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An order-preserving batch-effect correction method based on a monotonic deep learning framework
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DOI：10.1093/bib/bbaf247.png)
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Abstract
En 中文
Single-cell RNA sequencing has significantly advanced our understanding of cell heterogeneity and gene regulation. Batch-effect correction is essential for achieving robust data integration. Multiple methods have been developed to address this issue, particularly procedural approaches involving components such as anchoring or deep learning, which have achieved notable successes. However, order preservation, as an important feature, has been largely overlooked in procedural methods. Based on a monotonic deep learning network, we developed a correction method with order-preserving feature. By comparing with existing methods, we demonstrated that our method effectively improved clustering performance, better retained original inter-gene correlation and differential expression information.
Keywords:
scRNA-sequencing
batch effect
order-preserving
monotonic deep learning network
inter-gene correlation
differential expression consistency
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