ArticleNature communications2024
scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing 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 20 papers.
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20 citing papers in PubMed, 28 citations in OpenAlex.
- Donor-specific, but not sex-specific, signatures dominate cell-type classification in lung scRNA-seq data.Biochemistry and biophysics reports · 2026Article
- Single-cell-level perturbation-induced and condition-related signal estimation with batch effect removal using NDreamer.Briefings in bioinformatics · 2026Article
- Deep Learning-Based Multimodal Fusion of Whole-Slide Images and RNA Sequencing Identifies Survival-Relevant Glioblastoma Clusters.Cancer medicine · 2026Article
- Condition-Associated Pattern Extraction and Recovery From Multi-Condition Single-Cell RNA-seq Data With CAPER.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Benchmarking the prediction of responding cells to perturbations affecting both gene expression and cellular abundance using scRNA sequencing.Scientific reports · 2026Article
- scArchon: a scalable benchmarking framework for assessing single-cell perturbation models.Genome biology · 2026Article
- Semi-supervised disentangled representation learning for single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Benchmarking algorithms for generalizable single-cell perturbation response prediction.Nature methods · 2026Article
- Integrative Learning of Disentangled Representations from Single-Cell RNA-Sequencing Datasets.Computational and structural biotechnology journal · 2026Article
- Interpretable phenotype decoding from multicondition sequencing data with ALPINE.Genome research · 2025Article
- Causal disentanglement for single-cell representations and controllable counterfactual generation.Nature communications · 2025Article
- RGCN-BA: relational graph convolutional network with batch awareness for single-cell RNA sequencing clustering.Briefings in bioinformatics · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- Harmonizing heterogeneous single-cell gene expression data with individual-level covariate information.Bioinformatics advances · 2025Article
- Bridging gaps: a neural network approach for cross-species scRNA-seq analysis in COVID-19.EBioMedicine · 2024Article
- Small data methods in omics: the power of one.Nature methods · 2024Review
- Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation.BMC bioinformatics · 2024Article
- scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data.Nature communications · 2024Article
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5 authors at 1 institution in 1 country.
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Abstract
Single-cell RNA-sequencing (scRNA-seq) has been widely used for disease studies, where sample batches are collected from donors under different conditions including demographic groups, disease stages, and drug treatments. It is worth noting that the differences among sample batches in such a study are a mixture of technical confounders caused by batch effect and biological variations caused by condition effect. However, current batch effect removal methods often eliminate both technical batch effect and meaningful condition effect, while perturbation prediction methods solely focus on condition effect, resulting in inaccurate gene expression predictions due to unaccounted batch effect. Here we introduce scDisInFact, a deep learning framework that models both batch effect and condition effect in scRNA-seq data. scDisInFact learns latent factors that disentangle condition effect from batch effect, enabling it to simultaneously perform three tasks: batch effect removal, condition-associated key gene detection, and perturbation prediction. We evaluate scDisInFact on both simulated and real datasets, and compare its performance with baseline methods for each task. Our results demonstrate that scDisInFact outperforms existing methods that focus on individual tasks, providing a more comprehensive and accurate approach for integrating and predicting multi-batch multi-condition single-cell RNA-sequencing data.
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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.