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Proc Natl Acad Sci U S A DOI:10.1073/pnas.0808843106

Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning.

Publication TypeJournal Article
Year of Publication2009
AuthorsJones, TR, Carpenter, AE, Lamprecht, MR, Moffat, J, Silver, SJ, Grenier, JK, Castoreno, AB, Eggert, US, Root, DE, Golland, P, Sabatini, DM
JournalProc Natl Acad Sci U S A
Date Published2009 Feb 10
KeywordsAlgorithms, Animals, Artificial Intelligence, Cells, Diagnostic Imaging, Feedback, Humans, Image Cytometry, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Phenotype, RNA Interference, Tissue Array Analysis

Many biological pathways were first uncovered by identifying mutants with visible phenotypes and by scoring every sample in a screen via tedious and subjective visual inspection. Now, automated image analysis can effectively score many phenotypes. In practical application, customizing an image-analysis algorithm or finding a sufficient number of example cells to train a machine learning algorithm can be infeasible, particularly when positive control samples are not available and the phenotype of interest is rare. Here we present a supervised machine learning approach that uses iterative feedback to readily score multiple subtle and complex morphological phenotypes in high-throughput, image-based screens. First, automated cytological profiling extracts hundreds of numerical descriptors for every cell in every image. Next, the researcher generates a rule (i.e., classifier) to recognize cells with a phenotype of interest during a short, interactive training session using iterative feedback. Finally, all of the cells in the experiment are automatically classified and each sample is scored based on the presence of cells displaying the phenotype. By using this approach, we successfully scored images in RNA interference screens in 2 organisms for the prevalence of 15 diverse cellular morphologies, some of which were previously intractable.


Alternate JournalProc. Natl. Acad. Sci. U.S.A.
PubMed ID19188593
PubMed Central IDPMC2634799
Grant ListR01 AI047389 / AI / NIAID NIH HHS / United States
RL1 CA133834-02 / CA / NCI NIH HHS / United States
DK070069-01 / DK / NIDDK NIH HHS / United States
R01 GM0725555 / GM / NIGMS NIH HHS / United States
T90 DK070069 / DK / NIDDK NIH HHS / United States
RL1 CA133834 / CA / NCI NIH HHS / United States