A randomized approach to speed up the analysis of large-scale read-count data in the application of CNV detection Public Deposited

Downloadable Content

Download PDF
Creator
  • Sun, Wei
    • Other Affiliation: Biostatistics Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, 19024 Seattle, USA
  • Szatkiewicz, Jin
    • Affiliation: School of Medicine, Department of Genetics
  • Wang, WeiBo
    • Affiliation: College of Arts and Sciences, Department of Computer Science
  • Wang, Wei
    • Other Affiliation: Department of Computer Science, University of California, Los Angeles, 580 Portola Plaza, 90095-1596 Los Angeles, USA
Abstract
  • Abstract Background The application of high-throughput sequencing in a broad range of quantitative genomic assays (e.g., DNA-seq, ChIP-seq) has created a high demand for the analysis of large-scale read-count data. Typically, the genome is divided into tiling windows and windowed read-count data is generated for the entire genome from which genomic signals are detected (e.g. copy number changes in DNA-seq, enrichment peaks in ChIP-seq). For accurate analysis of read-count data, many state-of-the-art statistical methods use generalized linear models (GLM) coupled with the negative-binomial (NB) distribution by leveraging its ability for simultaneous bias correction and signal detection. However, although statistically powerful, the GLM+NB method has a quadratic computational complexity and therefore suffers from slow running time when applied to large-scale windowed read-count data. In this study, we aimed to speed up substantially the GLM+NB method by using a randomized algorithm and we demonstrate here the utility of our approach in the application of detecting copy number variants (CNVs) using a real example. Results We propose an efficient estimator, the randomized GLM+NB coefficients estimator (RGE), for speeding up the GLM+NB method. RGE samples the read-count data and solves the estimation problem on a smaller scale. We first theoretically validated the consistency and the variance properties of RGE. We then applied RGE to GENSENG, a GLM+NB based method for detecting CNVs. We named the resulting method as “R-GENSENG". Based on extensive evaluation using both simulated and empirical data, we concluded that R-GENSENG is ten times faster than the original GENSENG while maintaining GENSENG’s accuracy in CNV detection. Conclusions Our results suggest that RGE strategy developed here could be applied to other GLM+NB based read-count analyses, i.e. ChIP-seq data analysis, to substantially improve their computational efficiency while preserving the analytic power.
Date of publication
Identifier
  • doi:10.1186/s12859-018-2077-6
Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • The Author(s)
Language
  • English
Bibliographic citation
  • BMC Bioinformatics. 2018 Mar 01;19(1):74
Publisher
  • BioMed Central
Parents:

This work has no parents.

Items