Assessment of Single Cell RNA-Seq Normalization Methods

被引：7
|
作者：
Ding, Bo [1 ]
Zheng, Lina [1 ]
Wang, Wei [1 ,2 ]
机构：
[1] Univ Calif San Diego, Dept Chem & Biochem, La Jolla, CA 92093 USA
[2] Univ Calif San Diego, Dept Cellular & Mol Med, La Jolla, CA 92093 USA
来源：
G3-GENES GENOMES GENETICS | 2017年 / 7卷 / 07期
基金：
美国国家卫生研究院;
关键词：
normalization; scRNA; statistical index; DIFFERENTIAL EXPRESSION ANALYSIS; HETEROGENEITY; NOISE;
D O I：
10.1534/g3.117.040683
中图分类号：
Q3 [遗传学];
学科分类号：
071007 ; 090102 ;
摘要：
We have assessed the performance of seven normalization methods for single cell RNA-seq using data generated from dilution of RNA samples. Our analyses showed that methods considering spike-in External RNA Control Consortium (ERCC) RNA molecules significantly outperformed those not considering ERCCs. This work provides a guidance of selecting normalization methods to remove technical noise in single cell RNA-seq data.
引用
收藏
页码：2039 / 2045
页数：7
相关论文
共 50 条
  • [1] Normalization Methods on Single-Cell RNA-seq Data: An Empirical Survey
    Lytal, Nicholas
    Ran, Di
    An, Lingling
    FRONTIERS IN GENETICS, 2020, 11
  • [2] Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
    Cole, Michael B.
    Risso, Davide
    Wagner, Allon
    DeTomaso, David
    Ngai, John
    Purdom, Elizabeth
    Dudoit, Sandrine
    Yosef, Nir
    CELL SYSTEMS, 2019, 8 (04) : 315 - +
  • [3] SCnorm: robust normalization of single-cell RNA-seq data
    Bacher, Rhonda
    Chu, Li-Fang
    Leng, Ning
    Gasch, Audrey P.
    Thomson, James A.
    Stewart, Ron M.
    Newton, Michael
    Kendziorski, Christina
    NATURE METHODS, 2017, 14 (06) : 584 - +
  • [4] The Impact of Normalization Methods on RNA-Seq Data Analysis
    Zyprych-Walczak, J.
    Szabelska, A.
    Handschuh, L.
    Gorczak, K.
    Klamecka, K.
    Figlerowicz, M.
    Siatkowski, I.
    BIOMED RESEARCH INTERNATIONAL, 2015, 2015
  • [5] Resistant Fit Regression Normalization for Single-cell RNA-seq Data
    Kuang, Da
    Kim, Junhyong
    2020 IEEE 20TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE 2020), 2020, : 236 - 240
  • [6] Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R
    McCarthy, Davis J.
    Campbell, Kieran R.
    Lun, Aaron T. L.
    Wills, Quin F.
    BIOINFORMATICS, 2017, 33 (08) : 1179 - 1186
  • [7] Non-linear Normalization for Non-UMI Single Cell RNA-Seq
    Wu, Zhijin
    Su, Kenong
    Wu, Hao
    FRONTIERS IN GENETICS, 2021, 12
  • [8] Emerging deep learning methods for single-cell RNA-seq data analysis
    Zheng, Jie
    Wang, Ke
    QUANTITATIVE BIOLOGY, 2019, 7 (04) : 247 - 254
  • [9] An interpretable framework for clustering single-cell RNA-Seq datasets
    Zhang, Jesse M.
    Fan, Jue
    Fan, Christina
    Rosenfeld, David
    Tse, David N.
    BMC BIOINFORMATICS, 2018, 19
  • [10] Comparative Analysis of Single-Cell RNA-seq Cluster Methods
    Fang, Jingwen
    Yin, Zhaohua
    Guo, Chuang
    2ND INTERNATIONAL CONFERENCE ON FRONTIERS OF BIOLOGICAL SCIENCES AND ENGINEERING (FSBE 2019), 2020, 2208
12345