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
  • [41] Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis
    Sun, Shiquan
    Zhu, Jiaqiang
    Ma, Ying
    Zhou, Xiang
    GENOME BIOLOGY, 2019, 20 (01)
  • [42] Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data
    Peipei Li
    Yongjun Piao
    Ho Sun Shon
    Keun Ho Ryu
    BMC Bioinformatics, 16
  • [43] Single-cell RNA-seq and bulk RNA-seq explore the prognostic value of exhausted T cells in hepatocellular carcinoma
    Tang, Xiaolong
    Miao, Yandong
    Yang, Lixia
    Ha, Wuhua
    Li, Zheng
    Mi, Denghai
    IET SYSTEMS BIOLOGY, 2023, 17 (04) : 228 - 244
  • [44] Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data
    Li, Peipei
    Piao, Yongjun
    Shon, Ho Sun
    Ryu, Keun Ho
    BMC BIOINFORMATICS, 2015, 16
  • [45] Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists
    Zhu, Xun
    Wolfgruber, Thomas K.
    Tasato, Austin
    Arisdakessian, Cedric
    Garmire, David G.
    Garmire, Lana X.
    GENOME MEDICINE, 2017, 9
  • [46] Classification of low quality cells from single-cell RNA-seq data
    Ilicic, Tomislav
    Kim, Jong Kyoung
    Kolodziejczyk, Aleksandra A.
    Bagger, Frederik Otzen
    McCarthy, Davis James
    Marioni, John C.
    Teichmann, Sarah A.
    GENOME BIOLOGY, 2016, 17
  • [47] Analysis of Single-Cell RNA-seq Data by Clustering Approaches
    Zhu, Xiaoshu
    Li, Hong-Dong
    Guo, Lilu
    Wu, Fang-Xiang
    Wang, Jianxin
    CURRENT BIOINFORMATICS, 2019, 14 (04) : 314 - 322
  • [48] Computational methods for transcriptome annotation and quantification using RNA-seq
    Garber, Manuel
    Grabherr, Manfred G.
    Guttman, Mitchell
    Trapnell, Cole
    NATURE METHODS, 2011, 8 (06) : 469 - 477
  • [49] Improving Single-Cell RNA-seq Clustering by Integrating Pathways
    Zhang, Chenxing
    Gao, Lin
    Wang, Bingbo
    Gao, Yong
    BRIEFINGS IN BIOINFORMATICS, 2021, 22 (06)
  • [50] Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels the heterogeneity of cancer-associated fibroblasts in TNBC
    Wu, Xiaoqing
    Lu, Wenping
    Zhang, Weixuan
    Zhang, Dongni
    Mei, Heting
    Zhang, Mengfan
    Cui, Yongjia
    Zhuo, Zhili
    AGING-US, 2023, 15 (21): : 12674 - 12697
12345