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BL-FlowSOM: Consistent and Highly Accelerated FlowSOM Based on Parallelized Batch Learning
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DOI：10.1002/cyto.a.24934.png)
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Abstract
En 中文
The recent increase in the dimensionality of cytometry data has led to the development of various computational analysis methods. FlowSOM is one of the best-performing clustering methods but has room for improvement in terms of the consistency and speed of the clustering process. Here, we introduce Batch Learning FlowSOM (BL-FlowSOM), which is a consistent and highly accelerated FlowSOM based on parallelized batch learning. The change of the learning algorithm from online learning to batch learning with principal component analysis initialization improves consistency and eliminates randomness in the clustering process. It also enables the parallelization of the learning process, leading to significant acceleration of the clustering process with clustering quality equivalent to that of FlowSOM. BL-FlowSOM is available on Sony's Spectral Flow Analysis (SFA)-Life sciences Cloud Platform ().
Keywords:
batch learning
computational cytometry
high-dimensional flow cytometry
parallelization
self-organizing map
spectral flow cytometry
unsupervised clustering
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