1 MicroArray -- Data Analysis Cecilia Hansen & Dirk Repsilber Bioinformatics - 10p, October 2001

2 Overview Monday –MA experimental basic (C) –MA data analysis (D) –Introduction to lab 1 (C) –lab 1 (C&D) Tuesday –Introduction to lab 2 (D) –lab 2 (D&C) Bio-Informatic motivation

3 Functional Genomics –adaptation and development –gene regulation & gene function –”Transcriptome” (diagnostical purposes) Analysis of large-scale noisy data –data management –statistical tools –associating further biological knowledge (database-links) Bio-Informatic motivation

4 Overview Monday –MA experimental basic –MA data analysis –Introduction to lab 1 –lab 1 Tuesday –Introduction to lab 2 –lab 2 Bio-Informatic motivation

5 Target (solid phase) 1)Genomic DNA or cDNA clones with known sequence. 2)PCR products are spotted on a glas slide in an organised way. 3)With a good printing device you can print at leased spots (genes) on one microscope glas slide. 4)This gives you a matrix of spots, where every spot represents only ONE gene. ***** experimental basics

6 t=0 t=2 t=1 t=3 t=7 Probe RNA is isolated from the cells mRNA is a very instable molecule! The amount of mRNA is in direct relation to the expression level of the gene. Make cDNA from mRNA with reverse transcriptase and incorporate fluorophores Cy3 and/or Cy5. experimental basics control sample...

7 Hybridization 2) The probe will bind to the complementary target to the array. 1) Mix equal amounts ( 1:1 ) of Cy3 and Cy5 samples and hybridize it on to the array. 3) A laser scanner is then used to make the fluorophores to emit light with different wavelength. This will give us a picture of the array with spots in green, yellow and red. green > red green = red green < red experimental basics

8 MA data analysis How do MA data look like ? What are the specific questions ? Methods

9 Overview Monday –MA experimental basic –MA data analysis –Introduction to lab 1 –lab 1 Tuesday –Introduction to lab 2 –lab 2 Bio-Informatic motivation

10 an expression profile like this for each gene : [mRNA] ~ Cy5/Cy3 = r time / h _ _ Start of experiment up-regulation induction down-regulation repression MA data analysis

11 Co-Regulation -- Inference of function: Genes belonging to the same pathway are often showing the same regulatory patterns (profiles) for a variety of biological situations (or in a time series). Hence, as a hypothesis, genes of unknown function showing similar regulatory behaviour as some genes of ”known” function may have a similar function. Which genes are differentially expressed ? Which genes are expressed in a ”similar” way when comparing to expression profiles of genes with known function? (co-regulation) patterns of expression (diagnostic ”Fingerprinting”) Reverse Engineering of genetic networks Differential expression: Comparing the Transcriptomes for two different biological samples (e.g. control, heat-shock) you are interested in the subset of genes which are expressed on different levels (up-/ down-regulated). Expression-Fingerprinting: Often in medical applications it is of interest to characterize the biological status of cells, e.g. the severeness of tumor cells, to be able to respond with the right therapy. Reverse Engineering: Using expression data to infer regulatory interactions between a number of genes responsible for a certain adaptation process or developmental process. MA data analysis

12 MA data analysis - methods

13 GeneSpring R - sma_package Excel MA data analysis - methods

14 MA data analysis Biological question Differentially expressed genes Classification etc. Testing Biological verification and interpretation Microarray experiment Description Experimental design Image analysis Normalization Clustering Discrimination lab 1 lab 2