ArticleGigaScience2025
Lifting the curse from high-dimensional data: automated projection pursuit clustering for a variety of biological data modalities.
Article in GigaScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A cell-based kinetic framework enables TCR specificity prediction.Signal transduction and targeted therapy · 2026Article
- PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns.bioRxiv : the preprint server for biology · 2026Article
- Automatic phenotyping using exhaustive projection pursuit.Communications biology · 2025Article
- Lifting the curse from high-dimensional data: automated projection pursuit clustering for a variety of biological data modalities.GigaScience · 2025Article
- On the dilemma of using single EV analysis for liquid biopsy: the challenge of low abundance of tumor EVs in blood.Theranostics · 2025Review
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Authors and funding
18 authors.
Funding
Abstract
Unsupervised clustering is a powerful machine-learning technique widely used to analyze high-dimensional biological data. It plays a crucial role in uncovering patterns, structures, and inherent relationships within complex datasets without relying on predefined labels. In the context of biology, high-dimensional data may include transcriptomics, proteomics, and a variety of single-cell omics data. Most existing clustering algorithms operate directly in the high-dimensional space, and their performance may be negatively affected by the phenomenon known as the curse of dimensionality. Here, we show an alternative clustering approach that alleviates the curse by sequentially projecting high-dimensional data into a low-dimensional representation. We validated the effectiveness of our approach, named automated projection pursuit (APP), across various biological data modalities, including flow and mass cytometry data, scRNA-seq, multiplex imaging data, and T-cell receptor repertoire data. APP efficiently recapitulated experimentally validated cell-type definitions and revealed new biologically meaningful patterns.
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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.