ArticleBMC genomics2020
Using DenseFly algorithm for cell searching on massive scRNA-seq datasets.
Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- BLEND: a fast, memory-efficient and accurate mechanism to find fuzzy seed matches in genome analysis.NAR genomics and bioinformatics · 2023Article
- A Hashing-Based Framework for Enhancing Cluster Delineation of High-Dimensional Single-Cell Profiles.Phenomics (Cham, Switzerland) · 2022Article
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3 authors.
Funding
Abstract
backgroundHigh 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.
resultsWe 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.
conclusionWe 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.
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