ArticleInternational journal of molecular sciences2022
IMGG: Integrating Multiple Single-Cell Datasets through Connected Graphs and Generative Adversarial Networks.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- The development of the generative adversarial supporting vector machine for molecular property generation.Journal of cheminformatics · 2025Article
- Integrating Multiple Single-Cell RNA Sequencing Datasets Using Adversarial Autoencoders.International journal of molecular sciences · 2023Article
- PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning.International journal of molecular sciences · 2023Article
- TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
There is a strong need to eliminate batch-specific differences when integrating single-cell RNA-sequencing (scRNA-seq) datasets generated under different experimental conditions for downstream task analysis. Existing batch correction methods usually transform different batches of cells into one preselected "anchor" batch or a low-dimensional embedding space, and cannot take full advantage of useful information from multiple sources. We present a novel framework, called IMGG, i.e., integrating multiple single-cell datasets through connected graphs and generative adversarial networks (GAN) to eliminate nonbiological differences between different batches. Compared with current methods, IMGG shows excellent performance on a variety of evaluation metrics, and the IMGG-corrected gene expression data incorporate features from multiple batches, allowing for downstream tasks such as differential gene expression analysis.
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