1. Gene Expression Data Analysis Lab Session CAD course Jian Li 01.28. 2011

2. Gene expression signatures Will be loosely defined here to mean a set of genes that are functionally associated with each other in some way. When using expression profiling to define genes, a gene expression signature consists of two things: –A set of genes going “up” (relative to something). –A set of genes going “down” (relative to something).

3. Gene expression profiling of IGF-I-stimulated MCF-7 cells

4. Five oncogenic pathway signatures in human cancers MYC Ras E2F3 b-cat Src

5. 5 signatures One combined signature (1) (2) (3,4) compare

6. Course webpage

7. Excel functions/features you will need for the computational exercise

8. TTEST array1 is the first data set. array2 is the second data set. tails specifies the # of distribution tails (Use “2”) type is the kind of t-Test to perform (Use “2”). TTEST(array1,array2,tails,type)

10. AVERAGE Number1, number2,... are 1 to 30 numeric arguments for which you want the average. The arguments must either be numbers or be names, arrays, or references that contain numbers. AVERAGE(number1, number2)

12. Data > Filter > AutoFilter arrows appear to the right of the column labels filtered items appear in blue. complex criteria: rows that contain values within a specific range (e.g. p<0.01)

19. MATCH lookup_value what value are you looking for? Lookup_array range of cells match_type should be 0 for our purposes. MATCH(lookup_value,lookup_array,match_type)

20. (Don’t forget the $)

21. COUNT Only numbers in a range are counted. Empty cells, logical values, text, or error values in the array or reference are ignored. COUNT(range) range cells to count

23. Compare two signatures Sig A: 1152 Sig B: 119 Genes on both platforms: 11079 Genes shared by both gene signatures: 44 one-sided Fisher's exact test

24. R function for one-sided Fisher's exact test dhyper Example: –100 balls –10 of the balls are red –I grab 20 balls –Five of my 20 balls are red Was the number of red balls I selected a significant number ? > m<-10 #number of red balls > n<-90 #number of other balls (total pop-m) > k<-20 #number of balls selected > x<-0:k #vector of successes > 1-sum(dhyper(x,m,n,k)[1:5]) [1] 0.02546455

25. R function for one-sided Fisher's exact test dhyper Sig A: 1162 Sig B: 119 Genes on both platforms: 11079 Genes shared by both gene signatures: 44 > m<-119 #number of Sig B genes > n<-11079-119 #number of other genes > k<-1162 #number of Sig A genes > x<-0:k #vector of successes > 1-sum(dhyper(x,m,n,k)[1:44]) [1] 1.265654e-14

26. Subramanian, Aravind et al. (2005) Proc. Natl. Acad. Sci. USA 102, 15545-15550 GSEA (rank-based) enrichment analysis All the genes in the dataset are used here Start from the top of the Ranked list. Add points to “Random walk” for each gene you find in S. Remove points from “Random walk” for each gene not in S.

27. GSEA (rank-based) enrichment analysis assign nominal P value

28. step 1 step 2 status/result

29. Subramanian, Aravind et al. (2005) Proc. Natl. Acad. Sci. USA 102, 15545-15550 GSEA (rank-based) enrichment analysis All the genes in the dataset are used here Start from the top of the Ranked list. Add points to “Random walk” for each gene you find in S. Remove points from “Random walk” for each gene not in S. (1) (2) (3)

30. Ranked-based enrichment analysis Rank-based approaches use all of the genes from one of the datasets to determine enrichment (does not make a “cut”). Rank ordered genes from dataset X Locations of genes from set Y