Microarray Analysis Using Rank Order Statistics for ARCH Residual Empirical Process
作者: Hiroko Kato SolvangMasanobu Taniguchi
作者单位: 1Marine Mammals Research Group, Institute of Marine Research, Bergen, Norway
2Deparment of Applied Mathematics, Waseda University, Tokyo, Japan
刊名: Open Journal of Statistics, 2017, Vol.07 (01), pp.54-71
来源数据库: Scientific Research Publishing Journal
DOI: 10.4236/ojs.2017.71005
原始语种摘要: Statistical two-group comparisons are widely used to identify the significant differentially expressed (DE) signatures against a therapy response for microarray data analysis. We applied a rank order statistics based on an Autoregressive Conditional Heteroskedasticity (ARCH) residual empirical process to DE analysis. This approach was considered for simulation data and publicly available datasets, and was compared with two-group comparison by original data and Auto-regressive (AR) residual. The significant DE genes by the ARCH and AR residuals were reduced by about 20% - 30% to these genes by the original data. Almost 100% of the genes by ARCH are covered by the genes by the original data unlike the genes by AR residuals. GO enrichment and Pathway analyses indicate the consistent...
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  • 当众 有效的
  • Analysis 分析
  • empirical 经验的
  • regressive 退化的
  • statistics 统计
  • original 原图
  • ordinal 序数
  • approach 
  • significant 有效的
  • publicly 有效的
  • enrichment 富集