ArticleNature communications2018
Exploring patterns enriched in a dataset with contrastive principal component analysis.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
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Who cites it
82 citing papers in PubMed.
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- An interpretable learning framework for exploring superelastic degradation of NiTi shape memory alloys using multimodal data.Nature communications · 2026Article
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- Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study.Circulation · 2026Article
- Linking functional and structural dendritic spine remodeling during fear learning and extinction in vivo.Science advances · 2026Article
- Independence and coherence in temporal sequence computation across the fronto-parietal network.Nature communications · 2026Article
- Article
- Contrastive Dimension Reduction: A Systematic Review.Wiley interdisciplinary reviews. Computational statistics · 2026Article
- Contrastive Latent Functional Model.Statistics in biosciences · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Systematic background selection with BasCoD enhances contrastive dimension reduction in single cell genomics.Nature communications · 2026Article
- Parsing Neurometabolic Signatures of Multiple Sclerosis with MRSI and cPCA.medRxiv : the preprint server for health sciences · 2026Article
- Joint modeling of cellular heterogeneity and condition effects with scPCA in single-cell RNA-seq.Communications biology · 2026Article
- Quantifying pathological progression from single-cell transcriptomic data with scPSS.Genome research · 2026Article
- Joint modelling of brain and behaviour dynamics with artificial intelligence.Nature reviews. Neuroscience · 2026Review
- Contrastive independent component analysis for salient patterns and dimensionality reduction.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- CoCo-ST detects global and local biological structures in spatial transcriptomics datasets.Nature cell biology · 2025Article
- Deep Learning of Brain-Behavior Dimensions Identifies Transdiagnostic Biotypes in Youth with ADHD and Anxiety Disorders.bioRxiv : the preprint server for biology · 2025Article
- Multimodal Neuroimaging-Guided Stratification in Amyotrophic Lateral Sclerosis Reveals Three Disease Subtypes: A Multi-Cohort Analysis.Human brain mapping · 2025Article
- Response to comment on "Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA".PLoS computational biology · 2025Article
22 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Visualization and exploration of high-dimensional data is a ubiquitous challenge across disciplines. Widely used techniques such as principal component analysis (PCA) aim to identify dominant trends in one dataset. However, in many settings we have datasets collected under different conditions, e.g., a treatment and a control experiment, and we are interested in visualizing and exploring patterns that are specific to one dataset. This paper proposes a method, contrastive principal component analysis (cPCA), which identifies low-dimensional structures that are enriched in a dataset relative to comparison data. In a wide variety of experiments, we demonstrate that cPCA with a background dataset enables us to visualize dataset-specific patterns missed by PCA and other standard methods. We further provide a geometric interpretation of cPCA and strong mathematical guarantees. An implementation of cPCA is publicly available, and can be used for exploratory data analysis in many applications where PCA is currently used.
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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.