CellProfiler Analyst: interactive data exploration, analysis and classification of large biological image sets

Abstract

Summary: CellProfiler Analyst allows the exploration and visualization of image-based data, together with the classification of complex biological phenotypes, via an interactive user interface designed for biologists and data scientists. CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery). Availability and Implementation: CellProfiler Analyst 2.0 is free and open source, available at http://www.cellprofiler.org and from GitHub (https://github.com/CellProfiler/CellProfiler-Analyst) under the BSD license. It is available as a packaged application for Mac OS X and Microsoft Windows and can be compiled for Linux. We implemented an automatic build process that supports nightly updates and regular release cycles for the software. Contact: anne@broadinstitute.org Supplementary information: Supplementary data are available at Bioinformatics online.

Multidimensional Profiling of Drug-Treated Cells by Imaging Mass Cytometry

“…Spatial distribution maps, dimensionality reduction and unsupervised clustering for 16-bit single images were performed using the histoCAT 1.73 open source toolbox [21]. Supervised analysis by support vector machine learning classification on 32-bit datasets was processed with CellProfiler Analyst 2.2.1 [38]. Hierarchical clustering and correlation heat maps of classified cell populations were prepared using the webbased tool Morpheus (GenePattern, Broad Institute) [39].…”

An open-source solution for advanced imaging flow cytometry data analysis using machine learning

“…It includes machine learning tools for identifying complex and subtle phenotypes [28]. CellProfiler Analyst has recently been updated to include multi-class classification, and it now offers a variety of supervised machine learning techniques [29].…”

Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline

“…Open‐source artificial intelligence (AI) software libraries such as Keras and TensorFlow, enable the power of neural networks and AI for the analysis of the datasets. Data inspection interfaces are built to inspect the results of such automated methods …”

“…[25,26] While these tools allow us to acquire substantially more data, it is still incumbent on the scientist to transform data into useful and actionable information. Manual image processing is unfeasible for those datasets, and automated data analysis techniques and methods are continuously being developed, [27][28][29][30][31][32][33] even more now with the advent of machine learning tools, [34][35][36][37][38][39][40] that often perform on par with human observers. Open-source artificial intelligence (AI) software libraries such as Keras [41,42] and TensorFlow, [43] enable the power of neural networks and AI for the analysis of the datasets.…”

“…Data inspection interfaces are built to inspect the results of such automated methods. [36,44] Beyond image processing, data visualization algorithms are being used to extract information from high-dimensional datasets. [45][46][47][48][49] Reusable and reproducible software built over open-source software such as python [50] and R, [51] make the development of software in repositories based on Git and GitHub commonplace.…”