Software information

Accessing source code

iCLOTS software source code and standalone methods are freely available.

All code is licensed under the Apache License 2.0, a standard open-source license. Our project github account also includes a code of conduct and contributing information, files standard to open-source projects.

Version 0.1.1 is being released as a beta version, a version of a piece of software that is made available for testing, typically by a group of users outside the development team, before a formal release. As such, your feedback is especially valuable. As iCLOTS continues to grow, all versions of iCLOTS will be maintained at the project website.

iCLOTS is built for 64-bit operating systems and requires at least 8 GB of RAM, with more suggested for larger datasets.

While we have extensively tested iCLOTS on several machines, as with any software, operational errors (“bugs”) may still be present. Users can contact us for prompt resolution by (1) filling out the contact form, (2) emailing the development team directly at help@iclots.org, or (3) raising an issue in GitHub, which is particularly useful for users with computational experience. The development team is dedicated to resolving any issues promptly.

Open source packages utilized

iCLOTS is a Python-based software built upon many successful open-source packages, including:

• Numpy

• Pandas

• OpenCV

• scikit-image

• scikit-learn

• Trackpy

• Matplotlib

• Seaborn

• pyinstaller

The iCLOTS development team thanks the authors of these projects. Descriptions of each application reference which Python libraries were used to implement image processing and machine learning algorithms. Please consider citing these resources as well when publishing iCLOTS-generated data.

Other excellent software projects

The iCLOTS development team would also like to acknowledge several open-source software products that served as an inspiration and guide during the creation of this project. Depending on your analysis goals, you might find these pieces of software more suitable for your own analysis:

• ilastik, useful for advanced image segmentation (ilastik website, appropriate citation: Berg S, Kutra D, Kroeger T, et al. ilastik: interactive machine learning for (bio)image analysis. Nature Methods. 2019;16(12):1226-1232.)

• CellProfiler, useful for advanced cell morphology metrics (CellProfiler website, appropriate citations: Lamprecht MR, Sabatini DM, Carpenter AE. CellProfiler: free, versatile software for automated biological image analysis. Biotechniques. 2007;42(1):71-75. and/or Stirling DR, Swain-Bowden MJ, Lucas AM, Carpenter AE, Cimini BA, Goodman A. CellProfiler 4: improvements in speed, utility and usability. BMC Bioinformatics. 2021;22(1):433.)

• ImageJ and FIJI, strong multi-purpose image analysis toolkits (ImageJ website, recent citation: Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nature Methods. 2012;9(7):671-675.)

Data availability

A limited set of test data for every application is available on github and our website (located under software files). This set of test data is designed to demonstrate software capabilities using real-world data.

All data used within our pending manuscript is also available without restriction upon request to corresponding author Wilbur Lam, MD, PhD (wilbur.lam@emory.edu) or to our development team (help@iclots.org).

Detailed experimental protocols and microfluidic device mask files for any data presented are also available upon request.