Rat toxicogenomic study reveals analytical consistency across microarray platforms

Abstract

To validate and extend the findings of the MicroArray Quality Control (MAQC) project, a biologically relevant toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine and comfrey) and each sample was hybridized to four microarray platforms. The MAQC project assessed concordance in intersite and cross-platform comparisons and the impact of gene selection methods on the reproducibility of profiling data in terms of differentially expressed genes using distinct reference RNA samples. The real-world toxicogenomic data set reported here showed high concordance in intersite and cross-platform comparisons. Further, gene lists generated by fold-change ranking were more reproducible than those obtained by t-test P value or Significance Analysis of Microarrays. Finally, gene lists generated by fold-change ranking with a nonstringent P-value cutoff showed increased consistency in Gene Ontology terms and pathways, and hence the biological impact of chemical exposure could be reliably deduced from all platforms analyzed.

Isolation and Molecular Characterization of Cancer Stem Cells in MMTV-Wnt-1 Murine Breast Tumors

“…Defining the Mouse Signature. Differentially expressed (DE) genes were obtained by using fold change rank ordering with a nonstringent p value cut-off [23,24]. To generate our DE gene list, we selected genes that had a normalized intensity of Ն100 on at least one array, that varied by more than twofold between the TG and NTG samples, and that had nominal paired t test p values of Յ.05.…”

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“…As recently demonstrated by us [6], even when technical measurement variations are small, DEG lists correctly determined by a statistical method such as SAM (Significance Analysis of Microarrays) [1] from different microarray studies for a cancer still tend to be highly inconsistent. Under such situations, selecting DEGs solely based on statistical cut-off criteria (e.g., a false discovery rate (FDR) level) becomes less relevant [7][8][9]. However, functional modules significantly enriched with DEGs tend to be robust to the uncertainty introduced by DEG selection [10,11], as also formally demonstrated by us recently [12].…”

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