The Soft Bulletin: PDF organisation

I recently asked on Twitter for any recommendations for software to organise my PDFs. I got several replies, but nothing really fitted the bill. This is a brief summary.

My situation

I have quite a lot of books, textbooks, cheat sheets, manuals, protocols etc. in PDF format and I need a way to organise them. I don’t need to reference this content, I just need to search it and access it quickly – ideally across several devices.

Note: I don’t collect PDFs of research articles. I have a hundred or so articles that were difficult to get hold of, and I keep those, but I’m pretty complacent about my access to scholarly literature.

I currently use Papers2 for storing my PDFs. It’s OK, but there are some bugs in it. Papers3 came out a few years ago, but I didn’t do the upgrade because there are issues with sync across multiple computers. Now it doesn’t look like Papers will be supported in the future. For example, I heard on Twitter that there is no ETA for an issue with Papers3 on Sierra. Future proofing – I’ve come to realise – is important to me as I am pretty loyal to software, I don’t like to change to something else, but I do like new features and innovation.

I don’t need a solution for referencing. I am resigned to using EndNote for that.

Ideally I just want something like iTunes to organise my PDFs, but I don’t want to use iTunes! Perhaps my requirements are too particular and what I want just isn’t available.

The suggestions

Thanks to everyone who made suggestions. Together with other solutions they were (in no particular order):

Zotero

www.zotero.org

I downloaded this and gave it a brief try. PDF import worked well and the UI looked OK. I stumbled on the sync capabilities. I currently sync my computers with Unison and this is complicated (but not impossible) to do for Zotero. They want you to use cloud syncing – which I would probably be OK with. I need to test out which cloud service is best to use. There is a webDAV option which my University supports and I think this would work for me. I think this software is the most likely candidate for me to switch to.

Mendeley

www.mendeley.com

This software got the most recommendations. I have to admit that the Elsevier connection is a huge turn-off for me. Although the irony of using it to organise my almost exclusively Elsevier-free content would be quite nice. I know that most of this type of software has been bought out by the publishing giants (Papers by Springer, EndNote by Thomson Reuters/Clarivate), but I don’t like this and I don’t have to support it if I don’t want to. I didn’t look into sync capabilities here.

Bookends

www.sonnysoftware.com

People rave about this software package for Mac. I like the fact that it has a separate lineage to the other packages. It is very expensive and it is primarily a referencing package. Right now, I’m just looking for something to organise my PDFs and this seems to be overkill.

Evernote

www.evernote.com

I use Evernote as a lab notebook and it is possible to use it to store PDFs. You can make a NoteBook for them, add a Note for each one and attach the PDF. The major plus here is that I already use it (and pay for it). The big negative is that I would prefer a separate standalone package to organise my PDFs. I know, difficult to please aren’t I?

Finder and Spotlight

This is the D.I.Y. option.

I have to say that this is the most appealing in many ways. If you just name PDFs systematically and store them in a folder hierarchy that you organise and tag – it would work. Sync would work with my current solution. Searching with Spotlight would work just as well as any other program. I would not need another program! At some point in the past I organised my PDFs like this. I moved to storing them in Papers so that it would save them in a hierarchical structure for me. This is what I mean by an iTunes-like organiser. An app to name, tag and file-away the PDFs would be ideal. I don’t want to go back to this if I can help it.

ReadCube

www.readcube.com

Like Mendeley, this is an option that I did not seriously entertain. I think this is too far away from what I want. As I see it, this software is designed as a web extension and paper recommendation service, which is not what I’m looking for.

Papers3

papersapp.com

As mentioned above, the lack of updates to this software and problems with sync mean that I am looking for something else. I really liked Papers2 and would be happy to continue using this if various things like import and editing were improved. I guess the option here is to stay with Papers2 and put up with the little things that annoy me. At some point though there will be a macOS update which breaks it and then I will be stuck.

Endnote
endnote.com

I use Endnote for referencing. I hate Endnote with a passion. But I can use it. I know how to write styles etc. and edit term lists because I’ve used it since something like v3. At some point in the past I began to store papers in Endnote. I stopped doing this and moved to Papers2. I have to admit it’s OK at doing this, although the way it organises the PDFs on disk is a bit strange IMO. I don’t like storing books and other content in my library though so this is not a good solution.

iBooks

Here is a curveball. I use iBooks and Kindle app for reading books in mobi/epub/pdf format. Actually, iBooks works quite well for PDFs and has the ability to sync with other devices. I have a feeling this could work, although some of the PDFs I have are quite bulky and I’d need to figure out a way for them to stay in the cloud and not reside on mobile devices. It’s definitely designed for reading books and not for pulling up the PDF in Preview and quickly finding a specific thing. For this reason I don’t think it would work.

Note that there are other apps for this task. Also, if you search for “PDF” in the App Store, there plenty of other programs aimed at people outside academia. Maybe one of those would be OK.

So what did I do?

I doubt anyone has the precise requirements that I have and so you’re probably not interested in what I decided. However, the simplest thing to do was to import the next batch of PDFs into Papers2 and wait to see if something better comes along. I will try Zotero a bit more when I get some time and see if this is the solution for me.

The post title is taken from The Flaming Lips’ 1999 album “The Soft Bulletin”.

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.

Round-the-table

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.

Time

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.

Tips from the blog XI: Overleaf

I was recently an external examiner for a PhD viva in Cambridge. As we were wrapping up, I asked “if you were to do it all again, what would you do differently?”. It’s one of my stock questions and normally the candidate says “oh I’d do it so much quicker!” or something similar. However, this time I got a surprise. “I would write my thesis in LaTeX!”, was the reply.

As a recent convert to LaTeX I could see where she was coming from. The last couple of manuscripts I have written were done in Overleaf and have been a breeze. This post is my summary of the site.

overleaf-greygreen-410

I have written ~40 manuscripts and countless other documents using Microsoft Word for Mac, with EndNote as a reference manager (although I have had some failed attempts to break free of that). I’d tried and failed to start using TeX last year, motivated by seeing nicely formatted preprints appearing online. A few months ago I had a new manuscript to write with a significant mathematical modelling component and I realised that now was the chance to make the switch. Not least because my collaborator said “if we are going to write this paper in Word, I wouldn’t know where to start”.

screen-shot-2016-12-11-at-07-39-13I signed up for an Overleaf account. For those that don’t know, Overleaf is an online TeX writing tool on one half of the screen and a rendered version of your manuscript on the other. The learning curve is quite shallow if you are used to any kind of programming or markup. There are many examples on the site and finding out how to do stuff is quick thanks to LaTeX wikibooks and stackexchange.

Beyond the TeX, the experience of writing a manuscript in Overleaf is very similar to editing a blog post in WordPress.

Collaboration

The best thing about Overleaf is the ability to collaborate easily. You can send a link to a collaborator and then work on it together. Using Word in this way can be done with DropBox, but versioning and track changes often cause more problems than it’s worth and most people still email Word versions to each other, which is a nightmare. Overleaf changes this by having a simple interface that can be accessed by multiple people. I have never used Google docs for writing papers, but this does offer the same functionality.

All projects are private by default, but you can put your document up on the site if you want to. You might want to do this if you have developed an example document in a certain style.

screen-shot-2016-12-11-at-07-38-36

Versioning

Depending on the type of account you have, you can roll back changes. It is possible to ‘save’ versions, so if you get to a first draft and want to send it round for comment, you can save a version and then use this to go back to, if required. This is a handy insurance in case somebody comes in to edit the document and breaks something.

You can download a PDF at any point, or for that matter take all the files away as a zip. No more finalfinalpaper3final.docx…

If you’re keeping score, that’s Overleaf 2, Word nil.

Figures

Placing figures in the text is easy and all major formats are supported. What is particularly nice is that I can generate figures in an Igor layout and output directly to PDF and put that into Overleaf. In Word, the placement of figures can be fiddly. Everyone knows the sensation of moving a picture slightly and it disappears inexplicably onto another page. LaTeX will put the figure in where you want it or the next best place. It just works.

screen-shot-2016-12-11-at-07-44-33Equations

This is what LaTeX excels at. Microsoft Word has an equation editor which has varied over the years from terrible to just-about-usable. The current version actually uses elements of TeX (I think). The support for mathematical text in LaTeX is amazing, not surprising since this is the way that most papers in maths are written. Any biologist will find their needs met here.

Templates and formatting

There are lots of templates available on Overleaf and many more on the web. For example, there are nice PNAS and PLoS formats as well as others for theses and for CVs and other documents. The typesetting is beautiful. Setting out sections/subsections and table of contents is easy. To be fair to Word, if you know how to use it properly, this is easy too, but the problem is that most people don’t, and also styles can get messed up too easily.

Referencing

This works by adding a bibtex file to your project. You can do this with any reference manager. Because I have a huge EndNote database, I used this initially. Another manuscript I’ve been working on, my student started out with a Mendeley library and we’ve used that. It’s very flexible. Slightly more fiddly than with Word and EndNote. However, I’ve had so many problems (and crashes) with that combination over the years that any alternative is a relief.

Compiling

You can set the view on the right to compile automatically or you can force updates manually. Either way the document must compile. If you have made a mistake, it will complain and try to guess what you have done wrong and tell you. Errors that prevent the document from being compiled are red. Less serious errors are yellow and allow compilation to go ahead. This can be slow going at first, but I found that I was soon up to speed with editing.

Preamble

This is the name of the stuff at the header of a TeX document. You can add in all kinds of packages to cover proper usage of units (siunitx) or chemical notation (mhchem). They all have great documentation. All the basics, e.g. referencing, are included in Overleaf by default.

Offline

The entire concept of Overleaf is to work online. Otherwise you could just use TeXshop or some other program. But how about times when you don’t have internet access? I was concerned about this at the start, but I found that in practice, these days, times when you don’t have a connection are very few and far between. However, I was recently travelling and wanted to work on an Overleaf manuscript on the aeroplane. Of course, with Word, this is straightforward.

With Overleaf it is possible. You can do two things. The first is to download your files ahead of your period of internet outage. You can edit your main.tex document in an editor of your choice. The second option is more sophisticated. You can clone your project with git and then work on that local clone. The instructions of how to do that are here (the instructions, from 2015, say it’s in beta, but it’s fully working). You can work on your document locally and then push changes back to Overleaf when you have access once more.

Downsides

OK. Nothing is perfect and I noticed that typos and grammatical errors are more difficult for me to detect in Overleaf. I think this is because I am conditioned with years of Word use. The dictionary is smaller than in Word and it doesn’t try to correct your grammar like word does (although this is probably a good thing!). Maybe I should try the rich text view and see if that helps. I guess the other downside is that the other authors need to know TeX rather than Word. As described above if you are writing with a mathematician, this is not a problem. For biologists though this could be a challenge.

Back to the PhD exam

I actually think that writing a thesis is probably a once-in-a-lifetime chance to understand how Microsoft Word (and EndNote) really works. The candidate explained that she didn’t trust Word enough to do everything right, so her thesis was made of several different documents that were fudged to look like one long thesis. I don’t think this is that unusual. She explained that she had used Word because her supervisor could only use Word and she had wanted to take advantage of the Review tools. Her heart had sunk when her supervisor simply printed out drafts and commented using a red pen, meaning that she could have done it all in LaTeX and it would have been fine.

Conclusion

I have been totally won over by Overleaf. It beats Microsoft Word in so many ways… I’ll stick to Word for grant applications and other non-manuscript documents, but I’m going to keep using it for manuscripts, with the exception of papers written with people who will only use Word.

Reaching Out

Outreach means trying to engage the public with what we are doing in our research group. For me, this mainly means talking to non-specialists about our work and showing them around the lab. These non-specialists are typically interested members of the public and mainly supporters of the charity that funds work in my lab (Cancer Research UK). The most recent batch of activities have prompted this post on doing outreach.

The challenge

Outreach is challenging. Taking part in these events made me realise what a tough job it is to do science communication, and how good the best the communicators are.

There are many ways that an outreach talk is tougher to give than a research seminar. Not least because explaining what we do in the lab can quickly spiral down into a full-on Cell Biology 101 lecture.

A statement like “we work on process x and we are studying a protein called y”, needs to be followed by “jobs in cells are done by proteins”, then maybe “proteins are encoded by genes”, in our DNA, which is a bunch of letters, oh there’s mRNA, ahhh stop! Pretty soon, it can get too confusing for the audience. In a seminar, the level of knowledge is already there, so protein x can be mentioned without worrying about why or how it got there.

On the other hand, giving an outreach talk is much easier than giving a seminar because the audience is already warm to you and they don’t want you to stuff it up. It’s a bit like giving a speech at a wedding.

The challenge is exciting because it means that our work needs to be explained plainly and placed in a bigger context. If you get the chance to explain your work to a lay audience, I recommend you try.

Disarming questions

The big difference between doing a scientific talk for scientists and talking to non-specialists is in the questions. They can be disarming, for various reasons. Here are a few that I have had on recent visits. How would you answer?

Can you tell the difference [down the microscope] between cells from a black person versus those from a white person!?

For context, we had just looked at some HeLa cells down the microscope and I had explained a little bit about Henrietta Lacks and the ethical issues surrounding this cell line.

You mentioned evolution but I think you’ll find that the human cell is just too intricate. How do you think cells are really made?

Hint: it doesn’t matter what you reply. You will be unlikely to change their mind.

Do you dream of being famous? What will be your big discovery?

I’ve also been asked “are we close to a cure for cancer?”. It’s important to temper people’s enthusiasm here I think.

Are you anything to do with [The Crick]? No? Good! It’s a waste of money and it shouldn’t have been built in London!

I had wondered if lay people knew about The Crick, which is now the biggest research institute in the UK. Clearly they have! I tried to explain that The Crick is a chance to merge several institutes that already existed in London and so it would save money on running these places.

Aren’t you just being exploited by the pharmaceutical industry?

This person was concerned that academics generate knowledge which is then commercialised by companies.

My friend took a herbal remedy and it cured his cancer. Why aren’t you working on that?

Like the question rejecting evolution, it is difficult for people to abandon their N-of-one/anecdotal knowledge.

Does X cause cancer?

This is a problem of the media in our country I think. Who seem to be on a mission to categorise everything (red meat, wine, tin foil) into either cancer-causing or cancer-preventing.

As you can see, the questions are wide-ranging, which is unsettling in itself. It’s very different to “have you tried mutating serine 552 to test if the effect is one of general negative charge on the protein?” that you get in a research seminar.

The charity that organises some of the events I’ve been involved in are really supportive and give a list of good ways to answer “typical questions”. However, most questions I get are atypical, and the anticipated questions about animal research or embryo cloning do not arise.

I find it difficult to give a succinct answer to these lay questions. I try to give an accurate reply, but this leads to  long and complicated answer that probably confuses the person even more. I have the same problem with children’s questions, which often get me scurrying to Wikipedia to find the exact answer for “why the sky is blue”. I should learn to just give a vaguely correct answer and not worry about the details so much.

Amazing questions

The best questions are those where you can tell that the person has really got into it. In the last talk I gave, I described “stop” and “go” signals for cell division. One person asked

How does a cell suddenly know that it has to divide? It must get a signal from somewhere… what is that signal?

My initial reply was that asking these sorts of questions is what doing science is all about!

Two more amazing questions:

Is it true that scientists are secretive with their results and think more about advancing their careers than publicising their findings openly to give us value for money?

This was from a supporter of the charity who had read a piece in The Guardian about scientific publishing. She followed up by asking why do scientists put their research behind paywalls. I found this tough to answer because I suddenly felt responsible for the behaviour of the entire scientific community.

You mentioned taxol and the side effects. I was taking that for my breast cancer and it is true what you said. It was very painful and I had to stop treatment.

This was the first time a patient had talked to me about their experience of things that were actually in my talk. This was a stark reminder that the research I am doing is not as abstract as I think. It also made me more cautious about the way I talk about current treatments, since people in the room may be actually taking them!

Good support

With the charity I’ve been to Polo Clubs, hotels, country houses, Bishop’s houses, relay events in public parks. The best part is welcoming people to our lab. These might be a Mayor or people connected wth the city football team, but mainly they are interested supporters of the charity. It’s nice to be able to explain where their money goes and what a life in cancer research is really like.

To do these events, there is a team of people doing all the organisation: inviting participants, sorting out parking, tea and coffee etc. The team are super-enthusiastic and they are really skilled at talking to the public. The events could not go ahead without them. So, a big thank you to them. I’ve also been helped by the folks in the lab and colleagues in my building who have helped to show visitors around and let them see cells down the microscope etc.

Give it a try

Of course there are many other ways to engage the public in our research. This is just focussed on talking to non-scientists and the issues that arise. As I’ve tried to outline here, it’s a fun challenge. If you get the opportunity to do this, give it a try.

The post title comes from “Reaching Out” by Matthew Sweet from his Altered Beast LP. Lovely use of diminished seventh in a pop song and of course the drums are by none other than Mick Fleetwood.

Come To California

I’ve returned from the American Society for Cell Biology 2016 meeting in San Francisco. Despite being a cell biologist and people from my lab attending this meeting numerous times, this was my first ASCB meeting.

cell-biology-2016

The conference was amazing, so much excellent science and so many opportunities to meet up with people. For the areas that I work in: mitosis, cytoskeleton and membrane traffic, the meeting was pretty much made for me. Often there were two or more sessions I could have attended, but couldn’t. I’ll try to summarise some of my highlights.

One of the best talks I saw was from Dick McIntosh, who is a legend of cell biology and is still making outstanding contributions. He showed some new tomography data of growing microtubules in a number of systems which suggest that microtubules have curved protofilaments as they grow. This is in agreement with structural data and some models of MT growth, but not with many other schematic diagrams.

The “bottom-up cell biology” subgroup was one of the first I attended. Organised by Dan Fletcher and Matt Good, the theme was reconstitution of biological systems in vitro. The mix of speakers was great, with Thomas Surrey and Marileen Dogterom giving great talks on microtubule systems, and Jim Hurley and Patricia Bassereau representing membrane curvature reconstitution. Physical principles and quantitative approaches were a strong theme here and throughout the meeting, which reflects where cell biology is at right now.

img_3382

I took part in a subgroup on preprints organised by Prachee Avasthi and Jessica Polka. I will try to write a separate post about this soon. This was a fun session that was also a chance to meet up with many people I had only met virtually. There was a lot of excitement about preprints at the meeting and it seemed like many attendees were aware of preprinting. I guess this is not too surprising since the ASCB have been involved with the Accelerating Science and Publishing in Biology (ASAPbio) group since the start.

Of the super huge talks I saw in the big room, the Cellular Communities session really stood out. Bonnie Bassler and Jurgen Knoblich gave fantastic talks on bacterial quorum sensing and “minibrains” respectively. The Porter Lecture, given by Eva Nogales on microtubule structure was another highlight.

The poster sessions (which I heard were sprawling and indigestible) were actually my favourite part of the meeting. I saw mostly new work here and had the chance to talk to quite a few presenters. My lab took three posters of different projects at various stages of publication (Laura’s work preprinted/in revision project presented by me, Nick’s work soon to submit and Gabrielle’s work soon to write up) and so we were all happy to get some useful feedback on our work. We’ve had follow up emails and requests for collaboration which made the long trip worthwhile. We also had a mini lab reunion with Dan Booth one of my former students who was presenting his work on using 3D Correlative Light Electron Microscopy to examine chromosome structure.

For those that follow me on Twitter, you may know that I like to make playlists from my iTunes library when I visit another city. This was my first time back on the west coast since 2001. Here are ten tracks selected from my San Francisco, CA playlist:

10. California Über Alles – Dead Kennedys from Fresh Fruit For Rotting Vegetables

9. San Franciscan Nights – The Animals from Winds of Change

8. Who Needs the Peace Corps? – The Mothers of Invention from We’re Only In It For The Money

7. San Francisco – Brian Wilson and Van Dyke Parks from Orange Crate Art

6. Going to California – Led Zeppelin from IV

5. Fake Tales of San Francisco – Arctic Monkeys from Whatever People Say I Am, That’s What I’m Not

4. California Hills – Ty Segall from Emotional Mugger

3. The Portland Cement Factory at Monolith California – Cul de Sac from ECIM (OK Monolith is nearer to LA than SF but it’s a great instrumental track).

2. Come to California – Matthew Sweet from Blue Sky on Mars

1. Russian Hill – Jellyfish from Spilt Milk

Before the meeting, I went on a long walk around SF with the guys from the lab and we accidentally found ourselves on Russian Hill.

img_3334

For some reason I have a higher than average number of bootlegs recorded in SF. Television (Old Waldorf 1978), Elliott Smith (Bottom of the Hill, 1998), Jellyfish (Warfield Theater 1993), My Bloody Valentine, Jimi Hendrix etc. etc.

The post title comes from #2 in my playlist

 

Blind To The Truth

Molecular Biology of The Cell, the official journal of the American Society for Cell Biology, recently joined a number of other periodicals in issuing guidelines for manuscripts, concerning statistics and reproducibility. I discussed these guidelines with the lab and we felt that there are two areas where we can improve:

  • blind analysis
  • power calculations

A post about power analysis is brewing, this post is about a solution for blind analysis.

For anyone that doesn’t know, blind analysis refers to: the person doing the analysis being blind to (not knowing) the experimental conditions. It is a way of reducing bias, intentional or otherwise, of analysis of experimental data. Most of our analysis workflows are blinded, because a computer does the analysis in an automated way so there is no way of a human biasing the result. Typically, a bunch of movies are captured, fed into a program using identical settings, and the answer gets spat out. Nothing gets excluded, or the exclusion criteria are agreed beforehand. Whether the human operating the computer is blind or not to the experimental conditions doesn’t matter.

For analysis that has a manual component we do not normally blind the analyser. Instead we look for ways to make the analysis free of bias. An example is using a non-experimental channel in the microscope image to locate a cellular structure. This means the analysis is done without “seeing” any kind of “result”, which might bias the analysis.

Sometimes, we do analysis which is almost completely manual and this is where we can improve by using blinding. Two objections raised to blinding are practical ones:

  • it is difficult/slow to get someone else to do the analysis of your data (we’ve tried it and it doesn’t work well!)
  • the analyser “knows” the result anyway, in the case of conditions where there is a strong effect

There’s not much we can do about the second one. But the solution to the first is to enable people to blindly analyse their own data if it is needed.

I wrote* a macro in ImageJ called BlindAnalysis.ijm which renames the files in a random fashion** and makes tsv log of the associations. The analyser can simply analyse blind_0001.tif, blind_0002.tif and then reassociate the results to the real files using this tsv.

blindanalysis

The picture shows the macro in action. A folder containing 10 TIFFs is processed into a new folder called BLIND. The files are stripped of labels (look at the original TIFF, left and the blind version, right) and saved with blinded names. The log file keeps track of associations.

I hope this simple macro is useful to others. Feedback welcome either on this post or on GitHub.

* actually, I found an old macro on the web called Shuffler written by Christophe Leterrier. This needed a bit of editing and also had several options that weren’t needed, so it was more deleting than writing.

** it also strips out the label of the file. If you only rename files, the label remains in ImageJ so the analyser is not blind to the condition. Originally I was working on a bash script to do the renaming, but when I realised that I needed to strip out the labels, I needed to find an all-ImageJ solution.

Edit @ 2016-10-11T06:05:48.422Z I have updated the macro with the help of some useful suggestions.

The post title is taken from “Blind To The Truth” a 22 second-long track from Napalm Death’s 2nd LP ‘From Enslavement To Obliteration.

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)], 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…

https://royalsociety.org/topics-policy/publications/2010/scientific-century/
https://royalsociety.org/topics-policy/publications/2010/scientific-century/

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 Arcane Model

I’m currently writing two manuscripts that each have a substantial data modelling component. Some of our previous papers have included computer code, but it was straightforward enough to have the code as a supplementary file or in a GitHub repo and leave it at that. Now with more substantial computation in the manuscript, I was wondering how best to describe it. How much detail is required?

How much explanation should be in the main text, how much is in supplementary information and how much is simply via commenting in the code itself?

I asked for recommendations for excellent cell biology papers that had a modelling component, where the computation was well described.

I got many replies and I’ve collated this list of papers below so that I can refer to them and in case it is useful for anyone who is also looking for inspiration. I’ve added the journal names only so that you can see what journals are interested in publishing cell biology with a computational component. Here they are, in no particular order:

  • This paper on modelling kinetochore-microtubule attachment in pombe. Published in JCB there is also a GitHub repo for the software, kt_simul written in Python. The authors used commenting and also put a PDF of the heavy detail on GitHub.
  • Modelling of signalling networks here in PLoS Comput Biol.
  • This paper using Voronoi tesselations to examine tissue packing of cells in EMBO J.
  • Two papers, this one in JCB featuring modelling of DNA repair and this one in Curr Biol on photoreceptors in flies.
  • Cell movements via depletion of chemoattractants in PLos Biol.
  • Protein liquid droplets as organising centres for biochemical reactions is a hot topic. This paper in Cell was recommended.
  • Final tip was to look at PLoS Comput Biol for inspiration, searching for cell biology topics. Papers like this one on Smoldyn 2.1.

Thanks to Hadrien Mary, Robert Insall, Joachim Goedhart, Stephen Floor, Jon Humphries, Luis Escudero, and Neil Saunders for the suggestions.

The post title is taken from “The Arcane Model” by The Delgados from their album Peloton.

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

(Normal)

Measurement

(not Normal)

Binomial

 

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.

Correlation

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.