The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance.
The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance.
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DOI：
10.1038/nbt.3001
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发表时间：
2014-09
影响因子：
46.9
通讯作者：
Tong, Weida
中科院分区：
文献类型：
--
作者：
Wang, Charles;Gong, Binsheng;Bushel, Pierre R.;Thierry-Mieg, Jean;Thierry-Mieg, Danielle;Xu, Joshua;Fang, Hong;Hong, Huixiao;Shen, Jie;Su, Zhenqiang;Meehan, Joe;Li, Xiaojin;Yang, Lu;Li, Haiqing;Labaj, Pawel P.;Kreil, David P.;Megherbi, Dalila;Gaj, Stan;Caiment, Florian;van Delft, Joost;Kleinjans, Jos;Scherer, Andreas;Devanarayan, Viswanath;Wang, Jian;Yang, Yong;Qian, Hui-Rong;Lancashire, Lee J.;Bessarabova, Marina;Nikolsky, Yuri;Furlanello, Cesare;Chierici, Marco;Albanese, Davide;Jurman, Giuseppe;Riccadonna, Samantha;Filosi, Michele;Visintainer, Roberto;Zhang, Ke K.;Li, Jainying;Hsieh, Jui-Hua;Svoboda, Daniel L.;Fuscoe, James C.;Deng, Youping;Shi, Leming;Paules, Richard S.;Auerbach, Scott S.;Tong, Weida
RNA-seq有助于无偏见的全基因组基因表达谱分析。然而，它与成熟的微阵列平台的一致性必须严格评估，以确保在临床和监管应用中的使用。在这里，我们使用一个全面的研究设计，从同一组大鼠肝脏样本中生成Illumina RNA-seq和Affyellow微阵列数据，这些大鼠肝脏样本受到27种代表多种作用模式（MOA）的化学物质的不同程度的干扰。差异表达基因（DEG）或富集途径方面的跨平台一致性与治疗效应大小、基因表达丰度和MOA的生物学复杂性高度相关。RNA-seq在定量PCR的DEG验证中优于微阵列（90%对76%），主要收获是其对低表达基因的准确性提高。尽管如此，来自两个平台的预测分类器表现相似。因此，研究的终点及其生物学复杂性、转录本丰度和预期应用是转录组学研究和决策的重要因素。
RNA-seq facilitates unbiased genome-wide gene-expression profiling. However, its concordance with the well-established microarray platform must be rigorously assessed for confident uses in clinical and regulatory application. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same set of liver samples of rats under varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOA). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is highly correlated with treatment effect size, gene-expression abundance and the biological complexity of the MOA. RNA-seq outperforms microarray (90% versus 76%) in DEG verification by quantitative PCR and the main gain is its improved accuracy for low expressed genes. Nonetheless, predictive classifiers derived from both platforms performed similarly. Therefore, the endpoint studied and its biological complexity, transcript abundance, and intended application are important factors in transcriptomic research and for decision-making.
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DOI：
10.1093/bioinformatics/btr247
发表时间：
2011-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Łabaj PP;Leparc GG;Linggi BE;Markillie LM;Wiley HS;Kreil DP
通讯作者：
Kreil DP
DOI：
10.1073/pnas.011404098
发表时间：
2001-01-02
影响因子：
11.1
作者：
Li, C;Wong, WH
通讯作者：
Wong, WH
使用不同微阵列平台生成的预测特征基因和分类器的一致性
DOI：
10.1038/tpj.2010.34
发表时间：
2010-08
期刊：
The pharmacogenomics journal
影响因子：
--
作者：
Fan X;Lobenhofer EK;Chen M;Shi W;Huang J;Luo J;Zhang J;Walker SJ;Chu TM;Li L;Wolfinger R;Bao W;Paules RS;Bushel PR;Li J;Shi T;Nikolskaya T;Nikolsky Y;Hong H;Deng Y;Cheng Y;Fang H;Shi L;Tong W
通讯作者：
Tong W
影响因子：
2.7
作者：
Raghavachari N;Barb J;Yang Y;Liu P;Woodhouse K;Levy D;O'Donnell CJ;Munson PJ;Kato GJ
通讯作者：
Kato GJ
影响因子：
3.7
作者：
Mooney M;Bond J;Monks N;Eugster E;Cherba D;Berlinski P;Kamerling S;Marotti K;Simpson H;Rusk T;Tembe W;Legendre C;Benson H;Liang W;Webb CP
通讯作者：
Webb CP