1. Spanish Inquisition Final Project Week 4 - 5/21/09 Breast Cancer Gene Expression Data Leon Kay, Yan Tran, Chris Thomas Chris Yan Leon

2. Cluster Analysis - SAM Refined Clusters Using TMEV’s SAM Statistical Analysis Significance Analysis of Microarrays –determining whether changes in gene expression are statistically significant. –identifies statistically significant genes by measuring the strength of the relationship between gene expression and a response variable

3. MeV SAM Analysis - Results Creation of SAM file – Used Excel 2007 to manually create the SAM load file. SAM reduces number of genes to 265 significant genes, and 1279 non- significant genes (1544 total genes). SAM analysis reduces the number of genes to 17% of the original total.

4. MeV SAM Analysis – Significant Genes Graph

5. MeV SAM Analysis – Non- significant Genes Graph

6. Kaplan-Meier Survival Analysis Used to estimate the overall likelihood of survival, given a set of lifetime data Generated using the Excel Plug-in –www.xlstat.comwww.xlstat.com –Thanks Sri! A plot of the Kaplan-Meier estimate of the survival function is a series of horizontal steps of declining magnitude which, when a large enough sample is taken, approaches the true survival function for that population.

7. Survival Analysis – Breast Cancer Type

8. Survival Analysis - Overall

9. Relapse Probability 30 out of 270 patients relapsed. Only 270 patients in the clinical data has information recorded one way or the other for relapsing. This gives a relapse rate of.1111, or 11.11% Calculating a 99% confidence interval, we get +/- 0.049. The final probability of relapse, with 99% certainty, is.1111 +/- 0.049. Or, 11.11% +/- 4.9%, for a min and max range of (6.21%, 16.01%)

10. Relevance Networks The MeV manual states that a “relevance network is a group of genes whose expression profiles are highly predictive of one another.” Clusters are represented as genes connected together by lines showing that they are related to each other by a correlation coefficient R 2 within preset thresholds.

11. Relevance Networks The breast cancer data yielded 14 relevance networks.

12. GATA3 In week two we mentioned the GATA3 gene Linked to the estrogen receptor alpha. Method for providing prognosis because the expression profile is very different between Basal-like and Luminal. Will GATA3 show-up as a significant gene after post SAM analysis and will we find the gene associated with estrogen receptor alpha with it?

13. Relevance Networks GATA3 and ESR1 are in network 2.

14. References 1) Edward L. Kaplan, “This Week’s Citation Classic”, Current Contents June 1983 http://www.garfield.library.upenn.edu/classics1983/A1983QS51100001.pdf