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• W4213238517 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." @default.
• W4213238517 created "2022-02-24" @default.
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• W4213238517 date "2022-02-14" @default.
• W4213238517 modified "2023-10-17" @default.
• W4213238517 title "IMGG: Integrating Multiple Single-Cell Datasets through Connected Graphs and Generative Adversarial Networks" @default.
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• W4213238517 doi "https://doi.org/10.3390/ijms23042082" @default.
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