Differential expression-the next generation and beyond

被引:18
作者
Auer, Paul L. [3 ]
Srivastava, Sanvesh [1 ]
Doerge, R. W. [1 ,2 ]
机构
[1] Purdue Univ, Dept Stat, W Lafayette, IN 47907 USA
[2] Purdue Univ, Stat Bioinformat Ctr, W Lafayette, IN 47907 USA
[3] Fred Hutchinson Canc Res Ctr, Seattle, WA 98104 USA
基金
美国国家科学基金会;
关键词
experimental design; statistical bioinformatics; curse of dimensionality; dimension reduction; RNA-seq; differential expression; FALSE DISCOVERY RATE; RNA-SEQ; STATISTICAL-METHODS; EMPIRICAL BAYES; DESIGN; NORMALIZATION; REPRODUCIBILITY;
D O I
10.1093/bfgp/elr041
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
RNA-sequencing (RNA-seq) technologies have not only pushed the boundaries of science, but also pushed the computational and analytic capacities of many laboratories. With respect to mapping and quantifying transcriptomes, RNA-seq has certainly established itself as the approach of choice. However, as the complexities of experiments continue to grow, there is still no standard practice that allows for design, processing, normalization, efficient dimension reduction and/or statistical analysis. With this in mind, we provide a brief review of some of the key challenges that are general to all RNA-seq experiments, namely experimental design, statistical analysis and dimensionality reduction.
引用
收藏
页码:57 / 62
页数:6
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