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Attachment 3. Statistical Analysis of Toxicogenomic Microarray Data
Pages 58-65

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From page 58...
... Moving from making a scientific conjecture of a compound's toxicity to analyzing gene expression profiles to concluding the association between the toxicity and gene alteration is a complex process. Statistical considerations are vital in each step of the process.
From page 59...
... Conventional hypothesis testing on such a massive amount of data leads to severe multiplicity issues. Proper multiplicity adjustments for P values will be discussed to help distill massive amounts of information into useful information for each compound.
From page 60...
... We will provide some examples to show how different scientific questions lead to different experimental designs and statistical hypotheses. Microarray measures the expression abundance of essentially all the genes in a genome.
From page 61...
... Statistical Analysis of Toxicogenomic Microarray Data 61 different days, especially if it involves a change of reagents. It has been consistently shown that samples, even without any treatment, are different if the samples are collected at different times.
From page 62...
... In general, when the test is applied to each gene, the statistical approach is not different from what has been used in classic biologic research. However, after the initial statistical test, since it is "fishing" significant gene expression changes out of thousands of genes, adjustments should be made to the resulting P values to control the false positive rate.
From page 63...
... . When there are a large number of genes that are significant, after clustering, we may rescale the gene expression data within each gene and use a heat map to show the expression patterns.
From page 64...
... They are more interested in how many genes identified in a microarray experiment will fail to be confirmed in the follow-up studies, i.e., the false-positive rate among the "discoveries." This provides a perfect setting to apply FDR-controlling methods. When addressing the multiplicity issue and deciding what gene expression changes are significant using FDR or other approaches, the researchers assume that all gene expressions are independent and equally
From page 65...
... 1995. Controlling the false discovery rate: A practical and powerful approach to multiple testing.


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