Have you ever stumbled across some amazing data visualization tools that run entirely on a web browser (such as this and many others), and wished you could plug in your own data and visualize it? Or, as a biologist, you may know of a good analytic tool, but it either costs too much, requires programming expertise, or requires bundled installations of many other dependencies that might not be compatible with your system… And then you spend more time fixing the tool, than using it. In this blog post, we share how to use browser-based applications and perform tasks in multivariate data analysis and image processing to visualize data (like the one below!), with much less hassle.Continue reading
Happy holidays to everyone- we here at the CellProfiler team got you a little end-of-year treat in the form of CellProfiler 3.1.8. This is primarily a bugfix release, getting rid of some bugs in MeasureObjectIntensity, MeasureColocalization, ExportToSpreadsheet, CorrectIlluminationCalculate, and Smooth. We’ve also updated how we package for Windows, so those of you who had JAVA_HOME issues with 3.1.5 (feel free to now unset that in your environment variables if it’s set to your 3.1.5 install!) should now experience much smoother sailing. As usual, you will find this new release (and links to all our old releases) on our releases page.
Thanks very much to Allen Goodman, Matthew Bowden, Vito Zanotelli, and Christian Clauss for their contributions on this release.
On behalf of the whole CellProfiler team, may the season treat you well, and we wish you a happy end of 2018 and beginning of 2019!
Clearly communicating the impact of your research is one of the most important skills you need to develop as a scientist, and yet typically it is only taught by doing (and if you are lucky, feedback – especially critical feedback). Clear communication is important to get funding and resources for your work, to publish it, to entice collaborators, to impress colleagues and supervisors, … and to not be boring at parties when asked “So what do you do?”
You’ve earned degrees, authored papers, mentored supervisees, and traveled far and wide to speak about your work… And ideally it’s nicely showcased in your resume or curriculum vitae (CV), all updated and ready to go. But, if you’re like most academics, your CV is a sorely outdated PDF and upon its request, you always find yourself scrambling to dig up recent accomplishments to prove you’ve not just been lounging around for the last 6 months (or years). And updating it requires locating an elusive latest version of a Word doc, editing HTML on your lab website, or compiling and PDF-ifying your LaTeX file. Continue reading
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.
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!
As the PI of the Carpenter lab (a.k.a. Broad Institute Imaging Platform, including the CellProfiler team), people often ask how I manage so many ongoing collaborations: we discuss 50+ external projects each year so it is a lot to track! I am happy to reveal our secrets. Continue reading
Annotated image data is valuable for assessing the performance of an image processing pipeline and as training data for machine learning methods such as deep learning. When assessing the performance of a CellProfiler pipeline, for example a pipeline that segments nuclei, the annotated image data are used as the ground truth. The performance of the pipeline can be quantified by comparing the segmentation output to the ground truth and calculating a comparison metric, such as the Jaccard Index or F1 Score. Annotated images are also essential for deep learning applications as training data, for example see the 2018 Data Science Bowl; an in-depth discussion on how the Data Science Bowl images were annotated can be found on the Kaggle forum. Continue reading
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.