LCox:使用纵向基因表达数据选择与生存结果相关的基因的工具。

IF 0.9 4区 数学 Q3 Mathematics
Jiehuan Sun, Jose D Herazo-Maya, Jane-Ling Wang, Naftali Kaminski, Hongyu Zhao
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引用次数: 0

摘要

纵向基因组学数据和生存结果在生物医学研究中很常见,其中基因组学数据通常是高维的。选择与生存结果相关的信息性纵向生物标志物(如基因)是非常有趣的。在本文中,我们开发了一个计算效率高的工具LCox,用于使用纵向基因组学数据选择与生存结果相关的信息性生物标志物。LCox在检测纵向生物标志物与生存结果之间不同形式的依赖性方面具有强大的功能。我们通过广泛的仿真研究表明,与现有方法相比,LCox的性能有所提高。此外,通过将LCox应用于特发性肺纤维化患者的数据集,我们能够识别出具有生物学意义的基因,而所有其他方法都无法发现任何基因。执行LCox的R包可以在https://CRAN.R-project.org/package=LCox上免费获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data.

Longitudinal genomics data and survival outcome are common in biomedical studies, where the genomics data are often of high dimension. It is of great interest to select informative longitudinal biomarkers (e.g. genes) related to the survival outcome. In this paper, we develop a computationally efficient tool, LCox, for selecting informative biomarkers related to the survival outcome using the longitudinal genomics data. LCox is powerful to detect different forms of dependence between the longitudinal biomarkers and the survival outcome. We show that LCox has improved performance compared to existing methods through extensive simulation studies. In addition, by applying LCox to a dataset of patients with idiopathic pulmonary fibrosis, we are able to identify biologically meaningful genes while all other methods fail to make any discovery. An R package to perform LCox is freely available at https://CRAN.R-project.org/package=LCox.

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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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