ArticleGenome biology2021
iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks.
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 36 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
36 citing papers in PubMed.
- Single-cell-level perturbation-induced and condition-related signal estimation with batch effect removal using NDreamer.Briefings in bioinformatics · 2026Article
- scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects autoencoder.Nature communications · 2026Article
- A benchmark of semi-supervised scRNA-seq integration methods in real-world scenarios.PLoS computational biology · 2026Article
- scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization.Nature communications · 2025Article
- scBCN: deep learning-based batch correction network for integration of heterogeneous single-cell data.Briefings in bioinformatics · 2025Article
- CYCLONE: recycle contrastive learning for integrating single-cell gene expression data.BMC bioinformatics · 2025Article
- scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links.Nature communications · 2025Article
- Partially characterized topology guides reliable anchor-free scRNA-integration.Communications biology · 2025Article
- scTrans: Sparse attention powers fast and accurate cell type annotation in single-cell RNA-seq data.PLoS computational biology · 2025Article
- Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives.Molecular medicine (Cambridge, Mass.) · 2025Review
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- Immunosuppression and phenotypic plasticity in an atlas of human hepatocholangiocarcinoma.Hepatobiliary surgery and nutrition · 2024Article
- Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
- The effect of data transformation on low-dimensional integration of single-cell RNA-seq.BMC bioinformatics · 2024Article
- Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation.BMC bioinformatics · 2024Article
- BERMAD: batch effect removal for single-cell RNA-seq data using a multi-layer adaptation autoencoder with dual-channel framework.Bioinformatics (Oxford, England) · 2024Article
- Deep-learning methods for unveiling large-scale single-cell transcriptomes.Cancer biology & medicine · 2024Article
- scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier.Nature communications · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-to-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch effect removal framework, called iMAP, based on both deep autoencoders and generative adversarial networks. Compared with current methods, iMAP shows superior, robust, and scalable performance in terms of both reliably detecting the batch-specific cells and effectively mixing distributions of the batch-shared cell types. Applying iMAP to tumor microenvironment datasets from two platforms, Smart-seq2 and 10x Genomics, we find that iMAP can leverage the powers of both platforms to discover novel cell-cell interactions.
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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.