Literature DB >> 15914576

Pooling samples within microarray studies: a comparative analysis of rat liver transcription response to prototypical toxicants.

Robert A Jolly1, Keith M Goldstein, Tao Wei, Hong Gao, Peining Chen, Shuguang Huang, Jean-Marie Colet, Timothy P Ryan, Craig E Thomas, Shawn T Estrem.   

Abstract

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. Because 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 vs. individual samples. Hepatic transcript profiles from a single-dose time-course rat study in response to the prototypical toxicants clofibrate, diethylhexylphthalate, and valproic acid were evaluated. Approximately 50% more transcript expression changes were observed in the individual (statistical) analysis compared with the pooled analysis. While the majority of these changes were less than twofold in magnitude ( approximately 80%), a substantial number were greater than twofold (approximately 20%). Transcript changes unique to the individual analysis were confirmed by quantitative RT-PCR, 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 mechanisms 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 mechanisms 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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Year:  2005        PMID: 15914576     DOI: 10.1152/physiolgenomics.00260.2004

Source DB:  PubMed          Journal:  Physiol Genomics        ISSN: 1094-8341            Impact factor:   3.107


  17 in total

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Authors:  Bhagya K Wijayawardena; J Andrew DeWoody; Dennis J Minchella
Journal:  Genetica       Date:  2015-02-14       Impact factor: 1.082

2.  RNA isolation method for single embryo transcriptome analysis in zebrafish.

Authors:  Mark de Jong; Han Rauwerda; Oskar Bruning; Jurgo Verkooijen; Herman P Spaink; Timo M Breit
Journal:  BMC Res Notes       Date:  2010-03-16

3.  Characterization of peroxisome proliferator-activated receptor alpha--independent effects of PPARalpha activators in the rodent liver: di-(2-ethylhexyl) phthalate also activates the constitutive-activated receptor.

Authors:  Hongzu Ren; Lauren M Aleksunes; Carmen Wood; Beena Vallanat; Michael H George; Curtis D Klaassen; J Christopher Corton
Journal:  Toxicol Sci       Date:  2009-10-22       Impact factor: 4.849

4.  Gene expression in human peripheral blood mononuclear cells upon acute ischemic stroke.

Authors:  C Grond-Ginsbach; M Hummel; T Wiest; S Horstmann; K Pfleger; M Hergenhahn; M Hollstein; U Mansmann; A J Grau; S Wagner
Journal:  J Neurol       Date:  2008-05-15       Impact factor: 4.849

Review 5.  The PPARα-dependent rodent liver tumor response is not relevant to humans: addressing misconceptions.

Authors:  J Christopher Corton; Jeffrey M Peters; James E Klaunig
Journal:  Arch Toxicol       Date:  2017-12-02       Impact factor: 5.153

6.  Regulation of Proteome Maintenance Gene Expression by Activators of Peroxisome Proliferator-Activated Receptor α.

Authors:  Hongzu Ren; Beena Vallanat; Holly M Brown-Borg; Richard Currie; J Christopher Corton
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7.  RNA isolation for transcriptomics of human and mouse small skin biopsies.

Authors:  Oskar Bruning; Wendy Rodenburg; Teodora Radonic; Aeilko H Zwinderman; Annemieke de Vries; Timo M Breit; Mark de Jong
Journal:  BMC Res Notes       Date:  2011-10-24

8.  Effects of pooling samples on the performance of classification algorithms: a comparative study.

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Journal:  ScientificWorldJournal       Date:  2012-04-30

9.  Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression.

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Journal:  BMC Bioinformatics       Date:  2006-08-25       Impact factor: 3.169

10.  Expression profiles of the pluripotency marker gene POU5F1 and validation of reference genes in rabbit oocytes and preimplantation stage embryos.

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Journal:  BMC Mol Biol       Date:  2008-07-28       Impact factor: 2.946

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