ArticleNature communications2024
Semi-supervised integration of single-cell transcriptomics data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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
42 citing papers in PubMed.
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- Circulating immune profiling reveals impaired monocyte states and trajectories driving immunosuppression in glioblastoma.Journal of neuroinflammation · 2026Article
- BLASE: bulk linkage analysis for single cell experiments - teasing out the secrets of bulk transcriptomics with trajectory analysis.BMC methods · 2026Article
- A unified single-cell atlas of HNSCC: Toward characterizing HPV- and sex-associated TME variability.iScience · 2026Article
- Enhancing Proteasome Activity in T Cells Alleviates Exhaustion and Improves Antitumor Immunity.Cancer research · 2026Article
- The developing leaf of the wild grass Brachypodium distachyon at single-cell resolution.The Plant cell · 2026Article
- Shortcomings of silhouette in single-cell integration benchmarking.Nature biotechnology · 2026Article
- Robust integration of single-cell datasets with imbalanced modality composition.Nature communications · 2026Article
- Ex Vivo Expansion of Melanoma Tumor-Infiltrating Lymphocytes Leads to a Dominant Exhausted T-cell Population with a Lack of Memory Markers.Cancer immunology research · 2026Article
- A Head and Neck Cancer Patient-Specific Microphysiological System for Predicting Response to Chemoradiation.bioRxiv : the preprint server for biology · 2026Article
- Single-cell and spatial profiling in cancer biology and clinical oncology.Nature cancer · 2026Review
- Anti-Müllerian hormone ameliorates uterine DNA damage response and prevents pregnancy complications in doxorubicin-treated mice†.Biology of reproduction · 2026Article
- Exploring the human brain: spatial transcriptomics challenges and approaches in post-mortem analysis.Brain : a journal of neurology · 2026Review
- A benchmark of semi-supervised scRNA-seq integration methods in real-world scenarios.PLoS computational biology · 2026Article
- From the brain cell atlas to precision neurology: a review of the application of AI-driven multi-omics in brain science.GigaScience · 2026Review
- A pan-cancer single cell landscape reveals heterogeneity and functional diversity of double-negative T cells.Molecular cancer · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Reversing T cell dysfunction in a novelFrontiers in immunology · 2026Article
- Maladaptive immunity to the microbiota promotes neuronal hyperinnervation and itch via IL-17A.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Clonal evolution and transcriptional plasticity shape metastatic dissemination routes in prostate cancer.Nature communications · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Batch effects in single-cell RNA-seq data pose a significant challenge for comparative analyses across samples, individuals, and conditions. Although batch effect correction methods are routinely applied, data integration often leads to overcorrection and can result in the loss of biological variability. In this work we present STACAS, a batch correction method for scRNA-seq that leverages prior knowledge on cell types to preserve biological variability upon integration. Through an open-source benchmark, we show that semi-supervised STACAS outperforms state-of-the-art unsupervised methods, as well as supervised methods such as scANVI and scGen. STACAS scales well to large datasets and is robust to incomplete and imprecise input cell type labels, which are commonly encountered in real-life integration tasks. We argue that the incorporation of prior cell type information should be a common practice in single-cell data integration, and we provide a flexible framework for semi-supervised batch effect correction.
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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.