2004
DOI: 10.1186/1471-2105-5-42
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Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays

Abstract: Background: To identify differentially expressed genes across experimental conditions in oligonucleotide microarray experiments, existing statistical methods commonly use a summary of probe-level expression data for each probe set and compare replicates of these values across conditions using a form of the t-test or rank sum test. Here we propose the use of a statistical method that takes advantage of the built-in redundancy architecture of high-density oligonucleotide arrays.

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Cited by 24 publications
(3 citation statements)
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“…GC-RMA was used for probe-level normalization of array intensities (126). Batch effects caused by multiple data sources were corrected using ComBat (127). Probes from each probe set with the greatest interquartile range were retained for gene expression analysis.…”
Section: Methodsmentioning
confidence: 99%
“…GC-RMA was used for probe-level normalization of array intensities (126). Batch effects caused by multiple data sources were corrected using ComBat (127). Probes from each probe set with the greatest interquartile range were retained for gene expression analysis.…”
Section: Methodsmentioning
confidence: 99%
“…heterogeneous variances are modeled) on the ranks of each sample using a family-wise false discovery rate of 5% [ 93 ]. These analyses are similar to the non-parametric Friedman and Mack-Skillings rank tests used for the analysis of microarray data [ 94 - 97 ]. This approach is more conservative than the pooled t -test analysis of rank data advocated by Conover [ 98 ] since the Welch t -test models the obvious heteroscedastic variability between the ranks of the drip flow biofilm transcriptome and the ranks of the comparator transcriptomes.…”
Section: Methodsmentioning
confidence: 99%
“…All gene expression values were subjected to a probe-level two-way ANOVA test to determine variability of expression across all samples [13] . A total of 4,326 genes were identified as differentially expressed with p ANOVA <0.05 and FC >2 ( Table S1 ) and were subjected to further OPI clustering analysis.…”
Section: Methodsmentioning
confidence: 99%