Using DenseFly algorithm for cell searching on massive scRNA-seq datasets

BMC Genomics

卷:21

Sijie Chen¹  Xuegong Zhang²  Yixin Chen² 

[1] Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Tsinghua University;

[2] Department of Automation, MOE Key Laboratory of Bioinformatics;

关键词: DenseFly;    Locality sensitive hashing;    scRNA-seq;    Cell searching;   

DOI  :  10.1186/s12864-020-6651-8

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown