Simply Statistics


A disappointing response from @NatureMagazine about folks with statistical skills

Last week I linked to an ad for a Data Editor position at Nature Magazine. I was super excited that Nature was recognizing data as an important growth area. But the ad doesn’t mention anything about statistical analysis skills; it focuses exclusively on data management expertise. As I pointed out in the earlier post, managing data is only half the equation - figuring out what to do with the data is the other half. The second half requires knowledge of statistics.

The folks over at Nature responded to our post on Twitter:

 it’s unrealistic to think this editor (or anyone) could do what you suggest. Curation & accessibility are key. ^ng

I disagree with this statement for the following reasons:

1. Is it really unrealistic to think someone could have data management and statistical expertise? Pick your favorite data scientist and you would have someone with those skills. Most students coming out of computer science, computational biology, bioinformatics, or statistical genomics programs would have a blend of those two skills in some proportion. 

But maybe the problem is this:

Applicants must have a PhD in the biological sciences

It is possible that there are few PhDs in the biological sciences who know both statistics and data management (although that is probably changing). But most computational biologists have a pretty good knowledge of biology and a very good knowledge of data - both managing and analyzing. If you are hiring a data editor, this might be the target audience. I’d replace PhD in the biological science in the ad with, knowledge of biology,statistics, data analysis, and data visualization. There would be plenty of folks with those qualifications.

2. The response mentions curation, which is a critical issue. But good curation requires knowledge of two things: (i) the biological or scientific problem and (ii) how and in what way the data will be analyzed and used by researchers. As the Duke scandal made clear, a statistician with technological and biological knowledge running through a data analysis will identify many critical issues in data curation that would be missed by someone who doesn’t actually analyze data. 

3. The response says that “Curation and accessibility” are key. I agree that they are part of the key. It is critical that data can be properly accessed by researchers to perform new analyses, verify results in papers, and discover new results. But if the goal is to ensure the quality of science being published in Nature (the role of an editor) curation and accessibility are not enough. The editor should be able to evaluate statistical methods described in papers to identify potential flaws, or to rerun code and make sure that it performs the same/sensible analyses. A bad analysis that is reproducible will be discovered more quickly, but it is still a bad analysis. 

To be fair, I don’t think that Nature is the only organization that is missing the value of statistical skill in hiring data positions. It seems like many organizations are still just searching for folks who can handle/process the massive data sets being generated. But if they want to make accurate and informed decisions, statistical knowledge needs to be at the top of their list of qualifications.  


Sunday data/statistics link roundup (4/29)

  1. Nature genetics has an editorial on the Mayo and Myriad cases. I agree with this bit: “In our opinion, it is not new judgments or legislation that are needed but more innovation. In the era of whole-genome sequencing of highly variable genomes, it is increasingly hard to justify exclusive ownership of particularly useful parts of the genome, and method claims must be more carefully described.” Via Andrew J.
  2. One of Tech Review’s 10 emerging technologies from a February 2003 article? Data mining. I think doing interesting things with data has probably always been a hot topic, it just gets press in cycles. Via Aleks J. 
  3. An infographic in the New York Times compares the profits and taxes of Apple over time, here is an explanation of how they do it. (Via Tim O.)
  4. Saw this tweet via Joe B. I’m not sure if the frequentists or the Bayesians are winning, but it seems to me that the battle no longer matters to my generation of statisticians - there are too many data sets to analyze, better to just use what works!
  5. Statistical and computational algorithms that write news stories. Simply Statistics remains 100% human written (for now). 
  6. The 5 most critical statistical concepts. 

People in positions of power that don't understand statistics are a big problem for genomics

I finally got around to reading the IOM report on translational omics and it is very good. The report lays out problems with current practices and how these led to undesired results such as the now infamous Duke trials and the growth in retractions in the scientific literature. Specific recommendations are provided related to reproducibility and validation. I expect the report will improve things. Although I think bigger improvements will come as a result of retirements.

In general, I think the field of genomics  (a label that is used quite broadly) is producing great discoveries and I strongly believe we are just getting started. But we can’t help but notice that retraction and questionable findings are particularly high in this field. In my view most of the problems we are currently suffering stem from the fact that a substantial number of the people with positions of power do not understand statistics and have no experience with computing. Nevin’s biggest mistake was not admitting to himself that he did not understand what Baggerly and Coombes were saying. The lack of reproducibility just exacerbated the problem. The same is true for the editors that rejected the letters written by this pair in their effort to expose a serious problem - a problem that was obvious to all the statistics savvy biologists I talked to.

Unfortunately Nevins is not the only head of a large genomics lab that does not understand basic statistical principles and has no programming/data-management experience. So how do people without the necessary statistical and computing skills to be considered experts in genomics become leaders of the field?  I think this is due to the speed at which Biology changed from a data poor discipline to a data intensive ones. For example, before microarrays, the analysis of gene expression data amounted to spotting black dots on a piece of paper (see Figure A below). In the mid 90s this suddenly changed to sifting through tens of thousands of numbers (see Figure B).

Note that typically, statistics is not a requirement of the Biology graduate programs associated with genomics. At Hopkins neither of the two major programs (CMM and BCMB) require it. And this is expected, since outside of genomics one can do great  Biology without quantitative skills and for most of the 20th century most Biology was like this. So when the genomics revolution first arrived, the great majority of powerful Biology lab heads had no statistical training whatsoever. Nonetheless, a few of these decided to delve into this “sexy” new field and using their copious resources were able to perform some of the first big experiments. Similarly, Biology journals that were not equipped to judge the data analytic component of genomics papers were eager to publish papers in this field, a fact that further compounded the problem.

But I as I mentioned above, in general, the field of genomics is producing wonderful results. Several lab heads did have statistics and computational expertise, while others formed strong partnerships with quantitative types. Here I should mentioned that for these partnerships to be successful the statisticians also needed to expand their knowledge base. The quantitative half of the partnership needs to be biology and technology savvy or they too can make mistakes that lead to retractions

Nevertheless, the field is riddled with problems; enough to prompt an IOM report. But although the present is somewhat grim, I am optimistic about the future. The new generation of biologists leading the genomics field are clearly more knowledgeable and appreciative about statistics and computing than the previous ones. Natural selection helps, as these new investigators can’t rely on pre-genomics-revolution accomplishments and those that do not posses these skills are simply outperformed by those that do. I am also optimistic because biology graduate programs are starting to incorporate statistics and computation into their curricula. For example, as of last year, our Human Genetics program requires our Biostats 615-616 course


Nature is hiring a data will they make sense of the data?

It looks like the journal Nature is hiring a Chief Data Editor (link via Hilary M.). It looks like the primary purpose of this editor is to develop tools for collecting, curating, and distributing data with the goal of improving reproducible research.

The main duties of the editor, as described by the ad are: 

Nature Publishing Group is looking for a Chief Editor to develop a product aimed at making research data more available, discoverable and interpretable.

The ad also mentions having an eye for commercial potential; I wonder if this move was motivated by companies like figshare who are already providing a reproducible data service. I haven’t used figshare, but the early reports from friends who have are that it is great. 

The thing that bothered me about the ad is that there is a strong focus on data collection/storage/management but absolutely no mention of the second component of the data science problem: making sense of the data. To make sense of piles of data requires training in applied statistics (called by whatever name you like best). The ad doesn’t mention any such qualifications. 

Even if the goal of the position is just to build a competitor to figshare, it seems like a good idea for the person collecting the data to have some idea of what researchers are going to do with it. When dealing with data, those researchers will frequently be statisticians by one name or another. 

Bottom line: I’m stoked Nature is recognizing the importance of data in this very prominent way. But I wish they’d realize that a data revolution also requires a revolution in statistics. 


How do I know if my figure is too complicated?

One of the key things every statistician needs to learn is how to create informative figures and graphs. Sometimes, it is easy to use off-the-shelf plots like barplots, histograms, or if one is truly desperate a pie-chart

But sometimes the information you are trying to communicate requires the development of a new graphic. I am currently working on a project with a graduate student where the standard illustration are Venn Diagrams - including complicated Venn Diagrams with 5 or 10 circles. 

As we were thinking about different ways of illustrating our data, I started thinking about what are the key qualities of a graphic and how do I know if it is too complicated. I realized that:

  1. Ideally just looking at the graphic one can intuitively understand what is going on, but sometimes for more technical/involved displays this isn’t possible
  2. Alternatively, I think a good plot should be able to be explained in 2 sentences or less. I think that is true for pretty much every plot I use regularly. 
  3. That isn’t including describing what different colors/sizes/shapes specifically represent in any particular version of the graphic. 

I feel like there is probably something to this in the Grammar of Graphics or in some of William Cleveland’s work. But this is one of the first times I’ve come up with a case where a new, generalizable, type of graph needs to be developed. 


On the future of personalized medicine

Jeff Leek, Reeves Anderson, and I recently wrote a correspondence to Nature (subscription req.) regarding the Supreme Court decision in Mayo v. Prometheus and the recent Institute of Medicine report related to the Duke Clinical Trials Saga

The basic gist of the correspondence is that the IOM report stresses the need for openness in the process of developing ‘omics based tests, but the Court decision suggests that patent protection will not be available to protect those details. So how will the future of personalized medicine look? There is a much larger, more general, discussion that could be had about patents in this arena and we do not get into that here (hey, we had to squeeze it into 300 words). But it seems that if biotech companies cannot make money from patented algorithms, then they will have to find a new avenue. 

Here are some slides from a recent lecture I gave outlining some of the ideas and providing some background.


Sunday data/statistics link roundup (4/22)

  1. Now we know who is to blame for the pie chart. I had no idea it had been around, straining our ability to compare relative areas, since 1801. However, the same guy (William Playfair) apparently also invented the bar chart. So he wouldn’t be totally shunned by statisticians. (via Leonid K.)
  2. A nice article in the Guardian about the current group of scientists that are boycotting Elsevier. I have to agree with the quote that leads the article, “All professions are conspiracies against the laity.” On the other hand, I agree with Rafa that academics are partially to blame for buying into the closed access hegemony. I think more than a boycott of a single publisher is needed; we need a change in culture. (first link also via Leonid K)
  3. A blog post on how to add a transparent image layer to a plot. For some reason, I have wanted to do this several times over the last couple of weeks, so the serendipity of seeing it on R Bloggers merited a mention. 
  4. I agree the Earth Institute needs a better graphics advisor. (via Andrew G.)
  5. A great article on why multiple choice tests are used - they are an easy way to collect data on education. But that doesn’t mean they are the right data. This reminds me of the Tukey quote: “The data may not contain the answer. The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data”. It seems to me if you wanted to have a major positive impact on education right now, the best way would be to develop a new experimental design that collects the kind of data that really demonstrates mastery of reading/math/critical thinking. 
  6. Finally, a bit of a bleg…what is the best way to do the SVD of a huge (think 1e6 x 1e6), sparse matrix in R? Preferably without loading the whole thing into memory…

Replication, psychology, and big science

Reproducibility has been a hot topic for the last several years among computational scientists. A study is reproducible if there is a specific set of computational functions/analyses (usually specified in terms of code) that exactly reproduce all of the numbers in a published paper from raw data. It is now recognized that a critical component of the scientific process is that data analyses can be reproduced. This point has been driven home particularly for personalized medicine applications, where irreproducible results can lead to delays in evaluating new procedures that affect patients’ health. 

But just because a study is reproducible does not mean that it is replicable. Replicability is stronger than reproducibility. A study is only replicable if you perform the exact same experiment (at least) twice, collect data in the same way both times, perform the same data analysis, and arrive at the same conclusions. The difference with reproducibility is that to achieve replicability, you have to perform the experiment and collect the data again. This of course introduces all sorts of new potential sources of error in your experiment (new scientists, new materials, new lab, new thinking, different settings on the machines, etc.)

Replicability is getting a lot of attention recently in psychology due to some high-profile studies that did not replicate. First, there was the highly-cited experiment that failed to replicate, leading to a show down between the author of the original experiment and the replicators. Now there is a psychology project that allows researchers to post the results of replications of experiments - whether they succeeded or failed. Finally, the Reproducibility Project, probably better termed the Replicability Project, seeks to replicate the results of every experiment in the journals Psychological Science, the Journal of Personality and Social Psychology,or the Journal of Experimental Psychology: Learning, Memory, and Cognition in the year 2008.

Replicability raises important issues for “big science” projects, ranging from genomics (The Thousand Genomes Project) to physics (The Large Hadron Collider). These experiments are too big and costly to actually replicate. So how do we know the results of these experiments aren’t just errors, that upon replication (if we could do it) would not show up again? Maybe smaller scale replications of sub-projects could be used to help convince us of discoveries in these big projects?

In the meantime, I love the idea that replication is getting the credit it deserves (at least in psychology). The incentives in science often only credit the first person to an idea, not the long tail of folks who replicate the results. For example, replications of experiments are often not considered interesting enough to publish. Maybe these new projects will start to change some of the perverse academic incentives.


Sunday data/statistics link roundup (4/15)

  1. Incredibly cook, dynamic real-time maps of wind patterns in the United States. (Via Flowing Data)
  2. A d3.js coding tool that updates automatically as you update the code. This is going to be really useful for beginners trying to learn about D3. Real time coding (Via Flowing Data)
  3. An interesting blog post describing why the winning algorithm in the Netflix prize hasn’t actually been implemented! It looks like it was too much of an engineering hassle. I wonder if this will make others think twice before offering big sums for prizes like this. Unless the real value is advertising…(via Chris V.)
  4. An article about a group at USC that plans to collect all the information from apps that measure heart beats. Their project is called everyheartbeat. I think this is a little bit pre-mature, given the technology, but certainly the quantified self field is heating up. I wonder how long until the target audience for these sorts of projects isn’t just wealthy young technofiles? 
  5. A really good deconstruction of a recent paper suggesting that the mood on Twitter could be used to game the stock market. The author illustrates several major statistical flaws, including not correcting for multiple testing, an implausible statistical model, and not using a big enough training set. The scary thing is apparently a hedge fund is teaming up with this group of academics to try to implement their approach. I wouldn’t put my money anywhere they can get their hands on it. This is just one more in the accelerating line of results that illustrate the critical need for statistical literacy both among scientists and in the general public.