Undocumented MATLAB has an in depth look at the next generation graphics handler for MATLAB which you can use today, although it’s not officially released yet. Use the command line option “-hgVersion 2″ when launching MATLAB. See the post for more details.
Posts tagged with analysis
Two-Photon Processor and SeNeCA – A freely available software package to process data from two-photon calcium imaging at speeds down to several ms per frame.
Jakub Tomek, Ondrej Novak, and Josef Syka
TJ Neurophysiol published 10 April 2013, 10.1152/jn.00087.2013
It’s notable in that it is an “all-in-one” package that’s freely available.
The image processing to detect cells and draw ROIs seems to work pretty good, even with poor S:N. I’d like to see it operate on GCaMP images, since those are more challenging in some ways. Cells labeled with Oregon Green BAPTA-1 tend to exhibit spherical patterns of somatic fluorescence, but GCaMP, when it’s working well, does not brightly label the nucleus, so the shape of the ideal ROI is quite different. Plus, it’s nice to pick up dendrites and other features, not just somata.
Hat tip to Christian Wilms
EDIT: The code became available shortly after this post.
If you like Python, want to analyze data online, and are interested in a standardized environment that can be easily shared, read on:
If you’re an academic frustrated by setting up computing environments and annoyed that your colleagues can’t easily run your code, Wakari is made for you. Wakari handles all of the problems related to setting up a Python scientific computing environment. Because Wakari builds on Anaconda, useful libraries like SciKit, mpi4py and NumPy are right at your fingertips without compilation gymnastics.
Since you run code on our servers through a web browser, it is easy for your colleagues to re-run your code to repeat your analysis, or try out variations on their own. At Continuum, we understand that reproducibility is an important part of the scientific process that your results be consistent for reviewers and colleagues.
If you’d like to read some deep, wide-ranging philosophical discussions about R, an open statistics software package, then I wonder why we’re friends. But since we are, here are some links, you psychopath:
“R is a programming language missing a GUI”
“R is really important to the point that it’s hard to overvalue it”
“…it’s object oriented rather than data record oriented…”
If you just want to get on with it, and run stats on your data, then keep reading.
I like R and have used it in a lot of my work. I recommend it. However, it is all command-line based (there are GUIs that can be applied to R, but I haven’t tried them, here’s one). As we all know, not everyone is spellbound by command-line interfaces. SOFA is an open statistics software package with a well thought out GUI, including database interface, chart generation, and more.
Hat tip to CSH.
GIMP is an open source image editing program. You can use it for a lot of the same things Adobe Photoshop is used for.
They just released version 2.8 (release notes).
In addition to GIMP, be sure to keep up with Inkscape, an open source vector graphics/illustration program. You might be able to do without Adobe Illustrator better than you think.
You can even typeset math equations using TeX. (link)
Both GIMP and Inkscape are available for Windows, Mac OSX, and Linux.
Nature just published a 4-page perspective article on the important of releasing the source code for programs used in scientific research. The authors emphasize the importance of reproducibility for results that depend on computation.
Labrigger has already covered releasing source code here, including the sympathetic CRAPL. In that license, it is acknowledged that the code offered is not pretty and no promise of support is offered (in fact, quite the opposite is promised). These are among the key concerns about releasing code. Perhaps the software relies on expensive, propriatery hardware and/or software. Perhaps the code is buggy as hell, thread hostile, devoid of error handling, and relies on core dumps as the main data output interface. Or perhaps the source code is poorly commented, or not commented at all, and all variable names are single characters. Perhaps the code is written in Whitespace with inline Brainfuck.
The authors spend a long time explaining that there is no substitute for releasing the source code. That is, pseudo code, mathematical, or natural language descriptions are never enough. Of course they’re right in principle, but I appreciate the alternative descriptions sometimes, so I wouldn’t want a source code release to replace those alternative descriptions. For example, I don’t want to have to sift through someone’s crap code just to find how they performed a specific bootstrap analysis. The description in the methods section should be sufficiently clear and detailed so that I can code it up myself.
One point the authors make is that errors can be detected when the entire source code is released. Sometimes, even commercial programs have bugs that change results. E.g., GraphPad had a rather unfortunate bug that resulted in data groups being flagged as significantly different when they weren’t.
OpenElectrophy is a somewhat mature, actively developed, open source program for analyzing data from multielectrode recordings. It makes heavy use of Python and SQL and is developed by a team of French researchers including Samuel Garcia, Nicolas Fourcaud-Trocmé, Stéphane Gaétan Roux, and Christophe Pouzat.
It handles many file formats.
And has a rich set of tools for spike sorting.