I’m excited to announce the release of CellProfiler 3.1.
Our focus for CellProfiler 3.1 was polishing features and squashing bugs introduced in CellProfiler 3.0. We also started laying down the foundation for our next release, CellProfiler 4.0, that will transition CellProfiler from Python 2 to Python 3, improve multiprocessing, and overhaul the interface.
There’re a few noteworthy changes that some users might enjoy like UTF-8 pipeline encoding, a simpler application bundle (that won’t require installing Java), and a variety of documentation improvements.
You can download CellProfiler 3.1 from the cellprofiler.org website. If you have feedback or questions, please let us know on the CellProfiler Forum message one of us on Twitter.
Of course this would not have been possible without the hard work of our software engineers and all our contributors- Allen Goodman, Claire McQuin, Matthew Bowden, Vasiliy Chernyshev, Kyle Karhohs, Jane Hung, Chris Allan, Vito Zanotelli, Carla Iriberri, and Christoph Moehl, take a bow!
For those of you who’ve been with us for a long time though, the obvious next question after how to use the new test mode is will my old CellProfiler pipelines work in the new version? We feel the same way – the pipelines you’ve accumulated over the years are precious resources! The good and bad news is that the answer is Yes, mostly. In order to facilitate the speedup and continue the process of streamlining the code, a few things had to go; we also removed some things we felt were causing “option fatigue” for the sake of user friendliness going forward.
Everyone here at the CellProfiler team is very excited about our new 3.0 release, and we certainly hope you are too! CellProfiler 3.0 is much faster than any of our previous releases, and the addition of volumetric processing is a huge game changer. Continue reading
Double clicking on the output images produced by CellProfiler sometimes opens up a screen in your operating system’s default image viewer that looks all black. This can make it seem like your pipeline didn’t work or didn’t produce the right output. However, this can happen for a couple of reasons:
(a) If you’re exporting objects and have only a few objects in your image
(b) If you’re exporting 16-bit images
Defining the input to CellProfiler can be the hardest part of getting your pipeline set up and your analysis underway. Incoming images are configured in the first 4 modules of CellProfiler – Images, Metadata, NamesAndTypes, and Groups – which offer lots of flexibility. But it’s sometimes confusing what each one does, and it’s not always obvious which ones you need for your experiment. Continue reading
There’s nothing more exciting than getting back a big batch of data from your automated microscope – finally, you have the results of your screen, your timelapse, or whatever you’ve spent the last weeks or months preparing. That excitement can turn to sadness quickly though when you realize that neither your laptop nor the old general-use computer in the lab are up to analyzing thousands (or tens of thousands, or hundreds of thousands!) of images. But, congratulations! You’ve reached an elite level of CellProfiler users when you outgrow processing on a single local computer. Continue reading
It can be confusing when you’re trying to set up your first pipeline to figure out which modules to use to generate your objects! A helpful way to understand the difference between Identifying Primary, Secondary, and Tertiary objects: Continue reading