Physiol. Genomics AJP: Renal Physiology
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Physiol. Genomics (May 24, 2005). doi:10.1152/physiolgenomics.00260.2004
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Submitted on November 3, 2004
Accepted on May 13, 2005

Pooling Samples Within Microarray Studies: A Comparative Analysis of Rat Liver Transcription Response to Prototypical Toxicants

Robert A Jolly1, Keith M Goldstein1, Tao Wei1, Hong Gao1, Peining Chen2, Shuguang Huang2, Jean-Marie Colet3, Timothy P Ryan1, Craig E Thomas1, and Shawn T Estrem1*

1 Lilly Research Laboratories, A Division of Eli Lilly and Company, Greenfield, IN 46140, USA
2 Lilly Research Laboratories, A Division of Eli Lilly and Company, Indianapolis, IN 46285, USA
3 Lilly Research Laboratories, Mont-Saint-Guibert, Belgium

* To whom correspondence should be addressed. E-mail: stestrem{at}lilly.com.

Combining or pooling individual samples when carrying out transcript profiling using microarrays is a fairly common means to reduce both the cost and complexity of data analysis. However, pooling does not allow for statistical comparison of changes between samples and can result in a loss of information. Since a rigorous comparison of the identified expression changes from the two approaches has not been reported, we compared the results for hepatic transcript profiles from pooled versus individual samples. Hepatic transcript profiles from a single dose time course rat study in response to the prototypical toxicants, clofibrate (CLO), diethylhexylphthalate (DEHP) and valproic acid (VPA) were evaluated. Approximately 50% more transcript expression changes were observed in the individual (statistical) analysis as compared to the pooled analysis. While the majority of these changes were less than 2-fold in magnitude (~80%), a substantial number were greater than 2-fold (~20%). Transcript changes unique to the individual analysis were confirmed by QRTPCR while all the changes unique to the pooled analysis did not confirm. The individual analysis identified more hits per biological pathway than the pooled approach. Many of the transcripts identified by the individual analysis were novel findings and may contribute to a better understanding of molecular mechanism of these compounds. Furthermore, having individual animal data provided the opportunity to correlate changes in transcript expression to phenotypes (i.e. histology) observed in toxicology studies. The two approaches were similar when clustering methods were used despite the large difference in the absolute number of transcripts changed. In summary, pooling reduced resource requirements substantially but the individual approach enabled statistical analysis that identified more gene expression changes to evaluate mechanism of toxicity. An individual animal approach becomes more valuable when the overall expression response is subtle and/or when associating expression data to variable phenotypic responses.




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