ScienceSnippets: Building communication skills and sharing what you love

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?”

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Tricks for maintaining your CV/resume with Google Docs: easy to edit, immediately published

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 But, if you’re like most academics, your CV is a sorely outdated PDF and upon its request, you always find yourself scrambling to dig up recent accomplishments to prove you’ve not just been lounging around for the last 6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

Announcing CellProfiler 3.1

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!

Annotating Images with CellProfiler and GIMP

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

The CellProfiler 2 User’s Guide to CellProfiler 3.0, Part II: Converting your pipelines

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.

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Z-score big! A game plan for image-based profiling

As described in our lab’s recent review article, there are so many great reasons to use microscopy images to create signatures of perturbations: identifying phenotypes associated with disease, identifying chemical mechanisms of action, and discovering gene functions, among others. Have you wanted to give this approach a try, but been overwhelmed by the computational options available? Continue reading

Mo Data, Mo Problems: How to share big data with ease

# Sharing Image Data

At the Imaging Platform we frequently need to send and receive images between collaborators or forum users. Working with images can become challenging simply due to their file size. Memory and disk limitations are often irritants when analyzing images, but in some cases, such as multi-dimensional images and whole-slide scans, images’ file sizes can be large enough that even accessing images becomes a major roadblock, especially when the size starts to rival the size of desktop or laptop hard drives. Continue reading