Cargando…
Using DenseFly algorithm for cell searching on massive scRNA-seq datasets
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 leve...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7739457/ https://www.ncbi.nlm.nih.gov/pubmed/33327944 http://dx.doi.org/10.1186/s12864-020-6651-8 |
_version_ | 1783623334629474304 |
---|---|
author | Chen, Yixin Chen, Sijie Zhang, Xuegong |
author_facet | Chen, Yixin Chen, Sijie Zhang, Xuegong |
author_sort | Chen, Yixin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7739457 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77394572020-12-17 Using DenseFly algorithm for cell searching on massive scRNA-seq datasets Chen, Yixin Chen, Sijie Zhang, Xuegong BMC Genomics Methodology 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. BioMed Central 2020-12-16 /pmc/articles/PMC7739457/ /pubmed/33327944 http://dx.doi.org/10.1186/s12864-020-6651-8 Text en © The Author(s). 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Methodology Chen, Yixin Chen, Sijie Zhang, Xuegong Using DenseFly algorithm for cell searching on massive scRNA-seq datasets |
title | Using DenseFly algorithm for cell searching on massive scRNA-seq datasets |
title_full | Using DenseFly algorithm for cell searching on massive scRNA-seq datasets |
title_fullStr | Using DenseFly algorithm for cell searching on massive scRNA-seq datasets |
title_full_unstemmed | Using DenseFly algorithm for cell searching on massive scRNA-seq datasets |
title_short | Using DenseFly algorithm for cell searching on massive scRNA-seq datasets |
title_sort | using densefly algorithm for cell searching on massive scrna-seq datasets |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7739457/ https://www.ncbi.nlm.nih.gov/pubmed/33327944 http://dx.doi.org/10.1186/s12864-020-6651-8 |
work_keys_str_mv | AT chenyixin usingdenseflyalgorithmforcellsearchingonmassivescrnaseqdatasets AT chensijie usingdenseflyalgorithmforcellsearchingonmassivescrnaseqdatasets AT zhangxuegong usingdenseflyalgorithmforcellsearchingonmassivescrnaseqdatasets |