ArticleBioinformatics (Oxford, England)2022
Systematic replication enables normalization of high-throughput imaging assays.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Growth factors maintain intratumoral heterogeneity and drive therapeutic resistance in triple-negative breast cancer.Cell reports · 2026Article
- Identification of senescence-associated drivers of tumour growth and progression using a novel microarray platform.bioRxiv : the preprint server for biology · 2025Article
- GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.Bioinformatics (Oxford, England) · 2024Article
- GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.bioRxiv : the preprint server for biology · 2023Article
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5 authors.
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Abstract
motivationHigh-throughput fluorescent microscopy is a popular class of techniques for studying tissues and cells through automated imaging and feature extraction of hundreds to thousands of samples. Like other high-throughput assays, these approaches can suffer from unwanted noise and technical artifacts that obscure the biological signal. In this work, we consider how an experimental design incorporating multiple levels of replication enables the removal of technical artifacts from such image-based platforms.
resultsWe develop a general approach to remove technical artifacts from high-throughput image data that leverages an experimental design with multiple levels of replication. To illustrate the methods, we consider microenvironment microarrays (MEMAs), a high-throughput platform designed to study cellular responses to microenvironmental perturbations. In application to MEMAs, our approach removes unwanted spatial artifacts and thereby enhances the biological signal. This approach has broad applicability to diverse biological assays. AVAILABILITY AND IMPLEMENTATION: Raw data are on synapse (syn2862345), analysis code is on github: gjhunt/mema_norm, a reproducible Docker image is available on dockerhub: gjhunt/mema_norm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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