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Data-driven batch detection enhances single-cell omics data analysis
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DOI：10.1016/j.cels.2024.09.011.png)
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Abstract
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In single-cell omics studies, data are typically collected across multiple batches, resulting in batch effects: technical confounders that introduce noise and distort data distribution. Correcting these effects is challenging due to their unknown sources, nonlinear distortions, and the difficulty of accurately assigning data to batches that are optimal for integration methods.
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