CellProfiler Analyst: data exploration and analysis software for complex image-based screens

By Thouis Raymond Jones, In Han Kang, D. Wheeler, Robert A Lindquist, Adam Papallo, David M. Sabatini, Polina Golland, Anne E Carpenter. BMC Bioinformatics, 2008

560 citations recorded by the source corpus.

BackgroundImage-based screens can produce hundreds of measured features for each of hundreds of millions of individual cells in a single experiment.ResultsHere, we describe CellProfiler Analyst, open-source software for the interactive exploration and analysis of multidimensional data, particularly data from high-throughput, image-based experiments.ConclusionThe system enables interactive data exploration for image-based screens and automated scoring of complex phenotypes that require combinations of multiple measured features per cell.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2e14d5736174492b5a6c2e17bc11407e27f367b8

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Citations in the corpus

Visualization of image data from cells to organisms (2010, unknown): “Several software tools offered by screening-oriented microscope companies enable certain data visualizations ( Table 3 ), as does third-party software such as Cellenger and the open-source CellProfiler projec”
Basic Image Analysis and Manipulation in ImageJ (2013, methodology): “Images are acquired with a high-resolution imaging instrument for rapid capture of cell images coupled to the extraction of detailed information using software platforms like CellProﬁler (Carpenter et al., 2006; Jones et al., 2008).”
Computer vision for high content screening (2016, methodology): “…classify cellular phenotypes include support vector machines (SVM) (Chong et al ., 2015; Fuchs et al ., 2010; Held et al ., 2010; Loo et al ., 2007), adaptive boosting (Jones et al ., 2008), random forests (Kaynig et al ., 2010), and neural networks (Boland & Murphy, 2001; Horvath et al ., 2011).”
Machine learning in cell biology – teaching computers to recognize phenotypes (2013, methodology): “CellProfiler Analyst provides a multi-class active learning interface based on boosting.”
Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning (2009, methodology): “Previously, 288 genes were screened for a metaphase phenotype by RNAi in Drosophila by using living-cell microarrays (33).”