This document summarizes statistical methods for analyzing cDNA microarray data, including data preprocessing, normalization techniques, and statistical tests. It discusses alignment, background calculation, data transformation, normalization methods like global normalization, housekeeping gene normalization, and intensity-dependent normalization. Statistical tests covered include t-tests, multiple testing adjustments, permutation tests, and significance analysis of microarrays (SAM). The document concludes that no single method is best and different data may require trying different analytical approaches.

1. Statistical Analysis of cDNA Microarray Genomics Data Yuehua Cui Graduate student Department of Statistics December 4th, 2002

7. Use Phosphor Imager laser scanner to obtain densities of each spot on filter. Control RNA Sample Test RNA Sample Hybridization to microarray filters radio-labelled cDNA probes Reverse-Transcription 33 P - dCTP 33 P - dCTP Compare densities at each spot to determine if treatment changes gene expression. Compile subset of differentially expressed genes. Gene Control Test A 1X 3X : : : Z 1X 0.5X

8. Scatter plots of adjusted log intensities for paired experiments of D MSO vs BaP

12. Scatter plot of log intensity before and after regression normalization

15. M-A plot of DMSO vs BaP (Before and after intensity-dependent normalization, f=0.3)

24. Significant genes list of BaP/DMSO

27. List of significant genes picked up by permutation test

33. SAM analysis