Anomaly detection for high-content image-based phenotypic cell profiling

High-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cell's physiological state. Classical representations of the phenotypic profile can not capture the full underlying complex...

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Bibliographic Details
Published in:	bioRxiv
Main Authors:	Shpigler, Alon, Kolet, Naor, Golan, Shahar, Weisbart, Erin, Zaritsky, Assaf
Format:	Journal Article Paper
Language:	English
Published:	United States Cold Spring Harbor Laboratory 03.06.2024
Edition:	1.1
Subjects:	Bioinformatics
ISSN:	2692-8205, 2692-8205
Online Access:	Get full text
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Summary:	High-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cell's physiological state. Classical representations of the phenotypic profile can not capture the full underlying complexity in cell organization, while recent weakly machine-learning based representation-learning methods are hard to biologically interpret. We used the abundance of control wells to learn the in-distribution of control experiments and use it to formulate a self-supervised reconstruction anomaly-based representation that encodes the intricate morphological inter-feature dependencies while preserving the representation interpretability. The performance of our anomaly-based representations was evaluated for downstream tasks with respect to two classical representations across four public Cell Painting datasets. Anomaly-based representations improved reproducibility, Mechanism of Action classification, and complemented classical representations. Unsupervised explainability of autoencoder-based anomalies identified specific inter-feature dependencies causing anomalies. The general concept of anomaly-based representations can be adapted to other applications in cell biology.
Bibliography:	ObjectType-Working Paper/Pre-Print-3 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Competing Interest Statement: The authors have declared no competing interest.
ISSN:	2692-8205 2692-8205
DOI:	10.1101/2024.06.01.595856