IMGG: Integrating Multiple Single-Cell Datasets through Connected Graphs and Generative Adversarial Networks

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 pres...

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Bibliographic Details
Published in:International journal of molecular sciences 2022-02, Vol.23 (4), p.2082
Main Authors: Wang, Xun, Zhang, Chaogang, Zhang, Ying, Meng, Xiangyu, Zhang, Zhiyuan, Shi, Xin, Song, Tao
Format: Article
Language:English
Subjects:
Algorithms
batch effect
connected graphs
Datasets
deep learning
Dendritic cells
Experiments
GAN
Gene expression
Genomics
Ratings & rankings
RNA-Seq - methods
scRNA-seq
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
Visualization
Whole Exome Sequencing - methods
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Summary: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.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms23042082