GraphPCA: a fast and interpretable dimension reduction algorithm for spatial transcriptomics data

• Published in: Genome Biology Vol. 25; no. 1; p. 287
• Main Authors: Yang, Jiyuan, Wang, Lu, Liu, Lin, Zheng, Xiaoqi
• Format: Journal Article
• Language: English
• Published: London BioMed Central 07.11.2024; Springer Nature B.V; BMC
• Subjects: Algorithms; Animal Genetics and Genomics; Bioinformatics; Biomedical and Life Sciences; data collection; Datasets; Deep learning; Dimension reduction; domain; Enzymes; Evolutionary Biology; Gene expression; Gene Expression Profiling - methods; genome; Genomics; Human Genetics; Humans; Life Sciences; Methodology; Microbial Genetics and Genomics; Neighborhoods; Noise; Optimization; PCA; Performance evaluation; Plant Genetics and Genomics; Principal Component Analysis; Principal components analysis; Sparsity; Spatial domain detection; Spatial transcriptomics; Standard deviation; Transcriptome; Transcriptomics; Dimension reduction; Spatial transcriptomics; Spatial domain detection; PCA
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-024-03429-x

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.