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Semi-supervised integration of single-cell transcriptomics data - Nature Communications

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Semi-supervised integration of single-cell transcriptomics data - Nature Communications

Batch effects in single-cell RNA-seq data pose a significant challenge for comparative analyses across samples, individuals, and conditions. Although batch effect correction methods are routinely applied, data integration often leads to overcorrection and can result in the loss of biological variability. In this work we present STACAS, a batch correction method for scRNA-seq that leverages prior knowledge on cell types to preserve biological variability upon integration. Through an

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Semi-supervised integration of single-cell transcriptomics data - Nature Communications

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Semi-supervised integration of single-cell transcriptomics data - Nature Communications

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