聚类分析
计算机科学
可扩展性
数据挖掘
数据库
人工智能
作者
Yunfan Li,Yijie Lin,Peng Hu,Dezhong Peng,Han Luo,Xi Peng
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-11
被引量:1
标识
DOI:10.1109/tnnls.2023.3260003
摘要
A variety of single-cell RNA-seq (scRNA-seq) clustering methods has achieved great success in discovering cellular phenotypes. However, it remains challenging when the data confounds with batch effects brought by different experimental conditions or technologies. Namely, the data partitions would be biased toward these nonbiological factors. Meanwhile, the batch differences are not always much smaller than true biological variations, hindering the cooperation of batch integration and clustering methods. To overcome this challenge, we propose single-cell RNA-seq debiased clustering (SCDC), an end-to-end clustering method that is debiased toward batch effects by disentangling the biological and nonbiological information from scRNA-seq data during data partitioning. In six analyses, SCDC qualitatively and quantitatively outperforms both the state-of-the-art clustering and batch integration methods in handling scRNA-seq data with batch effects. Furthermore, SCDC clusters data with a linearly increasing running time with respect to cell numbers and a fixed graphics processing unit (GPU) memory consumption, making it scalable to large datasets. The code will be released on Github.
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