In the previous article in this series, we covered teamwork and networking. Now it’s time to move on to what many people consider the most boring part of the lab work: the analysis. I know we all wish that a simple histogram or a rather nice-looking Western blot or PCR would suffice. But the fact is, evidence from one experiment isn’t really going to cut it. Ideally, we need to be able to show that our data is reliable: both on a mathematical and statistical level, and by looking at similar experiments that other groups or individuals have carried out and comparing those results to our own.
Analytical skills such as these are highly valued in both post-doc positions and in pharma and communication. A paper, grant submission or report will have more impact when it has some decent statistical analysis thrown in, and statistical skills will look good on your CV.
Dammit man! I’m a scientist, not a mathematician!
While we’re talking about analytical skills, don’t assume these need be limited to maths. Look up the Wikipedia entry for “Analysis”, and you’ll see a list of how analysis can be applied from everything from chemistry to literature. Some examples of the different types of analysis we use in biological sciences are:
· Mathematical Analysis
When most scientists think of maths, we automatically (in our state of panic) jump to statistics (more on stats later). However, all of us are already used to doing maths and working with numbers: think of every dilution you’ve ever calculated, or cell count you’ve painstakingly made, or transfection you’ve ever optimised, even every buffer you’ve had to make and adjust. Each is an exercise in maths itself, handling basics such as ratios, divisions multiplication and equation manipulations. Chances are, by the time you’re halfway through your PhD, you’re doing these on autopilot anyway (feel free to experience a sense of achievement here!), so slightly more complex numerical analysis should be considered a brain exercise, and not some kind of torture!
· Literature Analysis
As biologists, we can analyse literature from journals and textbooks – something we all have to do when it comes to writing an introduction for a paper or your lit review. Collating previous results, and comparing and contrasting it all, highlights your ability to manage and interpret data from different sources. Our ability to carry out literature searches and subsequent analysis of the many hits we get following a PubMed query is itself a highly valued skill – fast literature searchers and analysers are able to identify where new work will fit into what’s already been accomplished.
· Bioinformatical Analysis
Analysis in biology may also involve looking at protein or nucleotide structures, and deriving information such as size, charge, structure, hydrophobicity, etc. This ties in heavily with bioinformatics (something else which many biologists use with reluctance!) but can often provide a useful addition to any research you are carrying out. Simple cross-species comparisons can be made with databases such as Ensembl, and these comparisons may extend to anything from simple DNA/protein alignments, to comparisons of chromosome structures. Bioinformatical analysis may also help to identify previously unidentified isoforms within a species through BLAST analysis with EST (expressed sequence tag) databases. To go through the wealth of knowledge that bioinformatics provides that is literally at your fingertips would take weeks, and requires some degree of skill. The best way to go about learning how to do this is to seek the advice of an expert in the area (you may have one at your institution), or at least somebody who’s done a bit of bioinformatics before! (A useful book I’ve found is “Bioinformatics for Dummies” by Jean-Michel Claverie and Cedric Notredame).
· Image Analysis
It’s all very well taking a nice picture of some tissue or cells, but you need to be able to look at that picture and figure out what it’s telling you. Perhaps there are particular structures in your cell you need to point out. Or maybe there’s an interesting aspect to a particular histological aspect. Sometimes your pictures aren’t as clear as you’d like them to be (we’ve all had those days where the microscope just isn’t performing) and you need to enhance the brightness or contrast – knowing how to do this ethically (there’s only so much enhancement you can carry out before it looks suspicious) is also a valuable analytical skill. Software such as Image J can also be used to measure and analyse the density of bands on your Western blots – useful for comparing protein expression.
The “S” Word – Stats!
If there’s one word that can strike fear and trepidation into the hearts of biologists, it’s “statistics”. From the time I started school I hated this subject (and anybody who tried to teach it to me!), and I would do anything in my power to avoid it, or any other mathematical data handling which threatened to invade my PhD bubble. Sadly, now that I have reached the thesis stage, stats are becoming a sad reality and one which I simply cannot avoid – and no doubt, readers, you will have to face this analytical intrusion too.
Honestly, who gets it? Just looking at a list of the different types of statistical analysis available for a given set of data is enough to make my head spin and turn me into a gibbering wreck. How am I to know if I should apply Student’s T-Test or the Mann-Whitney U Test? And what the heck is standard error for? Or p-values? And how do I get those little error-bar thingies on my graphs? Luckily, most institutions (particularly universities with a maths department) will have help at hand. See if there’s some kind of statistics “help desk” available to you. Or in failing that, email somebody with relevant skills and ask for help (perhaps mention that if the data gets published there might be room for an extra name or acknowledgment on the paper if they make a statistical contribution!).
This was the avenue we took in my lab and honestly, it made handling my data a lot easier. When you have a simple pictorial graph to look at as opposed to a paragraph of text, it helps get your message across much more easily. As an added bonus, these days software programs such as Microsoft Excel make stats so much easier – it’s simply a matter of highlighting your data and typing in a simple equation shortcut: instant stats! Highlight some more cells, click on “Insert Graph”: instant figure!
Not only will stats offer an extra dimension of reliability to your work, it’ll help streamline all those experimental replicates you get with your raw data – because who wants to look throughout pages and pages of un-analysed OD readings or cell counts? Not me. And not anybody reading your thesis or paper either. So it’s worth getting a handle on at least some statistical analysis. Just so that you can prove in an interview (or on your CV) that you understood what you did, and why you chose that option over another.
If you’re still stuck, consider this book: “Statistics for the Terrified Biologist” by Helmut van Emden which I’m told by my (equally stats-clueless) friends is particularly useful! And as always, the “For Dummies” series usually has some helpful input too: “Statistics for Dummies” by Deborah Rumsey-Johnson.
Any more tips on analysis? Comment below!
Next time: (the last in this series) Time Management