Gene set selection via LASSO penalized regression (SLPR).
Gene set selection via LASSO penalized regression (SLPR).
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DOI：
10.1093/nar/gkx291
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发表时间：
2017-07-07
影响因子：
14.9
通讯作者：
Amos CI
中科院分区：
文献类型：
--
作者：
Frost HR;Amos CI
基因集测试是一种重要的生物信息学技术，它解决了能力，解释和复制的挑战。为了更好地支持大型和高度重叠的基因集集合的分析，研究人员最近开发了一些多集方法，这些方法联合评估集合中的所有基因集，以识别功能独立集的简约组。不幸的是，目前的多集方法都使用基因和基因集活性的二元指标，并假设如果任何包含基因集是活跃的，则基因是活跃的。这种过于简单的模型限制了许多类型的基因组数据的性能。为了解决这个问题，我们开发了基因集选择通过LASSO惩罚回归（SLPR），一种新的映射的多集基因集测试惩罚多元线性回归。SLPR方法假设基因活性的连续测量值与集合中所有基因集的活性之间存在线性关系。正如我们通过模拟研究和使用MSigDB基因集的TCGA数据分析所证明的那样，当真实的生物过程通过连续的活性测量和基因与基因集之间的线性关联很好地近似时，SLPR方法优于现有的多集方法。
Gene set testing is an important bioinformatics technique that addresses the challenges of power, interpretation and replication. To better support the analysis of large and highly overlapping gene set collections, researchers have recently developed a number of multiset methods that jointly evaluate all gene sets in a collection to identify a parsimonious group of functionally independent sets. Unfortunately, current multiset methods all use binary indicators for gene and gene set activity and assume that a gene is active if any containing gene set is active. This simplistic model limits performance on many types of genomic data. To address this limitation, we developed gene set Selection via LASSO Penalized Regression (SLPR), a novel mapping of multiset gene set testing to penalized multiple linear regression. The SLPR method assumes a linear relationship between continuous measures of gene activity and the activity of all gene sets in the collection. As we demonstrate via simulation studies and the analysis of TCGA data using MSigDB gene sets, the SLPR method outperforms existing multiset methods when the true biological process is well approximated by continuous activity measures and a linear association between genes and gene sets.
DOI：
10.1093/biostatistics/kxt004
发表时间：
2013-07
期刊：
Biostatistics (Oxford, England)
影响因子：
--
作者：
Zhou YH;Barry WT;Wright FA
通讯作者：
Wright FA