Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 4 complete abstracts (648 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
Mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Analyses, Principal Component; Analysis, Principal Component; Principal Component Analyses.
Where it fits in the MeSH classification
- Broader topics
- Dimensionality Reduction
Research landscape
PubMed indexes 33,204 records tagged with the MeSH heading “Principal Component Analysis” (counts retrieved 2026-10-29). 7,235 of them (21.8%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 20,520 records.
Reviews account for 432 records (1.3%) and meta-analyses for 53. Randomized controlled trials number 224 (0.7%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 0 |
| 2000-2009 | 5,981 |
| 2010-2019 | 20,520 |
| 2020 onward | 7,235 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 432 | 1.3% |
| Meta-analyses | 53 | 0.2% |
| Randomized controlled trials | 224 | 0.7% |
Method: PubMed query "Principal Component Analysis"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in clinical research
Research methods determine how much a result can be trusted. Randomized trials guard against confounding, systematic reviews and meta-analyses combine many studies, and observational designs are valuable but can show only association. Reporting standards, trial registration and pre-specified outcomes reduce bias. When reading about a method, ask what problem it is designed to solve and which problems it does not solve.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap | 2015 | Research article Design not recorded in structured metadata | 2,694 | cc by PMID 25969447 · DOI · PMC4489295 |
| Demixed principal component analysis of neural population data | 2016 | Research article Design not recorded in structured metadata | 526 | cc by PMID 27067378 · DOI · PMC4887222 |
| Fast principal component analysis of large-scale genome-wide data | 2014 | Research article Design not recorded in structured metadata | 257 | cc by PMID 24718290 · DOI · PMC3981753 |
| AlignPCA-2D: PCA-reduced Euclidean vector alignment for 2D classification in cryo-EM | 2026 | Research article Design not recorded in structured metadata | 0 | cc by PMID 42257246 · DOI · PMC13317688 |
ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap [1]
Abstract reading summary — source excerpts
- Opening excerpt
- The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of high-dimensional data, often followed by visualizing two of the components on the scatterplot.
- Closing excerpt
- ee/clustvis/.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (193 words)
The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of high-dimensional data, often followed by visualizing two of the components on the scatterplot. Although widely used, the method is lacking an easy-to-use web interface that scientists with little programming skills could use to make plots of their own data. The same applies to creating heatmaps: it is possible to add conditional formatting for Excel cells to show colored heatmaps, but for more advanced features such as clustering and experimental annotations, more sophisticated analysis tools have to be used. We present a web tool called ClustVis that aims to have an intuitive user interface. Users can upload data from a simple delimited text file that can be created in a spreadsheet program. It is possible to modify data processing methods and the final appearance of the PCA and heatmap plots by using drop-down menus, text boxes, sliders etc. Appropriate defaults are given to reduce the time needed by the user to specify input parameters. As an output, users can download PCA plot and heatmap in one of the preferred file formats. This web server is freely available at http://biit.cs.ut.ee/clustvis/.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 25969447 · DOI · PMC4489295
Demixed principal component analysis of neural population data [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity.
- Closing excerpt
- In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (148 words)
Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 27067378 · DOI · PMC4887222
Fast principal component analysis of large-scale genome-wide data [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Principal component analysis (PCA) is routinely used to analyze genome-wide single-nucleotide polymorphism (SNP) data, for detecting population structure and potential outliers.
- Closing excerpt
- This approach will also help to scale other applications that leverage PCA or eigen-decomposition to substantially larger datasets.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (159 words)
Principal component analysis (PCA) is routinely used to analyze genome-wide single-nucleotide polymorphism (SNP) data, for detecting population structure and potential outliers. However, the size of SNP datasets has increased immensely in recent years and PCA of large datasets has become a time consuming task. We have developed flashpca, a highly efficient PCA implementation based on randomized algorithms, which delivers identical accuracy in extracting the top principal components compared with existing tools, in substantially less time. We demonstrate the utility of flashpca on both HapMap3 and on a large Immunochip dataset. For the latter, flashpca performed PCA of 15,000 individuals up to 125 times faster than existing tools, with identical results, and PCA of 150,000 individuals using flashpca completed in 4 hours. The increasing size of SNP datasets will make tools such as flashpca essential as traditional approaches will not adequately scale. This approach will also help to scale other applications that leverage PCA or eigen-decomposition to substantially larger datasets.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 24718290 · DOI · PMC3981753
AlignPCA-2D: PCA-reduced Euclidean vector alignment for 2D classification in cryo-EM [4]
Abstract reading summary — source excerpts
- Opening excerpt
- Cryogenic electron microscopy (cryo-EM) has transformed structural biology by enabling the high-resolution reconstruction of macromolecular complexes from noisy projection images.
- Closing excerpt
- This approach provides a lightweight alternative for large-scale 2D classification tasks, and its modular design makes it compatible with existing cryo-EM processing pipelines.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (148 words)
Cryogenic electron microscopy (cryo-EM) has transformed structural biology by enabling the high-resolution reconstruction of macromolecular complexes from noisy projection images. However, the intrinsic heterogeneity and low signal-to-noise ratio of cryo-EM datasets make 2D classification a critical and computationally demanding step in the processing workflow. Here, we introduce AlignPCA-2D, a principal component analysis (PCA)-space Euclidean vector alignment method for fast, interpretable 2D classification in cryo-EM. By projecting particle images and class representations into a compressed latent PCA space, AlignPCA-2D reduces data dimensionality while preserving meaningful structural variability. The image-to-class assignment is then performed using Euclidean distance, enabling efficient and accurate classification. We benchmark AlignPCA-2D against established cryo-EM software, such as RELION and CryoSPARC, and demonstrate that it achieves competitive alignment accuracy while substantially reducing computational cost. This approach provides a lightweight alternative for large-scale 2D classification tasks, and its modular design makes it compatible with existing cryo-EM processing pipelines.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42257246 · DOI · PMC13317688
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data [5]
- Research article PCA in studying coordination and variability: a tutorial [6]
- Research article Independent component analysis of functional MRI: what is signal and what is noise? [7]
- Research article The use of principal component analysis and discriminant analysis in differential sensing routines [8]
- Research article Modelling with independent components [9]
- Research article The application of principal component analysis to drug discovery and biomedical data [10]
References
- Metsalu T, Vilo J. ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. . 2015. PMID 25969447 · DOI · PMC4489295
- Kobak D, Brendel W, Constantinidis C, Feierstein CE, Kepecs A, Mainen ZF, et al.. Demixed principal component analysis of neural population data. . 2016. PMID 27067378 · DOI · PMC4887222
- Abraham G, Inouye M. Fast principal component analysis of large-scale genome-wide data. . 2014. PMID 24718290 · DOI · PMC3981753
- Ramírez-Aportela E, Zarrabeitia OL, Fonseca YC, Ceska T, Subramaniam S, Carazo JM, et al.. AlignPCA-2D: PCA-reduced Euclidean vector alignment for 2D classification in cryo-EM. . 2026. PMID 42257246 · DOI · PMC13317688
- Calhoun VD, Liu J, Adali T. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage. 2009. PMID 19059344 · DOI · PMC2651152
- Daffertshofer A, Lamoth CJ, Meijer OG, Beek PJ. PCA in studying coordination and variability: a tutorial. Clin Biomech (Bristol). 2004. PMID 15109763 · DOI
- McKeown MJ, Hansen LK, Sejnowsk TJ. Independent component analysis of functional MRI: what is signal and what is noise?. Curr Opin Neurobiol. 2003. PMID 14630228 · DOI · PMC2925426
- Stewart S, Ivy MA, Anslyn EV. The use of principal component analysis and discriminant analysis in differential sensing routines. Chem Soc Rev. 2014. PMID 23995750 · DOI
- Beckmann CF. Modelling with independent components. Neuroimage. 2012. PMID 22369997 · DOI
- Giuliani A. The application of principal component analysis to drug discovery and biomedical data. Drug Discov Today. 2017. PMID 28111329 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.