Comparison of Microarrays and RNA-Seq for Gene Expression Analyses of Dose-Response Experiments

被引:37
|
作者
Black, Michael B. [1 ]
Parks, Bethany B. [1 ]
Pluta, Linda [1 ]
Chu, Tzu-Ming [2 ]
Allen, Bruce C. [3 ]
Wolfinger, Russell D. [2 ]
Thomas, Russell S. [1 ]
机构
[1] Hamner Inst Hlth Sci, Res Triangle Pk, NC 27709 USA
[2] SAS Inst Inc, Cary, NC 27513 USA
[3] Bruce Allen Consulting, Chapel Hill, NC 27514 USA
关键词
RT-PCR; bioinformatics; microarray; toxicogenomics; RNA-seq; dose response; risk assessment; CHEMICAL RISK-ASSESSMENT; DIFFERENTIAL EXPRESSION; DEPENDENT TRANSITIONS; EXPOSURE; RAT; NORMALIZATION; FORMALDEHYDE; CANCER;
D O I
10.1093/toxsci/kft249
中图分类号
R99 [毒物学(毒理学)];
学科分类号
100405 ;
摘要
Relative to microarrays, RNA-seq has been reported to offer higher precision estimates of transcript abundance, a greater dynamic range, and detection of novel transcripts. However, previous comparisons of the 2 technologies have not covered dose-response experiments that are relevant to toxicology. Male F344 rats were exposed for 13 weeks to 5 doses of bromobenzene, and liver gene expression was measured using both microarrays and RNA-seq. Multiple normalization methods were evaluated for each technology, and gene expression changes were statistically analyzed using both analysis of variance and benchmark dose (BMD). Fold-change values were highly correlated between the 2 technologies, whereas the log p values showed lower correlation. RNA-seq detected fewer statistically significant genes at lower doses, but more significant genes based on fold change except when a negative binomial transformation was applied. Overlap in genes significant by both p value and fold change was approximately 30%40%. Random sampling of the RNA-seq data showed an equivalent number of differentially expressed genes compared with microarrays at similar to 5 million reads. Quantitative RT-PCR of differentially expressed genes uniquely identified by each technology showed a high degree of confirmation when both fold change and p value were considered. The mean dose-response expression of each gene was highly correlated between technologies, whereas estimates of sample variability and gene-based BMD values showed lower correlation. Differences in BMD estimates and statistical significance may be due, in part, to differences in the dynamic range of each technology and the degree to which normalization corrects genes at either end of the scale.
引用
收藏
页码:385 / 403
页数:19
相关论文
共 50 条
  • [21] A comparison of strategies for generating artificial replicates in RNA-seq experiments
    Saremi, Babak
    Gusmag, Frederic
    Distl, Ottmar
    Schaarschmidt, Frank
    Metzger, Julia
    Becker, Stefanie
    Jung, Klaus
    SCIENTIFIC REPORTS, 2022, 12 (01)
  • [22] RNA-Seq Gene Profiling - A Systematic Empirical Comparison
    Fonseca, Nuno A.
    Marioni, John
    Brazma, Alvis
    PLOS ONE, 2014, 9 (09):
  • [23] On Differential Gene Expression Using RNA-Seq Data
    Lee, Juhee
    Ji, Yuan
    Liang, Shoudan
    Cai, Guoshuai
    Mueller, Peter
    CANCER INFORMATICS, 2011, 10 : 205 - 215
  • [24] Robustness of differential gene expression analysis of RNA-seq
    Stupnikov, A.
    McInerney, C. E.
    Savage, K. I.
    McIntosh, S. A.
    Emmert-Streib, F.
    Kennedy, R.
    Salto-Tellez, M.
    Prise, K. M.
    McArt, D. G.
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2021, 19 : 3470 - 3481
  • [25] Correction: Corrigendum: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks
    Cole Trapnell
    Adam Roberts
    Loyal Goff
    Geo Pertea
    Daehwan Kim
    David R Kelley
    Harold Pimentel
    Steven L Salzberg
    John L Rinn
    Lior Pachter
    Nature Protocols, 2014, 9 : 2513 - 2513
  • [26] RNA-Seq Identifies Key Reproductive Gene Expression Alterations in Response to Cadmium Exposure
    Hu, Hanyang
    Lu, Xing
    Cen, Xiang
    Chen, Xiaohua
    Li, Feng
    Zhong, Shan
    BIOMED RESEARCH INTERNATIONAL, 2014, 2014
  • [27] Length bias correction for RNA-seq data in gene set analyses
    Gao, Liyan
    Fang, Zhide
    Zhang, Kui
    Zhi, Degui
    Cui, Xiangqin
    BIOINFORMATICS, 2011, 27 (05) : 662 - 669
  • [28] Gene set analysis controlling for length bias in RNA-seq experiments
    Xing Ren
    Qiang Hu
    Song Liu
    Jianmin Wang
    Jeffrey C. Miecznikowski
    BioData Mining, 10
  • [29] Time Series Expression Analyses Using RNA-seq: A Statistical Approach
    Oh, Sunghee
    Song, Seongho
    Grabowski, Gregory
    Zhao, Hongyu
    Noonan, James P.
    BIOMED RESEARCH INTERNATIONAL, 2013, 2013
  • [30] Gene set analysis controlling for length bias in RNA-seq experiments
    Ren, Xing
    Hu, Qiang
    Liu, Song
    Wang, Jianmin
    Miecznikowski, Jeffrey C.
    BIODATA MINING, 2017, 10 : 1 - 18