A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics.
A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics.
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DOI：
10.1371/journal.pone.0191105
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Paules RS
中科院分区：
文献类型：
--
作者：
Mav D;Shah RR;Howard BE;Auerbach SS;Bushel PR;Collins JB;Gerhold DL;Judson RS;Karmaus AL;Maull EA;Mendrick DL;Merrick BA;Sipes NS;Svoboda D;Paules RS
基因表达的变化有助于揭示疾病过程的机制以及暴露于化学品、药物和环境因素所引起的毒性和对细胞反应的不利影响的作用方式。美国Tox21联邦合作项目目前在体外模型系统中通过定量高通量筛选（qHTS）对近10,000种化学物质的生物效应进行了量化，目前正在努力将基因表达谱分析纳入现有的一系列分析中。使用微阵列或RNA-Seq对大量样本进行全转录组分析目前成本过高。因此，Tox21项目正在寻求一种高通量转录组学（HTT）方法，专注于对精心挑选的转录组子集的基因表达进行靶向检测，这可能会将成本降低10倍，从而允许分析更多的样本。为了确定最佳的转录组子集，需要寻找以下基因：(1)代表高度多样化的生物空间，(2)能够作为未测量基因表达变化的代理，以及(3)足以覆盖描述良好的生物学途径。本文提出了一种结合数据驱动和知识驱动概念的基因选择混合方法。我们的方法是模块化的，适用于任何物种，并有助于对性能进行稳健的定量评估。特别是，我们能够进行基因选择，使最终的“哨兵基因”集充分代表来自分子特征数据库（MSigDB v4.0）的所有已知的典型途径，并可用于推断转录组其余部分的表达变化。由此产生的计算模型允许我们选择一个纯数据驱动的1500个前哨基因子集，称为S1500集，然后使用知识驱动的其他基因选择来增加它，以创建最终的S1500+基因集。我们的研究结果表明，所选择的前哨基因可以用来准确地预测所研究样本的途径扰动和生物学关系。
Changes in gene expression can help reveal the mechanisms of disease processes and the mode of action for toxicities and adverse effects on cellular responses induced by exposures to chemicals, drugs and environment agents. The U.S. Tox21 Federal collaboration, which currently quantifies the biological effects of nearly 10,000 chemicals via quantitative high-throughput screening(qHTS) in in vitro model systems, is now making an effort to incorporate gene expression profiling into the existing battery of assays. Whole transcriptome analyses performed on large numbers of samples using microarrays or RNA-Seq is currently cost-prohibitive. Accordingly, the Tox21 Program is pursuing a high-throughput transcriptomics (HTT) method that focuses on the targeted detection of gene expression for a carefully selected subset of the transcriptome that potentially can reduce the cost by a factor of 10-fold, allowing for the analysis of larger numbers of samples. To identify the optimal transcriptome subset, genes were sought that are (1) representative of the highly diverse biological space, (2) capable of serving as a proxy for expression changes in unmeasured genes, and (3) sufficient to provide coverage of well described biological pathways. A hybrid method for gene selection is presented herein that combines data-driven and knowledge-driven concepts into one cohesive method. Our approach is modular, applicable to any species, and facilitates a robust, quantitative evaluation of performance. In particular, we were able to perform gene selection such that the resulting set of “sentinel genes” adequately represents all known canonical pathways from Molecular Signature Database (MSigDB v4.0) and can be used to infer expression changes for the remainder of the transcriptome. The resulting computational model allowed us to choose a purely data-driven subset of 1500 sentinel genes, referred to as the S1500 set, which was then augmented using a knowledge-driven selection of additional genes to create the final S1500+ gene set. Our results indicate that the sentinel genes selected can be used to accurately predict pathway perturbations and biological relationships for samples under study.
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影响因子：
7.4
作者：
Attene-Ramos, Matias S.;Miller, Nicole;Huang, Ruili;Michael, Sam;Itkin, Misha;Kavlock, Robert J.;Austin, Christopher P.;Shinn, Paul;Simeonov, Anton;Tice, Raymond R.;Xia, Menghang
通讯作者：
Xia, Menghang
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
10.4
作者：
Tice RR;Austin CP;Kavlock RJ;Bucher JR
通讯作者：
Bucher JR
影响因子：
2.1
作者：
Irizarry, RA;Hobbs, B;Speed, TP
通讯作者：
Speed, TP
影响因子：
14.9
作者：
Edgar, R;Domrachev, M;Lash, AE
通讯作者：
Lash, AE