ArticleSLAS discovery : advancing life sciences R & D2019
Unbiased Phenotype Detection Using Negative Controls.
Article in SLAS discovery : advancing life sciences R & D, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Impact of regularization methods and outlier removal on unsupervised sample classification.BMC bioinformatics · 2026Article
- A machine learning-based analysis method for small molecule high content screening of three-dimensional cancer spheroid morphology.Molecular pharmacology · 2025Article
- Cell Painting: a decade of discovery and innovation in cellular imaging.Nature methods · 2025Review
- Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning.PLoS computational biology · 2024Article
- Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning.bioRxiv : the preprint server for biology · 2024Article
- A Decade in a Systematic Review: The Evolution and Impact of Cell Painting.bioRxiv : the preprint server for biology · 2024Article
- Article
- The Academic Pill: How Academia Contributes to Curing Diseases.SLAS discovery : advancing life sciences R & D · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phenotypic screens using automated microscopy allow comprehensive measurement of the effects of compounds on cells due to the number of markers that can be scored and the richness of the parameters that can be extracted. The high dimensionality of the data is both a rich source of information and a source of noise that might hide information. Many methods have been proposed to deal with this complex data in order to reduce the complexity and identify interesting phenotypes. Nevertheless, the majority of laboratories still only use one or two parameters in their analysis, likely due to the computational challenges of carrying out a more sophisticated analysis. Here, we present a novel method that allows discovering new, previously unknown phenotypes based on negative controls only. The method is compared with L1-norm regularization, a standard method to obtain a sparse matrix. The analytical pipeline is implemented in the open-source software KNIME, allowing the implementation of the method in many laboratories, even ones without advanced computing knowledge.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.