Identification of differentially expressed genes by means of outlier detection

[1] Quackenbush J(2006)Microarray analysis and tumor classification N Engl J Med 354 2463-72

[2] Tusher VG(2001)Significance analysis of microarrays applied to the ionizing radiation response Proc Natl Acad Sci U S A 90 5116-21

[3] Tibshirani R(2014)Empirical evaluation of consistency and accuracy of methods to detect differentially expressed genes based on microarray Comput Biol Med 46 1-10

[4] Chu G(2004)Linear models and empirical bayes methods for assessing differential expression in microarray experiments Stat Appl Genet Mol Biol 3 1-25

[5] Yang D(2015)Limma powers differential expression analyses for rna-sequencing and microarray studies Nucleic Acids Res 43 47-86

[6] Parrish RS(2004)Bioconductor: open software development for computational biology and bioinformatics Genome Biol 5 80-65

[7] Brock GN(2002)Empirical bayes methods and false discovery rates for microarrays Genet Epidemiol 23 70-300

[8] Smyth GK(2006)Microarray data analysis: from disarray to consolidation and consensus Nat Rev Genet 7 55-9

[9] Ritchie ME(1995)Controlling the false discovery rate: a practical and powerful approach to multiple testing J R Stat Soc B 57 289-8

[10] Phipson B(2006)Rat toxicogenomic study reveals analytical consistency across microarray platforms Nat Biotechnol 24 1162-17

[1] Quackenbush J(2006)Microarray analysis and tumor classification N Engl J Med 354 2463-72

[2] Tusher VG(2001)Significance analysis of microarrays applied to the ionizing radiation response Proc Natl Acad Sci U S A 90 5116-21

[3] Tibshirani R(2014)Empirical evaluation of consistency and accuracy of methods to detect differentially expressed genes based on microarray Comput Biol Med 46 1-10

[4] Chu G(2004)Linear models and empirical bayes methods for assessing differential expression in microarray experiments Stat Appl Genet Mol Biol 3 1-25

[5] Yang D(2015)Limma powers differential expression analyses for rna-sequencing and microarray studies Nucleic Acids Res 43 47-86

[6] Parrish RS(2004)Bioconductor: open software development for computational biology and bioinformatics Genome Biol 5 80-65

[7] Brock GN(2002)Empirical bayes methods and false discovery rates for microarrays Genet Epidemiol 23 70-300

[8] Smyth GK(2006)Microarray data analysis: from disarray to consolidation and consensus Nat Rev Genet 7 55-9

[9] Ritchie ME(1995)Controlling the false discovery rate: a practical and powerful approach to multiple testing J R Stat Soc B 57 289-8

[10] Phipson B(2006)Rat toxicogenomic study reveals analytical consistency across microarray platforms Nat Biotechnol 24 1162-17