RESUMEN
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.
Asunto(s)
Perfilación de la Expresión Génica/métodos , Análisis de Secuencia por Matrices de Oligonucleótidos/instrumentación , Garantía de la Calidad de Atención de Salud/métodos , Toxicogenética/métodos , Animales , Perfilación de la Expresión Génica/normas , Análisis de Secuencia por Matrices de Oligonucleótidos/métodos , Control de Calidad , Ratas , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
Gene expression profiling, metabolomic screens, and other high-dimensional methods have become an integral part of many biological investigations. To facilitate interpretation of these data, it is important to have detailed phenotypic data--including histopathology--to which these data can be associated, or anchored. However, as the amount of phenotypic data increases, associations within and across these data can be difficult to visualize and interpret. We have developed an approach for categorizing and clustering biologically related histopathological diagnoses to facilitate their visualization, thereby increasing the possibility of identifying associations and facilitating the comparison with other data streams. In this study, we utilize histopathological data generated as part of a standardized toxicogenomics compendium study to generate composite histopathological scores and to develop visualizations that facilitate biological insight. The validity of this approach is illustrated by the identification of transcripts that correlate with the pathology diagnoses that comprise the categories of "response to hepatocellular injury" and "repair." This approach is broadly applicable to studies in which histopathology is used to phenotypically anchor other data, and results in visualizations that facilitate biological interpretation and the identification of associations and relationships within the data.