Controlling mixed directional false discovery rate in multidimensional decisions with applications to microarray studies

• Haibing Zhao ^[1] ; Wing Kam Fung ^[2]
  1. [1] Shanghai University of Finance and Economics

     Shanghai University of Finance and Economics

     China

     
  2. [2] University of Hong Kong

     University of Hong Kong

     RAE de Hong Kong (China)

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 27, Nº. 2, 2018, págs. 316-337
• Idioma: inglés
• DOI: 10.1007/s11749-017-0547-1
• Texto completo no disponible (Saber más ...)
• Resumen

  • Time-course microarray experiments harvested samples at several time points. To reveal the dynamic gene expression changes over time, we need to identify the significant genes and detect the patterns of gene expressions, which may bring directional errors. Guo et al. (Biometrics 66(2):485–492, 2010) introduced a mixed directional false discovery rate (mdFDR) controlled procedure, which controls the sum of expected proportions of Type I and Type III errors among all rejections. In this paper, we develop weighted p value procedures for mdFDR control and give out some sufficient conditions to assure the (asymptotic) mdFDR control. Some weights and their estimators are illustrated to satisfy the sufficient conditions. The proposed weighted p value procedures are compared with the existing method by extensive simulations. Based on the proposed weighted p values procedure, we provide multiple CIs which control the false coverage-statement rate (FCR). We use the proposed methods to analyze the time-course microarray data studied in Lobenhofer et al. (Mol Endocrinol 16:1215–1229, 2002). Most of our findings are the same as those obtained by the existing method. In addition, we identify some other important genes, such as CDKN3 and NQO1.