使用 DenseFly 算法在大规模 scRNA-seq 数据集中进行细胞搜索。
Using DenseFly algorithm for cell searching on massive scRNA-seq datasets.
机构信息
Department of Automation, MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Tsinghua University, Beijing, 100084, China.
School of Life Sciences, Tsinghua University, Beijing, 100084, China.
出版信息
BMC Genomics. 2020 Dec 16;21(Suppl 5):222. doi: 10.1186/s12864-020-6651-8.
BACKGROUND
High throughput single-cell transcriptomic technology produces massive high-dimensional data, enabling high-resolution cell type definition and identification. To uncover the expressional patterns beneath the big data, a transcriptional landscape searching algorithm at a single-cell level is desirable.
RESULTS
We explored the feasibility of using DenseFly algorithm for cell searching on scRNA-seq data. DenseFly is a locality sensitive hashing algorithm inspired by the fruit fly olfactory system. The experiments indicate that DenseFly outperforms the baseline methods FlyHash and SimHash in classification tasks, and the performance is robust to dropout events and batch effects.
CONCLUSION
We developed a method for mapping cells across scRNA-seq datasets based on the DenseFly algorithm. It can be an efficient tool for cell atlas searching.
背景
高通量单细胞转录组学技术产生了海量的高维数据，能够实现高分辨率的细胞类型定义和鉴定。为了揭示大数据背后的表达模式，需要在单细胞水平上搜索转录组图谱的算法。
结果
我们探索了使用 DenseFly 算法在 scRNA-seq 数据上进行细胞搜索的可行性。DenseFly 是一种受果蝇嗅觉系统启发的局部敏感哈希算法。实验表明，在分类任务中，DenseFly 优于基线方法 FlyHash 和 SimHash，并且性能对丢包事件和批次效应具有鲁棒性。
结论
我们开发了一种基于 DenseFly 算法的跨 scRNA-seq 数据集映射细胞的方法。它可以成为细胞图谱搜索的有效工具。
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