ReviewNature protocols2024
Scanorama: integrating large and diverse single-cell transcriptomic datasets.
Review in Nature protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
23 citing papers in PubMed.
- Carmn sustains basal stemness in exocrine glands and CTND1-mediated Wnt signaling in mammary tissue.EMBO reports · 2026Article
- LIGER2: Scalable Single-Cell Integration with On-Disk Datasets.bioRxiv : the preprint server for biology · 2026Article
- Praxis-BGM: clustering of omics data using semi-supervised transfer learning for Gaussian mixture models via natural-gradient variational inference.Bioinformatics (Oxford, England) · 2026Article
- Batch correction for large-scale mass spectrometry imaging experiments.Bioinformatics (Oxford, England) · 2026Article
- Decoding disease and therapy through multiomics integration and systems analysis.Briefings in bioinformatics · 2026Review
- Single-cell and spatial profiling in cancer biology and clinical oncology.Nature cancer · 2026Review
- Subcellular transcriptome sequencing with single cell APEX-seq identifies regulators of cell-cell interactions.bioRxiv : the preprint server for biology · 2026Article
- A unified framework for correcting batch effects and integrating multi-omics data.Scientific reports · 2026Article
- Integrating and mapping single-cell transcriptomics across the entire gene expression space.Briefings in bioinformatics · 2026Article
- Spatial transcriptomics reveals expression gradients in developing wheat inflorescences at cellular resolution.The Plant cell · 2026Article
- DoTools: a cross platform framework to streamline common single cell workflows.Bioinformatics advances · 2026Article
- Integrated single cell transcriptomics analysis for unraveling heterogeneity and plasticity of root cells for sustainable and regenerative agriculture.Frontiers in plant science · 2026Review
- Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes.Bioinformatics advances · 2026Article
- Benchmarking porcine pancreatic ductal organoids for drug screening applications.EMBO molecular medicine · 2025Article
- Epithelial-mesenchymal cell competition coordinates fate transitions across tissue compartments during lung development and fibrosis.Nature communications · 2025Article
- Clustering of Omic Data Using Semi-Supervised Transfer Learning for Gaussian Mixture Models via Natural-Gradient Variational Inference: Method and Applications to Bulk and Single-Cell Transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- ANTIPODE Provides a Global View of Cell Type Homology and Transcriptomic Divergence in the Developing Mammalian Brain.bioRxiv : the preprint server for biology · 2025Article
- Multi-omics integration and batch correction using a modality-agnostic deep learning framework.bioRxiv : the preprint server for biology · 2025Article
- RGCN-BA: relational graph convolutional network with batch awareness for single-cell RNA sequencing clustering.Briefings in bioinformatics · 2025Article
- Integrated analysis of molecular atlases unveils modules driving developmental cell subtype specification in the human cortex.Nature neuroscience · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Merging diverse single-cell RNA sequencing (scRNA-seq) data from numerous experiments, laboratories and technologies can uncover important biological insights. Nonetheless, integrating scRNA-seq data encounters special challenges when the datasets are composed of diverse cell type compositions. Scanorama offers a robust solution for improving the quality and interpretation of heterogeneous scRNA-seq data by effectively merging information from diverse sources. Scanorama is designed to address the technical variation introduced by differences in sample preparation, sequencing depth and experimental batches that can confound the analysis of multiple scRNA-seq datasets. Here we provide a detailed protocol for using Scanorama within a Scanpy-based single-cell analysis workflow coupled with Google Colaboratory, a cloud-based free Jupyter notebook environment service. The protocol involves Scanorama integration, a process that typically spans 0.5-3 h. Scanorama integration requires a basic understanding of cellular biology, transcriptomic technologies and bioinformatics. Our protocol and new Scanorama-Colaboratory resource should make scRNA-seq integration more widely accessible to researchers.
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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.