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bioRxiv · 10.1101/2024.06.25.600557

Fast-scBatch: Batch Effect Correction Using Neural Network-Driven Distance Matrix Adjustment

Chen, F.·Tian, L.·Yu, T.
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Abstract

Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (scRNA-seq) data. To address this challenge, we introduce fast-scBatch, a novel and efficient two-phase algorithm for batch-effect correction in scRNA-seq data, designed to handle non-linear and complex batch effects. Specifically, this method utilizes the inherent correlation structure of the data for batch effect correction and employs a neural network to expedite the process. It outputs a corrected expression matrix, facilitating downstream analyses. We validated fast-scBatch through simulation studies and on two scRNA-seq datasets, demonstrating its superior performance in batch-effect correction compared to current methods, as evidenced by visualization using UMAP plots, and metrics including Adjusted Rand Index (ARI) and Adjusted Mutual Information (AMI).

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Chen, F., Tian, L., Yu, T.. 2024-06-28. Fast-scBatch: Batch Effect Correction Using Neural Network-Driven Distance Matrix Adjustment. https://doi.org/10.1101/2024.06.25.600557

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