• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

SCeQTL：一个用于从单细胞平行测序数据中识别 eQTL 的 R 包。

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

机构信息

MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China.

School of Life Sciences, Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China.

出版信息

BMC Bioinformatics. 2020 May 11;21(1):184. doi: 10.1186/s12859-020-3534-6.

DOI:10.1186/s12859-020-3534-6
PMID:32393315
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7216638/
Abstract

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/.

摘要

背景

随着单细胞基因组学的快速发展，多个实验室正在探索在单细胞中平行测序转录组和基因组的技术，并且这些技术正在变得可行。这为我们提供了通过单细胞数据的 eQTL 分析在单细胞水平上揭示基因型与基因表达表型之间的关联的机会。由于单细胞测序数据的特殊特征，此类任务需要新的方法。

结果

我们开发了一个 R 包 SCeQTL，该包使用零膨胀负二项式回归在单细胞数据上进行 eQTL 分析。它可以区分不同基因型组之间两种类型的基因表达差异。它还可用于寻找与其他分组因素（如细胞谱系或细胞类型）相关的基因表达变化。

结论

SCeQTL 方法能够对单细胞数据进行 eQTL 分析，并且能够检测基因表达与其他分组因素的关联。该方法的 R 包可在 https://github.com/XuegongLab/SCeQTL/ 上获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98b/7216638/9a2d1e42cbcc/12859_2020_3534_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98b/7216638/34107d0b7d45/12859_2020_3534_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98b/7216638/6f001b744d8a/12859_2020_3534_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98b/7216638/7ae9dcfd821d/12859_2020_3534_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98b/7216638/5d10b40682b7/12859_2020_3534_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98b/7216638/683b78771d2f/12859_2020_3534_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98b/7216638/9a2d1e42cbcc/12859_2020_3534_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98b/7216638/34107d0b7d45/12859_2020_3534_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98b/7216638/6f001b744d8a/12859_2020_3534_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98b/7216638/7ae9dcfd821d/12859_2020_3534_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98b/7216638/5d10b40682b7/12859_2020_3534_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98b/7216638/683b78771d2f/12859_2020_3534_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c98b/7216638/9a2d1e42cbcc/12859_2020_3534_Fig6_HTML.jpg

相似文献

1
SCeQTL: an R package for identifying eQTL from single-cell parallel sequencing data.SCeQTL：一个用于从单细胞平行测序数据中识别 eQTL 的 R 包。
BMC Bioinformatics. 2020 May 11;21(1):184. doi: 10.1186/s12859-020-3534-6.
2
Conditional eQTL analysis reveals allelic heterogeneity of gene expression.条件性表达数量性状基因座分析揭示了基因表达的等位基因异质性。
Hum Mol Genet. 2017 Apr 15;26(8):1444-1451. doi: 10.1093/hmg/ddx043.
3
Genetic control of longissimus dorsi muscle gene expression variation and joint analysis with phenotypic quantitative trait loci in pigs.猪背最长肌基因表达变异的遗传控制及与表型数量性状位点的联合分析。
BMC Genomics. 2019 Jan 3;20(1):3. doi: 10.1186/s12864-018-5386-2.
4
Population-specific genome-wide mapping of expression quantitative trait loci in the colon of Han Chinese.汉族人群结肠中表达数量性状基因座的全基因组特异性定位
J Dig Dis. 2016 Sep;17(9):600-609. doi: 10.1111/1751-2980.12399.
5
Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe.人类额叶的全面启动子水平表达数量性状位点分析
Genome Med. 2016 Jun 10;8(1):65. doi: 10.1186/s13073-016-0320-1.
6
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation.基于自然遗传变异的多重液滴单细胞 RNA 测序。
Nat Biotechnol. 2018 Jan;36(1):89-94. doi: 10.1038/nbt.4042. Epub 2017 Dec 11.
7
Kernel size-related genes revealed by an integrated eQTL analysis during early maize kernel development.通过早期玉米籽粒发育过程中的整合 eQTL 分析揭示与核大小相关的基因。
Plant J. 2019 Apr;98(1):19-32. doi: 10.1111/tpj.14193. Epub 2019 Jan 25.
8
Colon-specific eQTL analysis to inform on functional SNPs.用于提供功能性 SNP 信息的结肠特异性 eQTL 分析。
Br J Cancer. 2018 Oct;119(8):971-977. doi: 10.1038/s41416-018-0018-9. Epub 2018 Oct 4.
9
JEPEG: a summary statistics based tool for gene-level joint testing of functional variants.JEPEG：一种基于汇总统计量的功能变异基因水平联合检测工具。
Bioinformatics. 2015 Apr 15;31(8):1176-82. doi: 10.1093/bioinformatics/btu816. Epub 2014 Dec 12.
10
Mapping eQTL by leveraging multiple tissues and DNA methylation.通过利用多种组织和DNA甲基化来定位表达数量性状基因座
BMC Bioinformatics. 2017 Oct 18;18(1):455. doi: 10.1186/s12859-017-1856-9.

引用本文的文献

1
scQTLtools: An R/Bioconductor Package for Comprehensive Identification and Visualization of Single-Cell eQTLs.scQTLtools：一个用于全面识别和可视化单细胞表达数量性状基因座的R/Bioconductor软件包。
Biology (Basel). 2025 Jun 23;14(7):743. doi: 10.3390/biology14070743.
2
A brief guide to analyzing expression quantitative trait loci.表达数量性状位点分析简要指南。
Mol Cells. 2024 Nov;47(11):100139. doi: 10.1016/j.mocell.2024.100139. Epub 2024 Oct 22.
3
Expression quantitative trait locus studies in the era of single-cell omics.

本文引用的文献

1
DEsingle for detecting three types of differential expression in single-cell RNA-seq data.DEsingle 用于检测单细胞 RNA-seq 数据中的三种差异表达。
Bioinformatics. 2018 Sep 15;34(18):3223-3224. doi: 10.1093/bioinformatics/bty332.
2
ulfasQTL: an ultra-fast method of composite splicing QTL analysis.ulfasQTL：一种超快速的复合剪接QTL分析方法。
BMC Genomics. 2017 Jan 25;18(Suppl 1):963. doi: 10.1186/s12864-016-3258-1.
3
TreeQTL: hierarchical error control for eQTL findings.TreeQTL：eQTL研究结果的分层错误控制
单细胞组学时代的表达定量性状位点研究
Front Genet. 2023 May 22;14:1182579. doi: 10.3389/fgene.2023.1182579. eCollection 2023.
4
Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.阿尔茨海默病单细胞测序数据分析的生物信息学指南：综述、建议、实施和应用。
Mol Neurodegener. 2022 Mar 2;17(1):17. doi: 10.1186/s13024-022-00517-z.
5
Optimizing expression quantitative trait locus mapping workflows for single-cell studies.优化用于单细胞研究的表达数量性状基因座作图工作流程。
Genome Biol. 2021 Jun 24;22(1):188. doi: 10.1186/s13059-021-02407-x.
6
scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets.scReQTL：一种将单核苷酸变异与来自单个单细胞RNA测序数据集的基因表达相关联的方法。
BMC Genomics. 2021 Jan 8;22(1):40. doi: 10.1186/s12864-020-07334-y.
7
The single-cell eQTLGen consortium.单细胞 eQTLGen 联盟。
Elife. 2020 Mar 9;9:e52155. doi: 10.7554/eLife.52155.
Bioinformatics. 2016 Aug 15;32(16):2556-8. doi: 10.1093/bioinformatics/btw198. Epub 2016 Apr 19.
4
Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human Preimplantation Embryos.单细胞RNA测序揭示人类植入前胚胎中的谱系和X染色体动态变化。
Cell. 2016 May 5;165(4):1012-26. doi: 10.1016/j.cell.2016.03.023. Epub 2016 Apr 7.
5
Design and computational analysis of single-cell RNA-sequencing experiments.单细胞RNA测序实验的设计与计算分析
Genome Biol. 2016 Apr 7;17:63. doi: 10.1186/s13059-016-0927-y.
6
Discrete distributional differential expression (D3E)--a tool for gene expression analysis of single-cell RNA-seq data.离散分布差异表达（D3E）——一种用于单细胞RNA测序数据基因表达分析的工具。
BMC Bioinformatics. 2016 Feb 29;17:110. doi: 10.1186/s12859-016-0944-6.
7
G&T-seq: parallel sequencing of single-cell genomes and transcriptomes.G&T-seq：单细胞基因组和转录组的并行测序。
Nat Methods. 2015 Jun;12(6):519-22. doi: 10.1038/nmeth.3370. Epub 2015 Apr 27.
8
Integrated genome and transcriptome sequencing of the same cell.对同一细胞进行基因组和转录组的联合测序。
Nat Biotechnol. 2015 Mar;33(3):285-289. doi: 10.1038/nbt.3129. Epub 2015 Jan 19.
9
kruX: matrix-based non-parametric eQTL discovery.kruX：基于矩阵的非参数 eQTL 发现。
BMC Bioinformatics. 2014 Jan 14;15:11. doi: 10.1186/1471-2105-15-11.
10
Using gene expression noise to understand gene regulation.利用基因表达噪声理解基因调控。
Science. 2012 Apr 13;336(6078):183-7. doi: 10.1126/science.1216379.