ArticleGenome biology2021
scMC learns biological variation through the alignment of multiple single-cell genomics datasets.
Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 852 citations in OpenAlex.
- A microphysiological system HHT-on-a-chip platform recapitulates patient vascular lesions.Nature communications · 2026Article
- NeMO Analytics: a compendium of transcriptomic data for the exploration of neocortical development.Nature neuroscience · 2026Article
- A comprehensive benchmarking study on computational tools for cross-omics label transfer from single-cell RNA to ATAC data.G3 (Bethesda, Md.) · 2026Article
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- scBCN: deep learning-based batch correction network for integration of heterogeneous single-cell data.Briefings in bioinformatics · 2025Article
- Integrating single-cell data with biological variables.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Deep scSTAR: leveraging deep learning for the extraction and enhancement of phenotype-associated features from single-cell RNA sequencing and spatial transcriptomics data.Briefings in bioinformatics · 2025Article
- Integrating multiple spatial transcriptomics data using community-enhanced graph contrastive learning.PLoS computational biology · 2025Article
- scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization.Communications biology · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Multi-Omics Analysis Combined with Machine Learning Identified FABP4 in Smooth Muscle Cells as a Pathogenic Factor in Atherosclerosis.Journal of inflammation research · 2025Article
- Leveraging gene correlations in single cell transcriptomic data.BMC bioinformatics · 2024Article
- Detecting novel cell type in single-cell chromatin accessibility data via open-set domain adaptation.Briefings in bioinformatics · 2024Article
- Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data.Nature communications · 2024Article
- Targeting tumor-stromal interactions in triple-negative breast cancer using a human vascularized micro-tumor model.Breast cancer research : BCR · 2024Article
- Leveraging gene correlations in single cell transcriptomic data.bioRxiv : the preprint server for biology · 2023Article
- Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of the Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential.Cancer discovery · 2023Article
- Accurate integration of multiple heterogeneous single-cell RNA-seq data sets by learning contrastive biological variation.Genome research · 2023Article
- A unified computational framework for single-cell data integration with optimal transport.Nature communications · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment.
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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.