ArticleCommunications biology2022
A statistical framework for high-content phenotypic profiling using cellular feature distributions.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- A High-Content Imaging Pipeline to Investigate Subcytotoxic Effects in RTgill-W1 Cells.Environmental science & technology · 2026Article
- Image-based, pooled phenotyping reveals multidimensional, disease-specific variant effects.Cell · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Single-cell hit calling in high-content imaging screens with Buscar.bioRxiv : the preprint server for biology · 2026Article
- Quality Control Measures and Statistical Strategies to Address the Challenges of High-Content Phenotypic Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Article
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- A new class of natural anthelmintics targeting lipid metabolism.Nature communications · 2025Article
- Reporter-expressing viruses for antiviral drug discovery research.Frontiers in cellular and infection microbiology · 2025Review
- Article
- SPACe: an open-source, single-cell analysis of Cell Painting data.Nature communications · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-content screening (HCS) uses microscopy images to generate phenotypic profiles of cell morphological data in high-dimensional feature space. While HCS provides detailed cytological information at single-cell resolution, these complex datasets are usually aggregated into summary statistics that do not leverage patterns of biological variability within cell populations. Here we present a broad-spectrum HCS analysis system that measures image-based cell features from 10 cellular compartments across multiple assay panels. We introduce quality control measures and statistical strategies to streamline and harmonize the data analysis workflow, including positional and plate effect detection, biological replicates analysis and feature reduction. We also demonstrate that the Wasserstein distance metric is superior over other measures to detect differences between cell feature distributions. With this workflow, we define per-dose phenotypic fingerprints for 65 mechanistically diverse compounds, provide phenotypic path visualizations for each compound and classify compounds into different activity groups.
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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.