Monthly Archives: February 2017

You may have never heard of a bioinformatician but there is a big demand for them.

Today while scanning twitter I came across two posts relating to the demand for data science/analytical/programming jobs in both academia and industry.

The first tweet was from the Royal Society and Royal Statistical Society promoting a report on the need for STEM (science,  technology, engineering and maths) skills in the workforce. It is the product of a conference which brought together academics, the Government’s Chief Scientific Adviser and senior representatives from BT, Amazon, Jaguar Land Rover all united by their need for computer and numeracy literate graduates. They estimate that up to 58,000 data science jobs a year are created each year, and there are a large number of these positions which do not get filled because there is a lack of suitable candidates applying.  In industry there is demand to model data to make predictions and decisions on what trends to follow and demand to visualize this data in a way that allows those without such strong numerical skills to make sense of it. They require employees who can communicate effectively what they are doing and think creatively about what further information they can get out of the data that will improve the commercial aspects of the business. It is a worthwhile read for anyone wondering where their mathematics or computer science training might take them.

The second was a tweet from Barefoot Computing stating that in the UK we are losing £63bn from GDP as we don’t have the appropriate technical or digital skill. I don’t know where the statistic comes from, but Barefoot are using it to encourage confidence and enthusiasm in the teaching of the Primary School Computer Science curriculum which is their underlying ethos.

Both of these reiterated to me the  demand there is and will be, in a wide variety of disciplines, for individuals who have strong mathematical, or computer science skill sets. So if you are considering your career options or know someone who is, encourage them to pursue a mathematics or computer science degree as this demand will just keep on growing.


Diversity brings value; how might we get there?

Below is a copy of a post I wrote for the Software Sustainability Institute.

This post summarises a discussion with Lawrence Hudson, Roberto Murcio, Penny Andrew and Robin Long as part of the Fellow Selection Day 2017.

The question of how to improve diversity is suitably broad and vague to initially induce silence in a group, but eventually, true to its name, it promotes a wide-ranging discussion. Sometimes the task is divided up to target particular under-represented groups, as it starts to become a bit of a minefield to develop a scheme that improves diversity in general. What opens the door to some parts of society can simultaneously close the doors to others. Hackathon events are a common and successful method of attracting young people to computer science; however, if they take place over the weekend and are marketed as providing beer and pizza for sustenance, you start to exclude anyone with caring responsibilities or discourage anyone who doesn’t drink.

Before we can think about trying to improve diversity, it is helpful to consider what exactly do we mean and what are the benefits. It is easy to see how a varied workforce can lead to a larger pool of ideas, skills, and experience, as well as a more harmonious environment where differences are embraced, minimising direct comparisons and competition between colleagues. It can also lead to a broader outreach, either exposing your product or brand, attracting new audiences, or inspiring the next generation. Diversity is often quantified in demographics (gender, age, religion, sexual orientation etc.); however, in a working environment it should also include background or previous experience. Your team may be culturally diverse but if you have all got the same degree qualification from the same institution trained in the same school of thought, where are the new ideas going to come from?

To improve diversity, it is important to recognise where the variety is lost. Was there a great selection of applicants from different backgrounds that got filtered out before the interview stage? If you can identify which factors caused the potentially diverse new team members to be excluded from consideration, this can be used to formulate more open criteria. For example in a university, ranking candidates on number of publications tends to favour men over women, focusing on quality over quantity may prevent this bias. With this in mind, developing a range of metrics of equal merit rather than focusing on a single criterion will also favour a broader range of applicants. This may mean moving away from the standard template for job descriptions which requires some time and effort on the part of the employer, but using a structure that allows potential employees to be creative with how they might meet the criteria creates opportunities for those with less traditional career paths.

Institutions can play their part by celebrating and promoting successes at all levels, as purely focusing on the achievements of the most senior employees often reinforces existing typecasts. In academia, there is a lot of truth in the stereotype of Professors as white, male and middle aged, so only covering the publications, media appearances and grant money brought in by these individuals may deter anyone who physiologically cannot aspire to this demographic. Alternatively publicising both work (software developed, new recruits, promotions) and personal achievements (charity events, sporting triumphs or bake sales) of all members of staff starts to showcase the variety underlying the workforce and may inspire a broader scope of applicants. Active involvement in the wider community raises the profile and generates positive feelings towards an organisation.  Having a creative recruitment and outreach strategy with roles such as community managers, public engagement officers and more tailored positions such as artists in residence can promote a welcoming environment and reach previously untapped employment streams.

Employers need to be open and flexible to new ways of working in order to appeal to a more varied pool of applicants. While many employers recognise the value of diversity and would always embrace a broad range of applicants to choose from, when it comes to the final decision, it can take a brave individual to select a candidate that differs from their usual employee. Pressure to hit the ground running creates barriers for individuals with great potential but who require a little more training or time to adjust to a new environment.

With increasing variety in backgrounds, training opportunities and career paths, the diversity we know will benefit us is continually expanding in the working population. A more open, flexible recruitment strategy will provide the opportunities for those looking for a change, both for employers and employees. However, diversity cannot be enforced. For the benefits to be realised, it needs to be an organic experience where the individuals involved recognise its value.

Bringing together, statistics, genetics and genealogy

In this post I want to highlight a recent genetic study published this week in Nature Communications which uses genetic data to characterize the current population of the US and understand how it came to be using databases of family history.

Their starting point was a very large genetic data set of 774,516 people currently residing in the US, the majority of which were also born there, with measurements at 709,358 different genetic positions.

They compared the genetic profiles of all pairs of individuals to identify regions of the genome (of a certain size) shared by both individuals, consistent with those two individuals having a common ancestor. It is important to note, that this is very unlikely to be the case between two randomly selected or even two distantly related individuals. Therefore this study was only possible because they had accumulated such a large genetic data set, meaning they had enough pairs of individuals with such a genomic region in common to make any inferences. Based on this information they produce a plot of US states where distance between points represents the similarity in common ancestry between individuals born in those states, which closely resembles a geographical map of the US. What it means is that, in general, the closer together two individuals live, the closer their ancestry is likely to be. This isn’t hard to believe,  and has been shown before, for example, similar studies in European populations have produced similar figures in the past.

The aim of the study was to divide the sample up into groups, referred to as clusters, of individuals whose genetic data implied common ancestry and which represented the substructure of the US population. What is perhaps novel to this study, is the inclusion of information from participants relating to when and where their relatives were born to interpret the origins and migratory patterns of each cluster. All of which is then discussed in the context of known immigration and migration patterns in recent times (~last 500 years).

A few things struck me about this article. Firstly, the data was taken from a genetic genealogy service AncestryDNA, who use a saliva sample to genetically profile and generate statistics on customer’s ancestry. Their analytical sample size was 774,516 individuals of US origin who provided consent for their data to be included in genetics research demonstrating potentially how interested the general population is in the information that their genome harnesses. What’s more these individuals are also keen for it to be used to improve our understanding of how genetics influences health and disease.

Secondly, the authors used network analysis to identify their clusters of individuals with common ancestry. The article is littered with mathematical terminology, “principal components”,  “weight function”, “hierarchical clustering”, “ spectral dimensionality reduction technique”, demonstrating not only the utility of statistics in genetics but the additional applications of this to supplementing our knowledge of modern history.

Thirdly, they make use of a range of large data sets (multiple genetic data sets and genealogy databases). This is increasingly necessary in genetics research in order to interpret findings and draw conclusions, making this a nice demonstration of how to think about incorporating additional sources of information (like a historian would) in order to contextualize your results.

Finally, if nothing else, this research serves as a timely reminder of the broad roots and origins of the current residents of the USA and how they came to be there.

Let’s test all the genes!

In this blog post (and others to follow) I want to give some examples of how statistics and statisticians have helped advance genetics research.

Most genetic studies these days consider and measure variation across all 20,000 human genes simultaneously. This is a great advance as it means we can forgot all the old biological theories we had based any previous research around and as yet not found any concrete support for. This is the basis of a genome-wide association study, often shortened to GWAS. GWAS are often referred to as a hypothesis-free approach. Technically, they are not completely hypothesis-free, as to do any statistics we need a hypothesis to test. They work on the hypothesis is that the disease of interest has genetic risk factors, however, we don’t need to have idea which gene or genes may be involved before we start. This means we may find a completely new gene or novel biological process which could revolutionize our understanding of a particular disease. Hence, they brought great promise, and new insight,  to contemporary genetics research.

So when it comes to doing the statistical analysis for our GWAS, we are essentially performing the same mathematical routine over and over again for each genetic variant in turn. This procedure is automated by computer programmes designed to do this efficiently. At the end we have a vast table (as a gene will have multiple genetic variants across it this can contain hundreds of thousands or even millions of rows) of summary statistics to draw our conclusions from. One highly important number for each site is the p-value from each statistical test that we can use to rank our table of results. There is no plausible way in which we can apply the standard checks of the individual statistical tests that a mathematician may have typically been taught to do (i.e. do the data meet the assumptions), to every single genetic variant that we have tested. Instead we often look at the distribution of p-values across all the tests, generally using a Q-Q plot to compare the expected quartiles to the observed quartiles, to decide if there is major bias, or any confounders affecting the results. Once happy in general, we can look at which genetic variants are significantly associated with your disease of interest.

With a number of computer software tools it can be fairly straight-forward to plug in the numbers and perform the required statistical test. The challenge is often the interpretation or drawing conclusions, in particular when it comes to the p-value.  This is made harder by the fact that most statistical training courses make the rather unrealistic assumption that you will only ever do 1 statistical test at a time and teach you how to apply a significance threshold in this scenario. This knowledge is then taken forward, and merrily applied in exactly the same manner to all statistical tests performed from that point forward.

However, there is a potential trap.

When you perform multiple tests, you increase your chances of getting a significant finding, even if there are no true associations. For example, let’s assume that there is no association between eating fruit and time spent watching TV. But to be 100% sure, we have found a group of people to ask about their TV watching habits and how many apples, bananas, oranges, strawberries, kiwis, melons, pears, blueberries, mangoes and plums they eat each week, then we decide to test each one of these ten different fruits individually. At a 10% significance level ( i.e. p-value < 0.1) we would expect that 0.1 x 10 = 1 test would identify a significant finding, which would be a false positive finding. The more things we test, the more we increase our chances of finding a significant association, even where none exists. This is called ‘multiple testing’, or ‘multiple comparisons’.

This knowledge is crucial for correctly interpreting the results of a GWAS. Say we have tested 500,000 genetic variants, even if none of them were truly associated at a significance threshold of P < 0.05 we would get 500000 x 0.05 = 25000 associations! That is (potentially) a rather hefty number of false positives (the number of associations you report as true but in fact are false). To prevent this, we need to adjust our significance threshold to account for the number of tests we have performed, minimizing our chances of incorrectly reporting a false positive. There are multiple methodologies proposed to resolve this issue, and this is one example where statistics plays an important role in genetic research.

What’s more, by highlighting the high probability of chance findings in GWAS there is a common consensus that all findings, even if they withstand the appropriate control for the number of genetic variants tested, must be replicated before they are ‘believed’ or taken seriously. Replication means repeating the whole GWAS process in a completely separate sample. So that’s more work for the statisticians then!

If you are interested in this topic you may enjoy this cartoon, which gives an alternative (comical solution).