ArticleNature communications2025
A versatile information retrieval framework for evaluating profile strength and similarity.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- A host-centric morphological profiling approach to identify repurposed antiviral drugs.iScience · 2026Article
- Retrieval-Based Evaluation of Cell Painting Feature Spaces Reveals Differences in the Preservation of Biologically Meaningful Phenotypic Similarity.International journal of molecular sciences · 2026Article
- ExPO: an exposure-conditioned neural operator for L1000 signature prediction.Journal of cheminformatics · 2026Article
- Manifold lateralisation and variability in the language connectome at 7T.Research square · 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
- Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships.Communications biology · 2026Article
- A self-supervised machine learning pipeline for extracting information from live cell images at multiple doses and timepoints.Scientific reports · 2026Article
- Alternate dyes for image-based profiling assays.SLAS discovery : advancing life sciences R & D · 2025Article
- Prediction of cellular morphology changes under perturbations with a transcriptome-guided diffusion model.Nature communications · 2025Article
- Article
- Morphological map of under- and overexpression of genes in human cells.Nature methods · 2025Article
- Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning.Nature communications · 2025Article
- Live-cell painting: Image-based profiling in live cells using acridine orange.Molecular biology of the cell · 2025Article
- A versatile information retrieval framework for evaluating profile strength and similarity.Nature communications · 2025Article
- A morphology and secretome map of pyroptosis.Molecular biology of the cell · 2025Article
- A genome-wide atlas of human cell morphology.Nature methods · 2025Article
- Evaluating batch correction methods for image-based cell profiling.Nature communications · 2024Article
- Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning.bioRxiv : the preprint server for biology · 2024Article
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12 authors.
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Abstract
Large-scale profiling assays capture a cell population's state by measuring thousands of biological properties per cell or sample. However, evaluating profile strength and similarity remains challenging due to the high dimensionality and non-linear, heterogeneous nature of measurements. Here, we develop a statistical framework using mean average precision (mAP) as a single, data-driven metric to address this challenge. We validate the mAP framework against established metrics through simulations and real-world data, revealing its ability to capture subtle and meaningful biological differences in cell state. Specifically, we use mAP to assess a sample's phenotypic activity relative to controls, as well as the phenotypic consistency of groups of perturbations (or samples). We evaluate the framework across diverse datasets and on different profile types (image, protein, mRNA), perturbations (CRISPR, gene overexpression, small molecules), and resolutions (single-cell, bulk). The mAP framework, together with our open-source software package copairs, is useful for evaluating high-dimensional profiling data in biological research and drug discovery.
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