SPARSim single cell: a count data simulator for scRNA-seq data

Author:

Baruzzo Giacomo1,Patuzzi Ilaria12,Di Camillo Barbara13

Affiliation:

1. Department of Information Engineering, University of Padova, Padova, Italy

2. Microbial Ecology Unit, Istituto Zooprofilattico Sperimentale delle Venezie, Padova, Italy

3. CRIBI Innovative Biotechnology Center, University of Padova, Padova, Italy

Abstract

AbstractMotivationSingle cell RNA-seq (scRNA-seq) count data show many differences compared with bulk RNA-seq count data, making the application of many RNA-seq pre-processing/analysis methods not straightforward or even inappropriate. For this reason, the development of new methods for handling scRNA-seq count data is currently one of the most active research fields in bioinformatics. To help the development of such new methods, the availability of simulated data could play a pivotal role. However, only few scRNA-seq count data simulators are available, often showing poor or not demonstrated similarity with real data.ResultsIn this article we present SPARSim, a scRNA-seq count data simulator based on a Gamma-Multivariate Hypergeometric model. We demonstrate that SPARSim allows to generate count data that resemble real data in terms of count intensity, variability and sparsity, performing comparably or better than one of the most used scRNA-seq simulator, Splat. In particular, SPARSim simulated count matrices well resemble the distribution of zeros across different expression intensities observed in real count data.Availability and implementationSPARSim R package is freely available at http://sysbiobig.dei.unipd.it/? q=SPARSim and at https://gitlab.com/sysbiobig/sparsim.Supplementary informationSupplementary data are available at Bioinformatics online.

Funder

PROACTIVE 2017 ‘From Single-Cell to Multi-Cells Information Systems Analysis’

Department of Information Engineering, University of Padova

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference56 articles.

1. The statistical analysis of compositional data;Aitchison;J. R. Stat. Soc. Ser. B (Methodological),1982

2. Differential expression analysis for sequence count data;Anders;Genome Biol,2010

3. HTSeq—a Python framework to work with high-throughput sequencing data;Anders;Bioinformatics,2015

4. Delineating biological and technical variance in single cell expression data;Arzalluz-Luque;Int. J. Biochem. Cell Biol,2017

5. SCnorm: robust normalization of single-cell RNA-seq data;Bacher;Nat. Methods,2017

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3