Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges.
Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges.
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DOI：
10.3390/e22040427
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发表时间：
2020-04-10
期刊：
影响因子：
--
通讯作者：
Rai SN
中科院分区：
文献类型：
--
作者：
Das S;McClain CJ;Rai SN
在过去的十年中，基因集分析已成为通过基因表达和基因关联研究深入了解疾病潜在复杂生物学的首选。它还降低了统计分析的复杂性，提高了所得结果的解释力。虽然基因集分析方法被广泛用于基因表达和全基因组关联数据分析，但这些方法的统计结构和共同步骤尚未得到全面讨论，这限制了它们的实用性。在这篇文章中，我们提供了一个全面的概述，统计结构和步骤的基因集分析方法用于微阵列，RNA测序和全基因组关联数据分析。此外，我们还根据基因组研究的类型、零假设、抽样模型和检验统计量的性质等对基因集分析方法和工具进行了分类。我们没有单独回顾基因集分析方法，而是提供了这些方法的逐代演变微阵列、RNA测序和全基因组关联研究，并讨论了它们的相对优点和局限性。在这里，我们确定了当前基因集分析中的关键生物学和统计学挑战，这些挑战将由统计学家和生物学家共同解决，以开发下一代基因集分析方法。此外，这项研究将作为一个目录，并提供指导方针，基因组研究人员和实验生物学家选择适当的基因集分析方法的基础上，几个因素。
Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.
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影响因子：
5.8
作者：
Al-Shahrour, F;Díaz-Uriarte, R;Dopazo, J
通讯作者：
Dopazo, J
影响因子：
5.8
作者：
Berriz, GF;King, OD;Roth, FP
通讯作者：
Roth, FP
DOI：
10.1093/bioinformatics/btp101
发表时间：
2009-04-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Bindea G;Mlecnik B;Hackl H;Charoentong P;Tosolini M;Kirilovsky A;Fridman WH;Pagès F;Trajanoski Z;Galon J
通讯作者：
Galon J
影响因子：
3.7
作者：
Das S;Meher PK;Rai A;Bhar LM;Mandal BN
通讯作者：
Mandal BN
影响因子：
12.3
作者：
Conesa A;Madrigal P;Tarazona S;Gomez-Cabrero D;Cervera A;McPherson A;Szcześniak MW;Gaffney DJ;Elo LL;Zhang X;Mortazavi A
通讯作者：
Mortazavi A