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DCGL: an R package for identifying differentially coexpressed genes and links from gene expression microarray data

机译:DCGL:R程序包,用于识别差异共表达的基因和基因表达微阵列数据中的链接

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摘要

Summary: Gene coexpression analysis was developed to explore gene interconnection at the expression level from a systems perspective, and differential coexpression analysis (DCEA), which examines the change in gene expression correlation between two conditions, was accordingly designed as a complementary technique to traditional differential expression analysis (DEA). Since there is a shortage of DCEA tools, we implemented in an R package ‘DCGL’ five DCEA methods for identification of differentially coexpressed genes and differentially coexpressed links, including three currently popular methods and two novel algorithms described in a companion paper. DCGL can serve as an easy-to-use tool to facilitate differential coexpression analyses.
机译:简介:开发了基因共表达分析以从系统角度探讨表达水平上的基因互连,并相应地设计了差异共表达分析(DCEA),该技术检查两种条件之间基因表达相关性的变化,以此作为传统差异的补充技术。表达分析(DEA)。由于缺少DCEA工具,我们在R包“ DCGL”中实施了五种DCEA方法,用于鉴定差异共表达的基因和差异共表达的链接,其中包括三种当前流行的方法和在伴侣论文中描述的两种新颖算法。 DCGL可以作为易于使用的工具来促进差异共表达分析。

著录项

  • 来源
    《Bioinformatics》 |2010年第20期|p.2637-2638|共2页
  • 作者

    Yuan-Yuan Li;

  • 作者单位
  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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