ArticleGigaScience2024
spatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics.
Article in GigaScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deep Learning-Assisted Prioritization of Candidate Drug Targets in Tumors Using Spatial Multi-Omics.Biology · 2026Review
- From the brain cell atlas to precision neurology: a review of the application of AI-driven multi-omics in brain science.GigaScience · 2026Review
- 4D single-cell spatial transcriptomics reveals dynamic morphogenetic gradients and regenerative domains in planarians.GigaScience · 2026Article
- A comprehensive review of spatial transcriptomics data alignment and integration.Nucleic acids research · 2025Review
- POPARI: Modeling multisample variation in spatial transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- Thinking points for effective batch correction on biomedical data.Briefings in bioinformatics · 2024Article
- spatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics.GigaScience · 2024Article
- EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics.GigaScience · 2024Article
- BatchEval Pipeline: batch effect evaluation workflow for multiple datasets joint analysis.GigaByte (Hong Kong, China) · 2024Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundIntegrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times.
findingsWe propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space.
conclusionsIn benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.
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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.