Optimization of an Animal Test Protocol for Toxicogenomics Studies (I); Requirement Study of a Protocol

Sumida, Kayo; Saito, Koichi; Oeda, Kenji; Otsuka, Masanori; Tsujimura, Kazunari; Miyaura, Hideki; Sekijima, Masaru; Nakayama, Koji; Kawano, Yukiko; Kawakami, Yuki; Asamoto, Makoto; Shirai, Tomoyuki

doi:10.2131/jts.32.19

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…It may be noted that use of ANOVA causes the error of the second kind. Because of this, some of the recent studies skipped ANOVA in the decision tree and straight away used the statistical tools for post hoc comparison (Sumida et al, 2006;Nagano et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of statistical tools used in short-term repeated dose administration toxicity studies with rodents

et al. 2008

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-In order to know the different statistical tools used to analyze the data obtained from twenty-eight-day repeated dose oral toxicity studies with rodents and the impact of these statistical tools on interpretation of data obtained from the studies, study reports of 122 numbers of twenty-eight-day repeated dose oral toxicity studies conducted in rats were examined. It was found that both complex and easy routes of decision trees were followed for the analysis of the quantitative data. These tools include Scheffe's test, non-parametric type Dunnett's and Scheffe's tests with very low power. Few studies used the non-parametric Dunnett type test and Mann-Whitney's U test. Though Chi-square and Fisher's tests are widely used for analysis of qualitative data, their sensitivity to detect a treatment-related effect is questionable. Mann-Whitney's U test has better sensitivity to analyze qualitative data than the chi-square and Fisher's tests. We propose Dunnett's test for analysis of quantitative data obtained from twenty-eight-day repeated dose oral toxicity tests and for qualitative data, Mann-Whitney's U test. For both tests, one-sided test with p=0.05 may be applied.

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Section: Discussionmentioning

confidence: 99%

Evaluation of statistical tools used in short-term repeated dose administration toxicity studies with rodents

et al. 2008

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“…Male Fischer 344 rats (SPF, 5 weeks of age) were administered with 300 mg/kg/day of Safrol for up to 28 days at the Mitsubishi Chemical Safety Institute [11], [12]. RNA samples were isolated from the liver of each test animal.…”

Section: Methodsmentioning

confidence: 99%

“…To test the coincidence, a series of data were obtained from a rat toxicology project study [10]. Multi-sample RNA isolated from rat organs were then hybridized to two platforms: an in-house microarray (ToxArray III), and the GeneChip microarray in three different laboratories [11], [12]. The ToxArray III is a typical microarray on slide glass, consisting of a single 60 mer probe per gene, and two samples are measured simultaneously per chip.…”

Section: Introductionmentioning

confidence: 99%

Coincidence between Transcriptome Analyses on Different Microarray Platforms Using a Parametric Framework

Konishi

Takasaki

et al. 2008

PLoS ONE

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A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Microarrays are widely employed to acquire transcriptome information, and several platforms of chips are currently in use. However, discrepancies among studies are frequently reported, particularly among those performed using different platforms, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks are based on different philosophies and yield different results, but all involve normalization against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using several slide-glass-type chips and GeneChip. The model is based on a common statistical characteristic of microarray data, and each set of chip data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other frameworks.

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“…Male Fischer 377 rats (SPF, 5 weeks of age) were administered with 300 mg/kg/day of Safrol for up to 28 days at the Mitsubishi Chemical Safety Institute 14,15 . RNA samples were isolated from the liver of each test animal.…”

Section: Test Animalsmentioning

confidence: 99%

“…The data were obtained from a rat toxicology project study 13 in which rats were administered with various chemicals. Multi-sample RNA isolated from rat organs was then hybridized to two platforms: an in-house microarray (ToxArray III), and the GeneChip microarray in three different laboratories 14,15 . The ToxArray III is a typical microarray on slide glass, consisting of a single 60 mer probe per gene, and two samples are measured simultaneously per chip.…”

Section: Introductionmentioning

confidence: 99%

Coincidence between transcriptome analyses on different microarray platforms using a parametric framework

Konishi

Takasaki

et al. 2008

Nat Prec

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A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Discrepancies among transcriptome studies are frequently reported, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks normalizes data against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using an in-house printed chip and GeneChip. The framework is based on a common statistical characteristic of microarray data, and each data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other methods.

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Optimization of an Animal Test Protocol for Toxicogenomics Studies (I); Requirement Study of a Protocol

Cited by 4 publications

References 14 publications

Evaluation of statistical tools used in short-term repeated dose administration toxicity studies with rodents

Evaluation of statistical tools used in short-term repeated dose administration toxicity studies with rodents

Coincidence between Transcriptome Analyses on Different Microarray Platforms Using a Parametric Framework

Coincidence between transcriptome analyses on different microarray platforms using a parametric framework

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