Fast-scBatch: Batch effect correction using neural network-driven distance matrix adjustment Conference Paper
| Authors: | Chen, F.; Tian, L.; Fei, T.; Yu, T. |
| Title: | Fast-scBatch: Batch effect correction using neural network-driven distance matrix adjustment |
| Conference Title: | 15th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2024) |
| 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). © 2024 Copyright held by the owner/author(s). |
| Keywords: | principal component analysis; machine learning; single cells; principal-component analysis; neural-networks; machine-learning; single-cell sequencing; batch effect; correlation structure; distance matrix; effect corrections; two phase algorithm; batch data processing |
| Journal Title | Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics |
| Conference Dates: | 2024 Nov 22-25 |
| Conference Location: | Shenzhen China |
| ISBN: | 979-8-4007-1302-6 |
| Publisher: | The Association for Computing Machinery |
| Date Published: | 2024-12-16 |
| Start Page: | 66 |
| Language: | English |
| DOI: | 10.1145/3698587.3701383 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Source: Scopus |
