Microarray Analysis

1. Microarray Analysis Jesse Mecham CS 601R

2. Microarray Analysis • It all comes down to • Experimental Design • Preprocessing • Data Analysis

3. Experimental Design • Elimination of confounding factors • Same cell line, minimal exposure • Timing of sampling • Technological considerations • Hybridization considerations • Chip/tag selection

5. Slide to Data Gene Value D26528_at 193 D26561_cds1_at -70 D26561_cds2_at 144 D26561_cds3_at 33 D26579_at 318 D26598_at 1764 D26599_at 1537 D26600_at 1204 D28114_at 707

6. Preprocessing • Data import • Background adjustment • Normalization • Summarization of multiple probes per transcript • Quality control

7. Data Import • Incorporate various file formats into desired data formats • Different vendors have different representations • Sometimes desired data is not provided

8. Background Adjustment • It all comes down to one word…noise • Optical distortion • Non-specific hybridization • Equipment damage

9. M vs. A • M represents differential ratio M = (log R – log G) • A represents the fluorescence intensity A = (log R + log G)/2 • Desirable transformation would show uniform distribution of differential across intensities

11. Normalization • Normalization between samples needs to be established for a variety of reasons • Different reverse transcription efficiency levels • We are using PCR to amplify in separate plates • Hybridization inequalities • Variations in solution used in hybridization reaction • Spatial abnormalities between plates • Particularly apparent for in-house plates

12. Background Example

13. Possible Problem in Background?

14. Summarizing Data • Process of reducing the various samples into an analysis • The crux of microarray analysis • Can apply a linear or a non linear model using any of the following techniques • Support Vector Machines (SVM) • Neural Networks • Empirical Bayes

15. Quality Control • Concerned with accuracy and reproducibility • Dr. Piatetsy-Shapiro (last week’s colloquium) was primarily concerned with this area of microarray analysis • Detection of errors (x-validation) • Isolation and validation of significant results • Corrective behavior

16. Time for Fun • Dataset • ApoAI.RData • The apolipoprotein AI (ApoAI) gene is known to play a pivotal role in high density lipoprotein (HDL) metabolism. Mice which have the ApoAI gene knocked (KO) out have very low HDL cholesterol levels. • Puprose is to determine how ApoAI deficiency affects the action of other genes in the liver • Help determine what molecular pathways ApoAI operates on

17. Markers • All mRNA data from both knockout and wild-type were marked GREEN • KO and WT are marked RED • Oftentimes, both populations are run on same plate with one being marked RED and the other marked GREEN

18. Rwww.r-project.org • “S”-like GNU project language and environment for statistical computing • Great free package for linear and non-linear statistical modeling • Also includes: • an effective data handling and storage facility, • a suite of operators for calculations on arrays, in particular matrices, • a large, coherent, integrated collection of intermediate tools for data analysis, • graphical facilities for data analysis and display either on-screen or on hardcopy, and • a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

19. Bioconductorhttp://bioconductor.org • Open source package for statistical analysis of genomic data • Includes both statistical and graphical tools • Active project with a constant influx of new packages • Does not include more complex analysis tools at this time (SVM’s, etc.)

20. With Controls

21. Controls Removed