ArticleProceedings of the National Academy of Sciences of the United States of America2023
Phantom oscillations in principal component analysis.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Data Analysis Planning and Reporting for Confirmatory Multi-Lab Preclinical Trials: A Tutorial.Biometrical journal. Biometrische Zeitschrift · 2026Review
- Multivariate Analysis of Linear and Nonlinear Optical Properties in Purine Derivatives: A Predictive Framework from One-Photon Absorption Spectra.The journal of physical chemistry. A · 2026Article
- A Bayesian Causal Model for Matrix-Valued Exposures With Applications to Radiotherapy Planning.Statistics in medicine · 2026Article
- Article
- Fifteen years on: a review of the Cam-CAN study of the cognitive neuroscience of ageing.GeroScience · 2026Review
- Continuous input drives motor cortical dynamics during reaching.Research square · 2026Article
- Comparative transcriptomics reveal the common anteroposterior molecular blueprint of adult bilaterian guts.PLoS biology · 2026Article
- Single-shot hyperspectral wavefront imaging.Nature communications · 2025Article
- Altricial brains and the evolution of infant vocal learning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Investigating the complex cortical dynamics of an advanced concentrative absorption meditation called jhanas (ACAM-J): a geometric eigenmode analysis.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Individual-specific strategies inform category learning.Scientific reports · 2025Article
- Individual-specific strategies inform category learning.bioRxiv : the preprint server for biology · 2025Article
- Perinatal development of structural thalamocortical connectivity.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Lattice physics approaches for neural networks.iScience · 2024Review
- Pupillary manifolds: uncovering the latent geometrical structures behind phasic changes in pupil size.Scientific reports · 2024Article
- Scoping Review: Methods and Applications of Spatial Transcriptomics in Tumor Research.Cancers · 2024Article
- Multi-omics integration for both single-cell and spatially resolved data based on dual-path graph attention auto-encoder.Briefings in bioinformatics · 2024Article
- State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemia.Leukemia · 2024Article
- Cognitive abilities are associated with rapid dynamics of electrophysiological connectome states.Network neuroscience (Cambridge, Mass.) · 2024Article
- Why the simplest explanation isn't always the best.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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Abstract
Principal component analysis (PCA) is a dimensionality reduction method that is known for being simple and easy to interpret. Principal components are often interpreted as low-dimensional patterns in high-dimensional space. However, this simple interpretation fails for timeseries, spatial maps, and other continuous data. In these cases, nonoscillatory data may have oscillatory principal components. Here, we show that two common properties of data cause oscillatory principal components: smoothness and shifts in time or space. These two properties implicate almost all neuroscience data. We show how the oscillations produced by PCA, which we call "phantom oscillations," impact data analysis. We also show that traditional cross-validation does not detect phantom oscillations, so we suggest procedures that do. Our findings are supported by a collection of mathematical proofs. Collectively, our work demonstrates that patterns which emerge from high-dimensional data analysis may not faithfully represent the underlying data.
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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 Socratic graph.