Tag Archives: advice

Meeting in the Aisle

Lab meetings: love them or loathe them, they’re an important part of lab-life. There’s many different formats and ways to do a lab meeting. Sometimes it feels like we’ve tried them all! I’m going to describe our current format and then discuss some other things to try.

Our current lab meeting format is:

  • Weekly. For one hour (Wednesdays at 9am)
  • One person each week talks about their progress. It rotates around.
  • At the start, we talk about general lab issues.
  • Then, last week’s data presenter does a 5 minute, one slide Journal club on a paper of their choice.
  • We organise the rota and table any issues using our general lab Trello board.

Currently, we meet in one of the pods in our building. A pod is a sound-proofed booth that seats 8 people on two sofa style seats. It has a table and an additional 2 people can cram in if needed. Previously we used a meeting room, with the presenter stood at the front using PowerPoint with a projector. One week the meeting room was unavailable and so we used a pod instead. It is a lot more informal and the suggestions and discussions flowed as a result. So we have kept the meeting in the pod, using a laptop to present data.

In addition to this, each person in my lab meets with me for 30 min on a Monday morning to go through raw data and troubleshooting. They also present a more formal talk to the centre once every 6-9 months. I mention this to give some context. Our lab meetings are something between “my cloning hasn’t worked” and a polished presentation.

I’m happy with the current arrangement, but we’ve tried many alternatives. Here is a brief list of things you can consider.


Two presenters

In my opinion this is a bad idea. We went through a period of doing this so that lab presentations were more frequent, or because we were also doing journal clubs too (I forget which). What happens is that one person has a lot of data and gets lots of discussion and then we either run out of time or the other person feels bad if they don’t have as much stuff to talk about. Accidentally you have made unnecessary competition amongst lab members which is not good. Just go for one presenter. The presenter feels like it is their day to get as much as they can out of the meeting and then next week the focus will move to someone else.


This is where you go round and people say what they have done since the last meeting. Depending on the size of the group, this probably takes 2 hours or “as long as it takes” which cuts further into the working day. If the meeting is too frequent, lab members can soon get into a groove of saying “nothing worked” each time and it’s difficult to keep track of who is struggling. Not only is it easy for people to hide, the meeting can also become dominated by someone with interesting data. The format also doesn’t develop any presentation/explanation skills. My preference is to keep the focus on one person.

Rotating data talk and journal clubs

It is really common, especially if you have a small group to do data presentation one week and then journal club the next week. My feelings on Journal Clubs are: if they are done properly, they can be really useful and constructive. Too often they regress into the complete trashing of a paper. As fun as this is, it doesn’t teach trainees the right skills. I’d love it if people in the lab were on top of the literature, but forcing people to delve deeply into one paper is not very effective in promoting this behaviour. I think that it’s more important to use the lab meeting time to go through lab data rather than talk about someone else’s work. Some labs have it set up where the presenter can pick data or paper, which means people who are struggling with their project can hide behind presenting papers. I’m not a fan. We currently do a 5-minute journal club to briefly cover a paper and say why they thought it was good. This takes up minimal time and people can read more deeply if they want. I got this tip from another lab. I recently heard of a lab who spend one meeting a month going through one paper per lab member. We might try this in the future. We also have a list on our General lab Trello board for suggesting cool papers that people think others should read.

Banning powerpoint, western films on the table

At some point I got fed up with seeing a full-on talk from lab members each week, with an introduction and summary (and even acknowledgements!). Partly because it was very repetitive, partly because it inhibited discussions and also I felt people were spending too much time preparing their talk. Moving to the pod (see above) kind of solved this naturally. In the past, we did a total back-to-basics: “PowerPoint is now banned bring your lab book and let’s see the raw data”. This was a good shock to the system. However, people started printing out diagrams… these were made in PowerPoint … and before I knew it, PowerPoint was back! Now, there is value in lab members giving a proper talk in lab meeting. Everyone needs to learn to do it and it can quickly get people used to presenting. Not everyone is great at it though and what lab members need from a lab meeting – I believe – is feedback on their project and injection of new ideas. A formal talk from someone struggling to do a good job or overcome with nervousness doesn’t help anyone. I prefer to keep things informal. Lots of interruptions, questions and enthusiasm from the audience.

Joint lab meetings

When my group was starting and I just had two people we joined in with another lab in their lab meetings.   This worked well until my group was too large to make it work well. What was good was that the other PI was more experienced and liked to do a “blood on the floor” style of lab meeting. This is not really my style, but we had a “good cop, bad cop” thing going on which was useful. For a while. If the lab ethos is too different it can cause friction and if the other PI has any bad habits, things can quickly unravel. There’s also issues around collaboration and projects overlapping which can make joint lab meetings difficult. So, this can be useful if you can find the right lab to partner with, but proceed with caution.

Themed lab meetings 

No, not turning up dressed as someone from The Rocky Horror Picture Show… In my lab we work in two different areas. For a few years we segregated the lab meetings by theme. This seemed like a great idea initially, but in the end I changed from this because I worried it set up an artificial divide. People from the other theme started to ask if they could work in the lab instead. There was also different numbers of people working on the two themes. I tried to rotate the presenters fairly, but there was resentment that people presented more often on one theme than the other.  I know some dual-PI labs who do this successfully, but they have far more people. This is not recommended for a regular one PI lab with less than 10 people. Anyway, most labs just work in one area anyway.

Skype and remote lab meetings

For about one year, we had a student join our lab meetings via skype. She was working at another university and it was important for her to be involved in these meetings. It worked OK and she could even present her data when it was her turn. We used the lab dropbox folder for sharing slides, papers and data with her. We still use this folder now for that purpose. I know PIs who skype in to lab meetings when they are away, so that the lab meeting always goes ahead at the same time each week. I have never done this and don’t think it would work for our lab.

Fun stuff – breaking the routine

OK. Depending on your definition of fun… to check on the state of people’s lab books. I ask lab members to bring along their lab books without warning to the lab meeting and then get them to swap with a random person and then ask them to explain what that person did in the lab on a random date. It gets the message across and also brings up issues people are having with recording their data. We also occasionally do fun stuff such as quizzes but tend to do these outside of the lab meeting. I’ve also used the lab meeting to teach people how to do things in a software package or some other demo. This breaks things up a bit and can freshen up the lab meeting routine. Something else to consider to keep it fun: a cookie schedule. We don’t have one, but people randomly bring in some food if they have been away somewhere or they have cooked a delicacy from their home country.

State of the lab address

Once a year, normally in January when no-one wants to do the first lab meeting of the New Year, I do a state of the lab address. I go through the goals and objectives of the lab. Things that I feel are going well, areas where we could have done better. Successes from last year. The aim is to set the scene for the year ahead.

People in the lab can get a bit deep into their project and having some kind of overview is actually really helpful for them (or so they tell me!). Invite them along if you are giving a seminar or use a lab meeting to try out a seminar you are going to give so that they can see the big picture.

Ideas session

It doesn’t happen often that a presenter has nothing to present. The gaps between presenters are long enough to ensure this doesn’t happen. However, sometimes it can be that the person scheduled to talk has just given a bigger talk to the whole centre (and I forgot to check). When this has happened, we have switched to a forward-looking lab meeting to plan out ideas. Again this can break up the routine.


I think 1 hour is enough. Any longer and it can start to drag out. I try to make it every week. Occasionally it gets cancelled when my schedule doesn’t allow it. But if the schedule gets too ad hoc, it sends the wrong message to the lab members.

Wednesday morning works well for us, but we’ve tried Tuesday mornings, Wednesday afternoons etc. I’m happy to set this by the demands from experiments etc. For example, most people in my lab like to image cells Thursday and Friday so those days are off limits. I also ask that everyone comes on time, and try to lead by example. I know a lab where they instigated a 1 Euro fine for lateness, including the PI. This is used as a cookie fund.

No lab meeting at all!

During my PhD we never had a regular lab meeting. Well, I can remember a few occassions where we tried to get it going but it didn’t stick. In my postdoc lab we also similarly failed to do it regularly. I didn’t mind at the time and was happy to spend the time instead working in the lab. However, I can see that many issues in the lab would’ve probably been solved by regular meetings. So I’m pro-lab meeting.

And finally…

Maybe this should have been at the beginning… but what exactly is the point of a lab meeting?

Presenter – Feedback on their project, injection of new ideas, is this the right route to go down? etc. Improve presentation skills, explain their project to others can help understanding.

Other lab people – Update on the presenter’s project, a feeling for what is expected, ideas for their own project. Have your say and learn to ask questions constructively.

PI – Update on project, give feedback, oversee the tone and standard.

Everyone – lab cohesion, a chance to address issues around the lab, catch up on the latest papers and data.

If none of the above suggestions sound good to you, maybe think about what you are trying to get out of your lab meetings and design a format that helps you achieve this.

The post title is taken from Meeting in the Aisle by Radiohead, B-side on the Karma Police single.

Ten Years Gone

Ten years ago today I became a PI. Well, that’s not quite true. On that day, I took up my appointment as a Lecturer at University of Liverpool, but technically I was not a PI. I had no lab space (it was under construction), I had no people, and I also had no money for research. I arrived for work. I was shown to a windowless office that I would share with another recent recruit, and told to get on with it. With what I should be getting on with, I was not quite sure.

So is this a cause for celebration?

By Rob Irgendwer (Own work) [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons

By Rob Irgendwer (Own work) [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)%5D, via Wikimedia Commons

The slow start to my career as an independent scientist makes it a bit difficult to know when I should throw the party. I could mark the occasion of my lab finally becoming ready for habitation. This happened sometime in March 2007. Perhaps it should be when I did the first experiment in my new lab (April 2007). Or it could be when I received notification of my first grant award (Summer 2007), or when I hired the first person, a technician, in October 2007. It wasn’t until December of 2007 when my first postdoc arrived that the lab really got up-and-running. This was when I felt like I was actually a PI.

Looking back

In retrospect, I am amazed I survived this cold start to my independent career: effectively taking a year-long involuntary break from research. But I was one of the fortunate ones. I was hired at the same time as 6 other PIs-to-be. Over time it was clear that without good support some of us were going to fail. Sure enough, after 18 months, one switched to a career in grant administration in another country. Another left for a less independent position. One more effectively gave up on the PI dream and switched to full-time teaching. But there was success. Two of the other recruits landed grants early and were in business as soon as our labs were renovated. I also managed to get some money. The other person didn’t get a grant until years later, but somehow survived and is still running a group. So of 7 potential group leaders, only 4 ended up running a research group and the success of our groups has been mixed: problems with personnel, renewing funding…

Having a Plan B is probably a good idea. It’s well publicised that the conversion rate from PhD student to Professor is cited as 0.45% (from a 2010 report in the UK). It’s important to make new students aware of this. Maybe a one-in-200 chance sounds reasonable if they are full of confidence… but they need to realise that even they persevere down the academic route, they might indeed get a “group leader job” yet it still might not work out.

I had no Plan B.

I think there were many things that the University could have done differently to ensure more success among us new starters. The obvious thing would be to give a decent startup package. Recruiting as many people as possible with the money available gets lots of people, but gives them no resources. This isn’t a recipe for success.

Also, hiring seven people with completely unconnected research interests was not a smart thing to do. With nothing in common, any help we could give each other was limited. Moreover, only a few of us had genuine research links to established faculty. This made life even more difficult. Going over this in more detail is probably not appropriate here… I am grateful that I got hired, even if things were not ideal. Anyway, I survived this early phase and my lab began to grow…

Reasons to be cheerful

I have been very fortunate to have had some great people working in my group. The best thing about being a group leader is working with smart people. Seeing each develop as a scientist and progress in their careers… this is undoubtedly the highlight.

With talented people onboard, the group really got going and we began making discoveries. My top three papers which gave me most pleasure were not necessarily our biggest hitters. These are, in chronological order:

  1. Our first paper from the lab is special because it signified that we were “open for business”. This came in 2009. Fiona Hood and I showed (somewhat controversially) that two clathrin isoforms behaved similarly in cells depleted of endogenous clathrin.
  2. Dan Booth and Fiona worked together to find the spindle clathrin complex and show that it was a microtubule crosslinker. This paper was the main thing I was aiming to do when I setup my group.
  3. Anna Willox and I worked on one of my favourite papers showing that there are four interaction sites on the clathrin N-terminal domain. I love this paper because it was a side project for Anna. We made a prediction based on symmetry, and a large dollop of guesswork, which turned out to be right. Very satisfying.

Of course there were many more papers and I’m proud of them all. But these three stand out.

I’m also thankful that I’ve been able to keep the lab afloat financially. Thanks to Cancer Research UK, who funded my lab right at the start and still do today. Also thanks to Wellcome, BBSRC, MRC, North West Cancer Research who all funded important projects in my lab.

The other highlight has been interacting with other groups. There have been some great collaborations; most productively with Ian Prior in Liverpool and Richard Bayliss in Leeds, as well as other stuff which didn’t generate any papers but was still a lot of fun. Moving from Liverpool to Warwick in 2013 opened up so many new possibilities which I am continuing to enjoy immensely.

“The move” was the most significant event in the history of the Royle Lab. Many circumstances precipitated it, many of which are not appropriate to discuss here. However, the main driver was “being told to get on with it” right at the start. Feeling completely free to do whatever I wanted to do was absolutely fantastic and was one of the best things about my former University. Sometimes though, the best things are also the worst. I gradually began to realise that this freedom came because nobody really cared what I was doing or if my career was a success or not. I also needed more interactions with more cell biologists and this meant moving. Ironically, after I left, the University recruited a number of promising early career cell biologists all of whom I would have enjoyed working alongside.

If you have read this far, I am impressed!

Posts like this should probably end with some pithy advice. Except there’s none I have to offer to people just starting out. Ten years is a long time and a lot has changed. What worked back then probably doesn’t work now. Many of the mistakes I made, maybe you could dodge some of those, but you will make others. That’s OK, we’re all just making it up as we go along.

So, ten years of the Royle Lab (sort of). It’s been fun. I have the best job in the world and there’s lots to celebrate. But this post explains why I won’t be celebrating today.

The post title comes from “Ten Years Gone” by Led Zeppelin from their Double LP “Physical Graffiti”.

The Digital Cell: Statistical tests

Statistical hypothesis testing, commonly referred to as “statistics”, is a topic of consternation among cell biologists.

This is a short practical guide I put together for my lab. Hopefully it will be useful to others. Note that statistical hypothesis testing is a huge topic and one post cannot hope to cover everything that you need to know.

What statistical test should I do?

To figure out what statistical test you need to do, look at the table below. But before that, you need to ask yourself a few things.

  • What are you comparing?
  • What is n?
  • What will the test tell you? What is your hypothesis?
  • What will the p value (or other summary statistic) mean?

If you are not sure about any of these things, whichever test you do is unlikely to tell you much.

The most important question is: what type of data do you have? This will help you pick the right test.

  • Measurement – most data you analyse in cell biology will be in this category. Examples are: number of spots per cell, mean GFP intensity per cell, diameter of nucleus, speed of cell migration…
    • Normally-distributed – this means it follows a “bell-shaped curve” otherwise called “Gaussian distribution”.
    • Not normally-distributed – data that doesn’t fit a normal distribution: skewed data, or better described by other types of curve.
  • Binomial – this is data where there are two possible outcomes. A good example here in cell biology would be a mitotic index measurement (the proportion of cells in mitosis). A cell is either in mitosis or it is not.
  • Other – maybe you have ranked or scored data. This is not very common in cell biology. A typical example here would be a scoring chart for a behavioural effect with agreed criteria (0 = normal, 5 = epileptic seizures). For a cell biology experiment, you might have a scoring system for a phenotype, e.g. fragmented Golgi (0 = is not fragmented, 5 = is totally dispersed). These arbitrary systems are a not a good idea. Especially, if the person scoring is unblinded to the experimental procedure. Try to come up with an unbiased measurement procedure.


What do you want to do? Measurement



(not Normal)



Describe one group Mean, SD Median, IQR Proportion
Compare one group to a value One-sample t-test Wilcoxon test Chi-square
Compare two unpaired groups Unpaired t-test Wilcoxon-Mann-Whitney two-sample rank test Fisher’s exact test

or Chi-square

Compare two paired groups Paired t-test Wilcoxon signed rank test McNemar’s test
Compare three or more unmatched groups One-way ANOVA Kruskal-Wallis test Chi-square test
Compare three or more matched groups Repeated-measures ANOVA Friedman test Cochran’s Q test
Quantify association between two variables Pearson correlation Spearman correlation
Predict value from another measured variable Simple linear regression Nonparametric regression Simple logistic regression
Predict value from several measured or binomial variables Multiple linear (or nonlinear) regression Multiple logistic regression

Modified from Table 37.1 (p. 298) in Intuitive Biostatistics by Harvey Motulsky, 1995 OUP.

What do “paired/unpaired” and “matched/unmatched” mean?

Most of the data you will get in cell biology is unpaired or unmatched. Individual cells are measured and you have say, 20 cells in the control group and 18 different cells in the test group. These are unpaired (or unmatched in the case of more than one test group) because the cells are different in each group. If you had the same cell in two (or more) groups, the data would be paired (or matched). An example of a paired dataset would be where you have 10 cells that you treat with a drug. You take a measurement from each of them before treatment and a measurement after. So you have paired measurements: one for cell A before treatment, one after; one for cell B before and after, and so on.

How to do some of these tests in IgorPRO

The examples below assume that you have values in waves called data0, data1, data2,… substitute the wavenames for your actual wave names.

Is it normally distributed?

The simplest way is to plot them and see. You can plot out your data using Analysis>Histogram… or Analysis>Packages>Percentiles and BoxPlot… Another possibility is to look at skewness or kurtosis of the dataset (you can do this with WaveStats, see below)

However, if you only have a small number of measurements, or you want to be sure, you can do a test. There are several tests you can do (Kolmogorov-Smirnoff, Jarque-Bera, Shapiro-Wilk). The easiest to do and most intuitive (in Igor) is Shapiro-Wilk.

StatsShapiroWilkTest data0

If p < 0.05 then the data are not normally distributed. Statistical tests on normally distributed data are called parametric, while those on non-normally distributed data are non-parametric.

Describe one group

To get the mean and SD (and lots of other statistics from your data):

Wavestats data0

To get the median and IQR:

StatsQuantiles/ALL data0

The mean and sd are also stored as variables (V_avg, V_sdev). StatsQuantiles calculates V_median, V_Q25, V_Q75, V_IQR, etc. Note that you can just get the median by typing Print StatsMedian(data0) or – in Igor7 – Print median(data0). There is often more than one way to do something in Igor.

Compare one group to a value

It is unlikely that you will need to do this. In cell biology, most of the time we do not have hypothetical values for comparison, we have experimental values from appropriate controls. If you need to do this:

StatsTTest/CI/T=1 data0

Compare two unpaired groups

Use this for normally distributed data where you have test versus control, with no other groups. For paired data, use the additional flag /PAIR.

StatsTTest/CI/T=1 data0,data1

For the non-parametric equivalent, if n is large computation takes a long time. Use additional flag /APRX=2. If the data are paired, use the additional flag /WSRT.

StatsWilcoxonRankTest/T=1/TAIL=4 data0,data1

For binomial data, your waves will have 2 points. Where point 0 corresponds to one outcome and point 1, the other. Note that you can compare to expected values here, for example a genetic cross experiment can be compared to expected Mendelian frequencies. To do Fisher’s exact test, you need a 2D wave representing a contingency table. McNemar’s test for paired binomial data is not available in Igor

StatsChiTest/S/T=1 data0,data1

If you have more than two groups, do not do multiple versions of these tests, use the correct method from the table.

Compare three or more unmatched groups

For normally-distributed data, you need to do a 1-way ANOVA followed by a post-hoc test. The ANOVA will tell you if there are any differences among the groups and if it is possible to investigate further with a post-hoc test. You can discern which groups are different using a post-hoc test. There are several tests available, e.g. Dunnet’s is useful where you have one control value and a bunch of test conditions. We tend to use Tukey’s post-hoc comparison (the /NK flag also does Newman-Keuls test).

StatsAnova1Test/T=1/Q/W/BF data0,data1,data2,data3
StatsTukeyTest/T=1/Q/NK data0,data1,data2,data3

The non-parametric equivalent is Kruskal-Wallis followed by a multiple comparison test. Dunn-Holland-Wolfe method is used.

StatsKSTest/T=1/Q data0,data1,data2,data3
StatsNPMCTest/T=1/DHW/Q data0,data1,data2,data3

Compare three or more matched groups

It’s unlikely that this kind of data will be obtained in a typical cell biology experiment.

StatsANOVA2RMTest/T=1 data0,data1,data2,data3

There are also operations for StatsFriedmanTest and StatsCochranTest.


Straightforward command for two waves or one 2D wave. Waves (or columns) must be of the same length

StatsCorrelation data0

At this point, you probably want to plot out the data and use Igor’s fitting functions. The best way to get started is with the example experiment, or just display your data and Analysis>Curve Fitting…

Hazard and survival data

In the lab we have, in the past, done survival/hazard analysis. This is a bit more complex and we used SPSS and would do so again as Igor does not provide these functions.

Notes for use

Screen Shot 2016-07-12 at 14.18.18The good news is that all of this is a lot more intuitive in Igor 7! There is a new Menu item called Statistics, where most of these functions have a dialog with more information. In Igor 6.3 you are stuck with the command line. Igor 7 will be out soon (July 2016).

  • Note that there are further options to most of these commands, if you need to see them
    • check the manual or Igor Help
    • or type ShowHelpTopic “StatsMedian” in the Command Window (put whatever command you want help with between the quotes).
  • Extra options are specified by “flags”, these are things like “/Q” that come after the command. For example, /Q means “quiet” i.e. don’t print the output into the history window.
  • You should always either print the results to the history or put them into a table so that we can check them. Note that the table gets over written if you do the same test with different data, so printing in this case is a good idea.
  • The defaults in Igor are setup OK for our needs. For example, Igor does two-tailed comparison, alpha = 0.05, Welch’s correction, etc.
  • Most operations can handle waves of different length (or have flags set to handle this case).
  • If you are used to doing statistical tests in Excel, you might be wondering about tails and equal variances. The flags are set in the examples to do two-tailed analysis and unequal variances are handled by Welch’s correction.
  • There’s a school of thought that says that using non-parametric tests is best to be cautious. These tests are not as powerful and so it is best to use parametric tests (t test, ANOVA) when you can.

Part of a series on the future of cell biology in quantitative terms.

The Digital Cell: Getting started with IgorPRO

This post follows on from “Getting Started“.

In the lab we use IgorPRO for pretty much everything. We have many analysis routines that run in Igor, we have scripts for processing microscope metadata etc, and we use it for generating all figures for our papers. Even so, people in the lab engage with it to varying extents. The main battle is that the use of Excel is pretty ubiquitous.

I am currently working on getting more people in the lab started with using Igor. I’ve found that everyone is keen to learn. The approach so far has been workshops to go through the basics. This post accompanies the first workshop, which is coupled to the first few pages of the Manual. If you’re interested in using Igor read on… otherwise you can skip to the part where I explain why I don’t want people in the lab to use Excel.

IgorPro is very powerful and the learning curve is steep, but the investment is worth it.

WaveMetrics_IGOR_Pro_LogoThese are some of the things that Igor can do: Publication-quality graphics, High-speed data display, Ability to handle large data sets, Curve-fitting, Fourier transforms, smoothing, statistics, and other data analysis, Waveform arithmetic, Matrix math, Image display and processing, Combination graphical and command-line user interface, Automation and data processing via a built-in programming environment, Extensibility through modules written in the C and C++ languages. You can even play games in it!

The basics

The first thing to learn is about the objects in the Igor environment and how they work.There are four basic objects that all Igor users will encounter straight away.

  • Waves
  • Graphs
  • Tables
  • Layouts

All data is stored as waveforms (or waves for short). Waves can be displayed in graphs or tables. Graphs and tables can be placed in a Layout. This is basically how you make a figure.

The next things to check out are the command window (which displays the history), the data browser and the procedure window.

Essential IgorPro

  • Tables are not spreadsheets! Most important thing to understand. Tables are just a way of displaying a wave. They may look like a spreadsheet, but they are not.
  • Igor is case insensitive.
  • Spaces. Igor can handle spaces in names of objects, but IMO are best avoided.
  • Igor is 0-based not 1-based
  • Logical naming and logical thought – beginners struggle with this and it’s difficult to get this right when you are working on a project, but consistent naming of objects makes life easier.
  • Programming versus not programming – you can get a long way without programming but at some point it will be necessary and it will save you a lot of time.

Pretty soon, you will go beyond the four basic objects and encounter other things. These include: Numeric and string variables, Data folders, Notebooks, Control panels, 3D plots – a.k.a. gizmo, Procedures.

Getting started guide

Getting started guide

Why don’t we use Excel?

  • Excel can’t make high quality graphics for publication.
    • We do that in Igor.
    • So any effort in Excel is a waste of time.
  • Excel is error-prone.
    • Too easy for mistakes to be introduced.
    • Not auditable. Tough/impossible to find mistakes.
    • Igor has a history window that allows us to see what has happened.
  • Most people don’t know how to use it properly.
  • Not good for biological data – Transcription factor Oct4 gets converted to a date.
  • Limited to 1048576 rows and 16384 columns.
  • Related: useful link describing some spreadsheet crimes of data entry.

But we do use Excel a lot

  • Excel is useful for quick calculations and for preparing simple charts to show at lab meeting.
  • Same way that Powerpoint is OK to do rough figures for lab meeting.
  • But neither are publication-quality.
  • We do use Excel for Tracking Tables, Databases(!) etc.

The transition is tough, but worth it

Writing formulae in Excel is straightforward, and the first thing you will find is that to achieve the same thing in Igor is more complicated. For example, working out the mean for each row in an array (a1:y20) in Excel would mean typing =AVERAGE(A1:y1) in cell z1 and copying this cell down to z20. Done. In Igor there are several ways to do this, which itself can be unnerving. One way is to use the Waves Average panel. You need to know how this works to get it to do what you want.

But before you turn back, thinking I’ll just do this in Excel and then import it… imagine you now want to subtract a baseline value from the data, scale it and then average. Imagine that your data are sampled at different intervals. How would you do that? Dealing with those simple cases in Excel is difficult-to-impossible. In Igor, it’s straightforward.

Resources for learning more Igor:

  • Igor Help – fantastic resource containing the manual and more. Access via Help or by typing ShowHelpTopic “thing I want to search for”.
  • Igor Manual – This PDF is available online or in Applications/Igor Pro/Manual. This used to be a distributed as a hard copy… it is now ~3000 pages.
  • Guided Tour of IgorPro – this is a great way to start and will form the basis of the workshops.
  • Demos – Igor comes packed with Demos for most things from simple to advanced applications.
  • IgorExchange – Lots of code snippets and a forum to ask for advice or search for past answers.
  • Igor Tips – I’ve honestly never used these, you can turn on tips in Igor which reveal help on mouse over.
  • Igor mailing list – topics discussed here are pretty advanced.
  • Introduction to IgorPRO from Payam Minoofar is good. A faster start to learning to program that reading the manual.
  • Hands-on experience!

Part of a series on the future of cell biology in quantitative terms.

The Digital Cell: Getting Started

More on the theme of “The Digital Cell“: using quantitative, computational approaches in cell biology.

So you want to get started? Well, the short version of this post is:

Find something that you need to automate and get going!


I make no claim to be a computer wizard. My first taste of programming was the same as anyone who went to school in the UK in the 1980s: BBC Basic. Although my programming only went as far as copying a few examples from the book (right), this experience definitely reduced the “fear of the command line”. My next encounter with coding was to learn HTML when I was an undergraduate. It was not until I was a postdoc that I realised that I needed to write scripts in order get computers to do what I wanted them to do for my research.

Image analysis

I work in cell biology. My work involves a lot of microscopy. From the start, I used computer-based methods to quantify images. My first paper mentions quantifying images, but it wasn’t until I was a PhD student that I first used NIH Image (as it was called then) to extract quantitative information from confocal micrographs. I was also introduced to IgorPRO (version 3!) as a PhD student, but did no programming. That came later. As a postdoc, we used Scanalytics’ IPLab and Igor (as well as a bit of ImageJ as it had become). IPLab had an easy scripting language and it was in this program that I learned to write macros for analysis. At this time there were people in the lab who were writing software in IgorPro and MATLAB. While I didn’t pick up programming in IgorPRO or MATLAB then, it made me realise what was possible.

When I started my own group I discovered that IPLab had been acquired by BD Biosciences and then stripped out. I had hundreds of useless scripts and needed a new solution. ImageJ had improved enormously by this time and so this became our default image analysis program. The first data analysis package I bought was IgorPro (version 6) and I have stuck with it since then. In a future post, I will probably return to whether or not this was a good path.

Getting started with programming

Around 2009, I was still unable to program properly. I needed a macro for baseline subtraction – something really simple – and realised I didn’t know how to do it. We didn’t have just one or two traces to modify, we had hundreds. This was simply not possible by hand. It was this situation that made me realise I needed to learn to program.

…having a concrete problem that is impossible to crack any other way is the best motivator for learning to program.

This might seem obvious, but having a concrete problem that is impossible to crack any other way is the best motivator for learning to program. I know many people who have decided they “want to learn to code” or they are “going to learn to use R”. This approach rarely works. Sitting down and learning this stuff without sufficient motivation is really tough. So I would advise someone wanting to learn programming to find something that needs automation and just get going. Just get something to work!

Don’t worry (initially) about any of the following:

  • What program/language to use – as long as it is possible, just pick something and do it
  • If your code is ugly or embarrassing to show to an expert – as long as it runs, it doesn’t matter
  • About copy-and-pasting from examples – it’s OK as long as you take time to understand what you are doing, this is a quick way to make progress. Resources such as stackoverflow are excellent for this
  • Bugs – you can squish them, they will frustrate you, but you might need some…
  • Help – ask for help. Online forums are great, experts love showing off their knowledge. If you have local expertise, even better!

Once you have written something (and it works)… congratulations, you are a computer programmer!

IMG_2206Seriously, that is all there is to it. OK, it’s a long way to being a good programmer or even a competent one, but you have made a start. Like Obi Wan Kenobi says: you’ve taken your first step into a larger world.

So how do you get started with an environment like IgorPro? This will be the topic for next time.

Part of a series on the future of cell biology in quantitative terms.

The Digital Cell: Workflow

The future of cell biology, even for small labs, is quantitative and computational. What does this mean and what should it look like?

My group is not there yet, but in this post I’ll describe where we are heading. The graphic below shows my current view of the ideal workflow for my lab.


The graphic is pretty self-explanatory, but to walk you through:

  • A lab member sets up a microscopy experiment. We have standardised procedures/protocols in a lab manual and systems are in place so that reagents are catalogued to minimise error.
  • Data goes straight from the microscope to the server (and backed-up). Images and metadata are held in a database and object identifiers are used for referencing in electronic lab notebooks (and for auditing).
  • Analysis of the data happens with varying degrees of human intervention. The outputs of all analyses are processed automatically. Code for doing these steps in under version control using git (github).
  • Post-analysis the processed outputs contain markers for QC and error checking. We can also trace back to the original data and check the analysis. Development of code happens here too, speeding up slow procedures via “software engineering”.
  • Figures are generated using scripts which are linked to the original data with an auditable record of any modification to the image.
  • Project management, particularly of paper writing is via trello. Writing papers is done using collaborative tools. Everything is synchronised to enable working from any location.
  • This is just an overview and some details are missing, e.g. backup of analyses is done locally and via the server.

Just to reiterate, that my team are not at this point yet, but we are reasonably close. We have not yet implemented three of these things properly in my group, but in our latest project (via collaboration) the workflow has worked as described above.

The output is a manuscript! In the future I can see that publication of a paper as a condensed report will give way to making the data, scripts and analysis available, together with a written summary. This workflow is designed to allow this to happen easily, but this is the topic for another post.

Part of a series on the future of cell biology in quantitative terms.

The Digital Cell

If you are a cell biologist, you will have noticed the change in emphasis in our field.

At one time, cell biology papers were – in the main – qualitative. Micrographs of “representative cells”, western blots of a “typical experiment”… This descriptive style gave way to more quantitative approaches, converting observations into numbers that could be objectively assessed. More recently, as technology advanced, computing power increased and data sets became more complex, we have seen larger scale analysis, modelling, and automation begin to take centre stage.

This change in emphasis encompasses several areas including (in no particular order):

  • Statistical analysis
  • Image analysis
  • Programming
  • Automation allowing analysis at scale
  • Reproducibility
  • Version control
  • Data storage, archiving and accessing large datasets
  • Electronic lab notebooks
  • Computer vision and machine learning
  • Prospective and retrospective modelling
  • Mathematics and physics

The application of these areas is not new to biology and has been worked on extensively for years in certain areas. Perhaps most obviously by groups that identified themselves as “systems biologists”, “computational biologists”, and people working on large-scale cell biology projects. My feeling is that these methods have now permeated mainstream (read: small-scale) cell biology to such an extent that any groups that want to do cell biology in the future have to adapt in order to survive. It will change the skills that we look for when recruiting and it will shape the cell biologists of the future. Other fields such as biophysics and neuroscience are further through this change, while others have yet to begin. It is an exciting time to be a biologist.

I’m planning to post occasionally about the way that our cell biology research group is working on these issues: our solutions and our problems.

Part of a series on the future of cell biology in quantitative terms.

Tips from the blog IX: running route

University of Warwick is a popular conference destination, with thousands of visitors per year. Next time you visit and stay on campus, why not bring your running shoes and try out these routes?

Route 1


This is just over 10K and it takes you from main campus out towards Cryfield Pavilion. A path goes to the Greenway (a former railway), which is a nice flat gravel track. It goes up to Burton Green and back to campus via Westwood Heath Road. To exit the Greenway at Burton Green you need to take the “offramp” at the bridge otherwise you will end up heading to Berkswell. If you want to run totally off-road*, just turn back at this point (probably ~12K). The path out to the Greenway and the Greenway itself is unlit, so be careful early in the morning or late at night.

GPX of a trace put together on gpsies.

Track 2


This is a variation on Track 1. Instead of heading up the Greenway to Burton Green, take a left and head towards Kenilworth Common. With a bit of navigation you can run on alongside a brook and pop out in Abbey Fields and see the ruins of Kenilworth Abbey. This is out-and-back, 12K. Obviously you can turn back sooner if you prefer. It’s all off-road apart from a few 100m on quiet residential streets as you navigate from the Common to Abbey Fields. GPX from Uni to around the lake at Abbey Fields.

Track 3



This is a variation on Track 1 where you exit the Greenway and take a loop around Crackley Wood. The Wood is nice and has wild deer and other interesting wildlife. This route is totally off-road and is shorter at ~8K. GPX from Uni to around the Wood.


Other Routes

There is a footpath next to a bike lane down the A429 which is popular for runners heading to do a lap or two of Memorial Park in Coventry. This is OK, but means that you run alongside cars a lot.

If you don’t have time for these routes, the official Warwick page has three very short running routes of around 3 to 5 km (1, 2 and 3). I think that these routes are the ones that are on the signpost near the Sports Centre.

* Here, off-road means on paths but not alongside a road on a pavement. It doesn’t mean across fields.

This post is part of a series of tips.

My Blank Pages III: The Art of Data Science

largeI recently finished reading The Art of Data Science by Roger Peng & Elizabeth Matsui. Roger, together with Jeff Leek, writes the Simply Statistics blog and he works at JHU with Elizabeth.

The aim of the book is to give a guide to data analysis. It is not meant as a comprehensive data analysis “how to”, nor is it a manual for statistics or programming. Instead it is a high-level guide: how to think about data analysis and how to go about doing it. This makes it an interesting read for anyone working with data.

I think anyone who reads the Simply Statistics blog or who has read the piece Roger and Jeff wrote for Science, will be familiar with a lot of the content in here. At the beginning of the book, I didn’t feel like I learned too much. However, I can see that the “converted” are maybe not the target audience here. Towards the end of the book, the authors walk through a few examples of how to analyse some data focussing on the question in mind, how to refine it and then how to start the analysis. This is the most useful aspect of the book in my opinion, to see the approach to data analysis working in practice. The authors sum up the book early on by comparing it to books about songwriting. I admit to rolling my eyes at this comparison (data analysis as an artform…), but actually it is a good analogy. I think many people who work with data know how to do it, in the way that people who write songs know how to do it, although they probably have not had a formal course in the techniques that are being used. Equally reading a guidebook on songwriting will not make you a great songwriter. A book can only get you so far, intuition and invention are required and the same applies to data science.

The book was published via Lean Pub who have an interesting model where you pay a recommended price (or more!) but if you don’t have the money, you can pay less. Also, you can see what fraction goes to the author(s). The books can be updated continually as typos or code updates are fixed. Roger and the Simply Stats people have put out a few books via this publisher. These books on R, programming, statistics and data science all look good and it seems more books are coming soon.

On a personal note: In 2014, I decided to try and read one book per month. I managed it, but in 2015, I am struggling. It is now November and this book is the 7th I’ve read this year. It was published in September but it took me until now to finish it. Too much going on…

My Blank Pages is a track by Velvet Crush. This is an occasional series of book reviews.

Trellisaze: Using Trello for lab organisation

Previously, I wrote a post with tips for new PIs on lab organisation. Since that time, I’ve started using Trello to organise operations in my lab.

Trello is basically a way to track the progress of projects. Collaborative working is built-in. A friend had begun using Trello as she got involved in building an app. It seems that Trello is popular among teams working to develop software. Sure enough, I asked for opinions on Trello via twitter and got a nice email from somebody on the Open Microscopy Environment team on the pros and cons of using Trello. You can see one of their boards in action here, it is after all, open! This convinced me to give it a go. I set up a few boards, invited the lab members and got stuck in.


I set up subject-specific and technique-specific boards (as well as my own to-do list and a board for tasks at home). All lab members are members of Royle Lab group and we have two groups boards – General and Molecular Biology. The General Stuff contains information about lab meetings, one-to-one meetings, orders etc. even photos of lab socials. Molecular Biology, everyone is a member because everybody does some cloning in my group. Then the Membrane Traffic people have a board that the others can’t see etc. I’ll probably move to making them all available to everybody in the group soon. The default is for boards to be closed, i.e. not possible for outsiders to see. You need to add people to your board for them to see it and to work with you.

Part of an example board is shown here:


I’ve redacted parts that we’re not ready to tell the world about just yet. There are many guides online to show you how to get going with Trello. Basically, you have Boards. Within each board you have Lists, the columns that you see above. On a list, you put Cards. On the back of the card you can comment, add checklists, files, links, due dates etc etc. People can be assigned to cards and to provide updates with how it’s going. All of these things can be easily edited as priorities change. For example, I am writing a paper with one person and so we have a list for the paper, with cards for each figure and a card for writing.

I’m happy with how this is working. For example, when writing a paper, myself and the first author used to do an awful lot of rapid communication via email (I’ve previously called this Tiki-taka). It’s best if this was kept out of our Inboxes and organised somewhere. Also, how can we keep track of what still needs doing? Did that experiment get redone with the extra control? Which folder were the tracking experiments in? All of this can be recorded and managed using Trello. You can see the little speech bubble on each card indicating that we are talking to each other.

My tips/notes are:

  • In a team, there will always be some people who take to it and use it avidly, while others don’t engage.
  • To encourage take up, I communicate through Trello to the lab rather than using email.
  • Also, at our weekly one-to-one meetings, we edit cards together.
  • We are just using the free version. I’ve accumulated credits to go gold, but haven’t done so.
  • There are good iOS and Android apps for Trello. Notifications get pushed here if you subscribe to a board, list or card. It will ping you emails too, but you can switch this off.
  • File sharing is still done via our server (or Dropbox for small files), but notifications go on the board.
  • Make cards very specific, cards covering big lab projects will fester and clutter up the list.
  • The help files are incredibly nerdy… they even have a dog called Taco who pops up now and again.

Summary: I recommend Trello (note that other management softwares are available – kanbanflow, slack etc), particularly if you have a large group. Even for new PIs or those with small groups who might be on top of everything, I think there is still something that you’ll get out of it.

The post title is taken from Trellisaze by Slowdive from their Pygmalion LP.