in that kind of circumstance.

Parenthetically, Graham does refer to the climate sciences struggles with reproducibility and replication. If only they put the same effort into replication and data archiving they did into arguing with climate change deniers...

Third, Graham may be guilty of physics chauvinism (just like I'm almost certainly guilty of bioinformatics chauvinism...) Physics and biology are quite different: in physics, you often have a theoretical framework to go by, and results should at least roughly adhere to that or else they are considered guilty until proven innocent. In biology, we usually have no good idea of what we're expecting to see, and often we're looking at a system for the very first time. In that environment, I think it's important to make the underlying computation WAY more solid than you would demand in physics (see RNA editing above).

As Narayan Desai pointed out to me (following which I then put it in my PyCon talk (slide 5)), physics and biology are quite different in the way data is generated and analyzed. There's fewer sources of data generation in physics, there's more of a computational culture, and there's more theory. Having worked with physicists for much of my scientific life (and having published a number of papers with physicists) I can tell you that replication is certainly a big problem over there, but the consequences don't seem as big -- eventually the differences between theory and computation will be worked out, because they're far more noticeable when you have theory, like in physics. Not so in biology.

Fourth, a renewed emphasis on computational methods (and therefore on replicability of computational results) is a natural part of the transition to Big Data biology. The quality of analysis methods matters A LOT when you are dealing with massive data sets with weak signals and many systematic biases. (I'll write about this more later.)

Fifth, and probably most significant from a practical perspective, Graham misses the point of reuse. In bioinformatics, it behooves us to reuse proven (aka published) tools -- at least we know they worked for someone, at least once, which is not usually the case for newly written software. I don't pretend that it's the responsibility of people to write awesome reusable tools for every paper, but sure as heck I should expect to be able to run them on some combination of hardware and software. Often that's not the case, which means I get to reinvent the wheel (yay...) even when I'm doing the same stupid thing the last five pubs did.

For our paper, khmer and screed should be quite reusable. The analysis pipeline for the paper? It's not that great. But at least you can run it, and potentially steal code from it, too.

When I was talking to a colleague about the diginorm paper, he said something jokingly: "wow, you're making it way too easy for people!" -- presumably he meant it would be way to easy for people to criticize or otherwise complain about the specific way we're doing things. Then, a day or two later he said, "hmm, but now that I think of it, no one ever uses the software we publish, and you seem to have had better luck with that..." -- recognizing that if you are barely able to run your own software, perhaps others might find it even more difficult.

Heck, the diginorm paper itself would have been far harder to write without the data sets from the Trinity paper and the Velvet-SC paper. Having those nice, fresh, well-analyzed data sets already at hand was fantastic. Being able to run Trinity and reproduce their results was wonderful.

There's a saying in software engineering: "one of the main people you should be programming for is yourself, in 6 months." That's also true in science -- I'm sure I won't remember the finer details of the diginorm paper analysis in 2 years -- but I can always go look into version control. More importantly, new graduate students can go look and really see what's going on. (And I can use it for teaching, too.) And so can other people working with me. So there's a lot of utility in simply nailing everything down and making it runnable.

Replication is by no means sufficient for good science. But I'll be more impressed by the argument that "replication isn't all that important" when I see lack of replication as the exception rather than the rule. Replication is essential, and good, and useful. I long for the day when it's not interesting, because it's so standard. In the meantime I would argue that it certainly doesn't do any harm to emphasize it.

(Note that I really appreciate Jim Graham's commentary, as I think he is at worst usefully wrong on these points, and substantially correct in many ways. I'm just picking on him because he wrote it all down in one place for me to link to, and chose to use the word 'sic' when reproducing my spelling mistake. Low blow ;)

The future

I don't pretend to have all, or even many, of the answers; I just like to think about what form they might take.

I don't want to argue that this approach is a panacea or a high-quality template for others to use, inside or out of bioinformatics. For one thing, I haven't automated some of the analyses in the paper; it's just too much work for too little benefit at this point. (Trust me, they're easy to reproduce... :). For another, our paper used a fairly small amount of data overall; only a few dozen gigabytes all told. This makes it easy to post the data for others to use later on. Several of our next few papers will involve over a half terabyte of raw data, plus several hundred gb of ancillary and intermediate results; no idea what we'll do for them.

Diginorm is also a somewhat strange bioinformatics paper. We just analyzed other people's data sets (an approach which for some reason isn't in favor in high impact bioinformatics, probably because high impact journal subs are primarily reviewed by biologists who want to see cool new data that we don't understand, not boring old data that we don't understand). There's no way we can or should argue that biological replicates done in a different lab should replicate the results; that's where reproducibility becomes important.

But I would like it if people considered this approach (or some other approach) to making their analyses replicable. I don't mind people rejecting good approaches because they don't fit; to each their own. But this kind of limited enabling of replication isn't that difficult, frankly, and even if it were, it has plenty of upsides. It's definitely not irrelevant to the practice of science -- I would challenge anyone to try to make that claim in good faith.


p.s. I think I have to refer to this cancer results not reproducible paper somewhere. Done.

Legacy Comments

Posted by Jorgen on 2012-04-02 at 05:24.

Great post. It's encouraging to see that you care about
reproducibility in bioinformatics. Now we need to get the reviewers on
our side: A github page for every paper and all figures and tables
reproducible with a single 'make' :)    PS: The pyCon-slide link is
actually pointing to the cancer-not-reproducible link.

Posted by Ben on 2012-04-02 at 09:28.

I have struggled with reproducing (or replicating) others'
computational work, as well. This is a fantastic post! I wish more
computational biologists thought like you do!

Posted by Titus Brown on 2012-04-02 at 10:42.

Thanks, Jorgen -- updated.  Thanks, Ben!

Posted by David Escott on 2012-04-02 at 16:50.

Most institutions have required courses in intro statistics for field
X, but I feel that what would be more useful is a required course in
version control. The fact that you even know how to use git (sadly)
puts you well beyond what is known in a wide array of fields. Keep up
the good work.

Posted by Michael Kellen on 2012-04-03 at 01:10.

Great to see someone who is so thoughtful and complete in their work.
Have spent some time looking at GitHub myself as we look to develop a
similar system targeted at the data analysis space <a

Posted by Raphael on 2012-04-03 at 07:39.

Great work, great post. I'm looking forward to the ipython notebook!

Posted by Steve P on 2012-04-04 at 10:47.

Nice post. I've been working on a replication pipeline for a couple
papers I'm writing. Mine is just a bunch of bash scripts that invoke
Python and R scripts. I guess I wasn't too worried about platform
issues, such that I would need to wrap it in a virtual machine, but
maybe I should be. I definitely agree with your points about how this
exposes you to more scrutiny, because people can actually pick apart
the precise steps you took, and you sometimes have to make arbitrary
decisions that may have a substantial impact on your results.

Posted by Kris on 2012-04-04 at 17:34.

This is great stuff.  In some venues it might be appropriate to
require or at least strongly suggest this material be provided during
peer review.  Sadly, this would really increase the level of effort
required by a reviewer -- but it would also likely really increase the
quality of the review!

Posted by Anthony Scopatz on 2012-04-07 at 17:12.

Titus, how important is the VM / EC2 instance that you are using is to
the replication - reproducibility distinction?  Given the definition
of replication here it seems that being able to run on the same
hardware and in the same computing environment is critical.    For
example, obtaining the same or similar results using different
versions of the Linux kernel (say v3.3.1 vs v2.6.35) would be
reproduction, not replication.  Similarly, if you use different
chipsets this is reproduction.    With replication being narrowly
defined as having full equality over the hardware and software stack,
reproducibility certainly is the harder problem.  Knowing which of the
hundreds of thousands of extrinsic variables actually may matter to a
result is a hard problem in general.  However, this is where testing
&amp; debugging come in and ensure that a code works on many classes
of systems.    Note that I just trying to get a handle on when it is
appropriate to use these two terms.  I am not criticizing the good
&amp; noble pursuit of replication.  I dream of the day where
replication, testing, v&amp;v, and uq are just done as a matter of
course in the sciences.      tl;dr: replication is when you have full
control over the hardware and software stack and reproduction is
anything else where the tests still pass and you obtain the same basic
results, right?

Posted by Titus Brown on 2012-04-07 at 21:06.

Hey Anthony... yeah, good questions.  No idea how to respond.  I'm
sure there's a fuzzy boundary between replication and reproduction,
but not sure how to delineate.

Posted by steven mosher on 2012-04-08 at 17:12.

Great post..

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