ArticleGenome biology2025
Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Single-cell RNA sequencing offers novel perspectives in viral infection research.Frontiers in cellular and infection microbiology · 2026Pooled it
- IntegrateRigor: annotation-free integration optimization for cell identity recovery reveals cancer-immune interface niches.bioRxiv : the preprint server for biology · 2026Article
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Defining the role of natural killer cells in acute myeloid leukemia through the lens of single-cell omics.Frontiers in immunology · 2026Review
- Decrypting cancer's spatial code: from single cells to tissue niches.Molecular oncology · 2025Review
- Computer Vision Methods for Spatial Transcriptomics: A Survey.bioRxiv : the preprint server for biology · 2025Article
- Roadmap for spatial transcriptomics of HIV in tissues.Current opinion in HIV and AIDS · 2025Review
- Neuropeptidergic systems in psychiatric disorders.Frontiers in endocrinology · 2025Review
- A flexible systems analysis pipeline for elucidating spatial relationships in the tumor microenvironment linked with cellular phenotypes and patient-level features.Frontiers in immunology · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
Spatial transcriptomics facilitates gene expression analysis of cells in their spatial anatomical context. Batch effects hinder visualization of gene spatial patterns across samples. We present the Crescendo algorithm to correct for batch effects at the gene expression level and enable accurate visualization of gene expression patterns across multiple samples. We show Crescendo's utility and scalability across three datasets ranging from 170,000 to 7 million single cells across spatial and single-cell RNA sequencing technologies. By correcting for batch effects, Crescendo enhances spatial transcriptomics analyses to detect gene colocalization and ligand-receptor interactions and enables cross-technology information transfer.
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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.