ArticleNucleic acids research2022
Effective and scalable single-cell data alignment with non-linear canonical correlation analysis.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Variational sparse Gaussian-process method for detecting spatially variable genes and cellular interactions in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Multitask benchmarking of single-cell multimodal omics integration methods.Nature methods · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- Inference of gene coexpression networks from single-cell transcriptome data based on variance decomposition analysis.Briefings in bioinformatics · 2025Article
- A comparison of integration methods for single-cell RNA sequencing data and ATAC sequencing data.Quantitative biology (Beijing, China) · 2025Review
- scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links.Nature communications · 2025Article
- Exploring and mitigating shortcomings in single-cell differential expression analysis with a new statistical paradigm.Genome biology · 2025Article
- Pathway Activation Analysis for Pan-Cancer Personalized Characterization Based on Riemannian Manifold.International journal of molecular sciences · 2024Article
- Scbean: a python library for single-cell multi-omics data analysis.Bioinformatics (Oxford, England) · 2024Article
- Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets.Genome biology · 2023Review
- A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data.Bioinformatics (Oxford, England) · 2023Article
- Identifying driver modules based on multi-omics biological networks in prostate cancer.IET systems biology · 2022Article
- Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data.Nucleic acids research · 2022Article
- Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants.Briefings in bioinformatics · 2022Article
- Revealing the Key MSCs Niches and Pathogenic Genes in Influencing CEP Homeostasis: A Conjoint Analysis of Single-Cell and WGCNA.Frontiers in immunology · 2022Article
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3 authors.
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Abstract
Data alignment is one of the first key steps in single cell analysis for integrating multiple datasets and performing joint analysis across studies. Data alignment is challenging in extremely large datasets, however, as the major of the current single cell data alignment methods are not computationally efficient. Here, we present VIPCCA, a computational framework based on non-linear canonical correlation analysis for effective and scalable single cell data alignment. VIPCCA leverages both deep learning for effective single cell data modeling and variational inference for scalable computation, thus enabling powerful data alignment across multiple samples, multiple data platforms, and multiple data types. VIPCCA is accurate for a range of alignment tasks including alignment between single cell RNAseq and ATACseq datasets and can easily accommodate millions of cells, thereby providing researchers unique opportunities to tackle challenges emerging from large-scale single-cell atlas.
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