Significance analysis of Microarrays (SAM).ppt

Significance analysis of Microarrays (SAM) Applied to the ionizing radiation response Outline Problem at hand Reminder: t-Test, multiple hypothesis testing SAM in details Test SAM’s validity Other methods- comparison Variants of SAM Outline Problem at hand Reminder: t-Test, multiple hypothesis testing SAM in details Test SAM’s validity Other methods- comparison Variants of SAM The Problem: Identifying differentially expressed genes Determine which changes are significant Enormous number of genes Reminder: t-Test t-Test for a single gene: We want to know if the expression level changed from condition A to condition B. Null assumption: no change Sample the expression level of the genes in two conditions, A and B. Calculate H0: The groups are not different, t-Test Cont’d Under H0, and under the assumption that the data is normally distributed, Use the distribution table to determine the significance of your results. Multiple Hypothesis Testing Na?ve solution: do t-test for each gene. Multiplicity Problem: The probability of error increases. We’ve seen ways to deal with it, that try to control the FWER or the FDR. Today: SAM (estimates FDR) Outline Problem at hand Reminder: t-Test, multiple hypothesis testing SAM in details Test SAM’s validity Other methods- comparison Variants of SAM SAM- procedure overview SAM- procedure overview The Experiment Scaling Scale the data. Use technique known as “linear normalization” Twist- use cube root First glance at the data How to find the significant changes? Na?ve method SAM- procedure overview SAM’s statistic- Relative Difference Define a statistic, based on the ratio of change in gene expression to standard deviation in the data for this gene. At low expression levels, variance in d(i) can be high, due to small values of s(i). To compare d(i) across all genes, the distribution of d(i) should be independent of the level of gene expression and of s(i). Choose s0 to make the coefficient of variation of d(i) approximately c