Cargando…

SCeQTL: an R package for identifying eQTL from single-cell parallel sequencing data

BACKGROUND: With the rapid development of single-cell genomics, technologies for parallel sequencing of the transcriptome and genome in each single cell is being explored in several labs and is becoming available. This brings us the opportunity to uncover association between genotypes and gene expre...

Descripción completa

Detalles Bibliográficos
Autores principales: Hu, Yue, Xi, Xi, Yang, Qian, Zhang, Xuegong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7216638/
https://www.ncbi.nlm.nih.gov/pubmed/32393315
http://dx.doi.org/10.1186/s12859-020-3534-6
_version_ 1783532453269340160
author Hu, Yue
Xi, Xi
Yang, Qian
Zhang, Xuegong
author_facet Hu, Yue
Xi, Xi
Yang, Qian
Zhang, Xuegong
author_sort Hu, Yue
collection PubMed
description BACKGROUND: With the rapid development of single-cell genomics, technologies for parallel sequencing of the transcriptome and genome in each single cell is being explored in several labs and is becoming available. This brings us the opportunity to uncover association between genotypes and gene expression phenotypes at single-cell level by eQTL analysis on single-cell data. New method is needed for such tasks due to special characteristics of single-cell sequencing data. RESULTS: We developed an R package SCeQTL that uses zero-inflated negative binomial regression to do eQTL analysis on single-cell data. It can distinguish two type of gene-expression differences among different genotype groups. It can also be used for finding gene expression variations associated with other grouping factors like cell lineages or cell types. CONCLUSIONS: The SCeQTL method is capable for eQTL analysis on single-cell data as well as detecting associations of gene expression with other grouping factors. The R package of the method is available at https://github.com/XuegongLab/SCeQTL/.
format Online
Article
Text
id pubmed-7216638
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-72166382020-05-18 SCeQTL: an R package for identifying eQTL from single-cell parallel sequencing data Hu, Yue Xi, Xi Yang, Qian Zhang, Xuegong BMC Bioinformatics Methodology Article BACKGROUND: With the rapid development of single-cell genomics, technologies for parallel sequencing of the transcriptome and genome in each single cell is being explored in several labs and is becoming available. This brings us the opportunity to uncover association between genotypes and gene expression phenotypes at single-cell level by eQTL analysis on single-cell data. New method is needed for such tasks due to special characteristics of single-cell sequencing data. RESULTS: We developed an R package SCeQTL that uses zero-inflated negative binomial regression to do eQTL analysis on single-cell data. It can distinguish two type of gene-expression differences among different genotype groups. It can also be used for finding gene expression variations associated with other grouping factors like cell lineages or cell types. CONCLUSIONS: The SCeQTL method is capable for eQTL analysis on single-cell data as well as detecting associations of gene expression with other grouping factors. The R package of the method is available at https://github.com/XuegongLab/SCeQTL/. BioMed Central 2020-05-11 /pmc/articles/PMC7216638/ /pubmed/32393315 http://dx.doi.org/10.1186/s12859-020-3534-6 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 Article
Hu, Yue
Xi, Xi
Yang, Qian
Zhang, Xuegong
SCeQTL: an R package for identifying eQTL from single-cell parallel sequencing data
title SCeQTL: an R package for identifying eQTL from single-cell parallel sequencing data
title_full SCeQTL: an R package for identifying eQTL from single-cell parallel sequencing data
title_fullStr SCeQTL: an R package for identifying eQTL from single-cell parallel sequencing data
title_full_unstemmed SCeQTL: an R package for identifying eQTL from single-cell parallel sequencing data
title_short SCeQTL: an R package for identifying eQTL from single-cell parallel sequencing data
title_sort sceqtl: an r package for identifying eqtl from single-cell parallel sequencing data
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7216638/
https://www.ncbi.nlm.nih.gov/pubmed/32393315
http://dx.doi.org/10.1186/s12859-020-3534-6
work_keys_str_mv AT huyue sceqtlanrpackageforidentifyingeqtlfromsinglecellparallelsequencingdata
AT xixi sceqtlanrpackageforidentifyingeqtlfromsinglecellparallelsequencingdata
AT yangqian sceqtlanrpackageforidentifyingeqtlfromsinglecellparallelsequencingdata
AT zhangxuegong sceqtlanrpackageforidentifyingeqtlfromsinglecellparallelsequencingdata