ArticleGenome biology2024
GraphPCA: a fast and interpretable dimension reduction algorithm for spatial transcriptomics data.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- SpaMTP: integrative statistical analysis and visualization of spatial metabolomics and transcriptomics data.Nature methods · 2026Article
- Interpretable and scalable spatial gene set activity analysis with GESSO uncovers functional tissue architecture.bioRxiv : the preprint server for biology · 2026Article
- Deciphering skeletal muscle development: cellular heterogeneity and molecular regulatory networks from single-cell and spatial transcriptomic perspectives.Frontiers in cell and developmental biology · 2026Review
- jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data.Bioinformatics advances · 2026Article
- SEPAR enables spatial metagene discovery and associated molecular pattern characterization in spatial transcriptomics and multi-omics datasets.Communications biology · 2025Article
- Advancing genome-based precision medicine: a review on machine learning applications for rare genetic disorders.Briefings in bioinformatics · 2025Review
- ONTraC characterizes spatially continuous variations of tissue microenvironment through niche trajectory analysis.Genome biology · 2025Article
- Artificial intelligence in traditional Chinese medicine: advances in multi-metabolite multi-target interaction modeling.Frontiers in pharmacology · 2025Review
- GraphPCA: a fast and interpretable dimension reduction algorithm for spatial transcriptomics data.Genome biology · 2024Article
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Authors and funding
4 authors.
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Abstract
The rapid advancement of spatial transcriptomics technologies has revolutionized our understanding of cell heterogeneity and intricate spatial structures within tissues and organs. However, the high dimensionality and noise in spatial transcriptomic data present significant challenges for downstream data analyses. Here, we develop GraphPCA, an interpretable and quasi-linear dimension reduction algorithm that leverages the strengths of graphical regularization and principal component analysis. Comprehensive evaluations on simulated and multi-resolution spatial transcriptomic datasets generated from various platforms demonstrate the capacity of GraphPCA to enhance downstream analysis tasks including spatial domain detection, denoising, and trajectory inference compared to other state-of-the-art methods.
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