Gene Expression Data Analysis Lab Session

CAD course

Jian Li

01.28. 2011

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).

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

Five oncogenic pathway signatures in human cancers

MYC

Ras

E2F3

b-cat

Src

5 signatures

One combined signature(1)

(2)

(3,4)compare

• Course webpage

Excel functions/features you will need for the computational exercise

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)

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)

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)

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)

(Don’t forget the $)

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

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

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

R function for one-sided Fisher's exact test dhyper

Sig A: 1162 Sig B: 119

Genes on both platforms: 11079Genes 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

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.

GSEA (rank-based) enrichment analysis

assign nominal P value

step 1

step 2

status/result

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)

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