publication . Preprint . Other literature type . 2018

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

Hu, Yue; Zhang, Xuegong;
Open Access
  • Published: 19 Dec 2018
  • Publisher: Cold Spring Harbor Laboratory
Abstract
<jats:p>With the development of single-cell sequencing technologies, parallel sequencing the transcriptome and genome is becoming available and will bring us the opportunity to uncover association between genotype and phenotype at single-cell level. Due to the special characteristics of single-cell sequencing data, new method is needed to identify eQTL from single-cell data. 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 wit...
Related Organizations

7. Petropoulos, et al. (2016) Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human 9. Sun W. (2012) A statistical framework for eQTL mapping using RNA-seq data. Biometrics, 68(1): 1-11.

10. Zhun Miao, and Xuegong Zhang. (2016) Differential expression analyses for single-cell RNA-Seq: old 11. Miao Z, et al (2018), DEsingle for detecting three types of differential expression in single-cell RNA-seq Achim Zeileis, Christian Kleiber, Simon Jackman (2008). Regression Models for Count Data in R. Journal of Statistical Software 27(8). URL http://www.jstatsoft.org/v27/i08/.

Anders S and Huber W (2010). Differential expression analysis for sequence count data. Genome Biology, 11, pp. R106. doi: 10.1186/gb-2010-11-10-r106 Benjamini, Yoav; Hochberg, Yosef (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B. 57 (1): 125-133. JSTOR 2346101.

Nature biotechnology, 2015, 33(3): 285-289.

Peterson C B, Bogomolov M, Benjamini Y, et al. TreeQTL: hierarchical error control for eQTL findings[J]. Bioinformatics, 2016: btw198.

Abstract
<jats:p>With the development of single-cell sequencing technologies, parallel sequencing the transcriptome and genome is becoming available and will bring us the opportunity to uncover association between genotype and phenotype at single-cell level. Due to the special characteristics of single-cell sequencing data, new method is needed to identify eQTL from single-cell data. 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 wit...
Related Organizations

7. Petropoulos, et al. (2016) Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human 9. Sun W. (2012) A statistical framework for eQTL mapping using RNA-seq data. Biometrics, 68(1): 1-11.

10. Zhun Miao, and Xuegong Zhang. (2016) Differential expression analyses for single-cell RNA-Seq: old 11. Miao Z, et al (2018), DEsingle for detecting three types of differential expression in single-cell RNA-seq Achim Zeileis, Christian Kleiber, Simon Jackman (2008). Regression Models for Count Data in R. Journal of Statistical Software 27(8). URL http://www.jstatsoft.org/v27/i08/.

Anders S and Huber W (2010). Differential expression analysis for sequence count data. Genome Biology, 11, pp. R106. doi: 10.1186/gb-2010-11-10-r106 Benjamini, Yoav; Hochberg, Yosef (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B. 57 (1): 125-133. JSTOR 2346101.

Nature biotechnology, 2015, 33(3): 285-289.

Peterson C B, Bogomolov M, Benjamini Y, et al. TreeQTL: hierarchical error control for eQTL findings[J]. Bioinformatics, 2016: btw198.

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