Microarray Data Analysis

1. Microarray Data Analysis The Bioinformatics side of the bench

2. The anatomy of your data files from MAS 5.0 (Microarray Suite 5.0) • .DAT • .CEL • .EXP • .CHP • .txt files generated from .CHP

3. Quality Control (QC) of the chip – visual inspection • Look at the .DAT file or the .CHP file image • Scratches? Spots? • Corners and outside border checkerboard appearance (B2 oligo) • Positive hybridization control • Used by software to place grid over image • Array name is written out in oligos!

6. Scratch on a chip

7. Possible chip contamination

8. Internal controls • B. subtilis genes (added poly-A tails) • Assessment of quality of sample preparation • Also as hybridization controls • Not used in our module

9. More internal controls • Eukaryotic Hybridization controls (bioB, bioC, bioD, cre) • E. coli and P1 bacteriophage biotin-labeled cRNAs • Spiked into the hybridization cocktail • Assess hybridization efficiency

10. And still more internal controls • Actin and GAPDH assess RNA sample/assay quality • Compare signal values from 3’ end to signal values from 5’ end • ratio generally should not exceed 3 • Percent genes present (%P) • Replicate samples - similar %P values

11. MAS 5.0 output files • For each transcript (gene) on the chip: • signal intensity • a “present” or “absent” call (presence call) • p-value (significance value) for making that call • Each gene associated with GenBank accession number (NCBI database)

12. How are transcripts determined to be present or absent? • Probe pair (PM vs. MM) intensities • generate a detection p-value • assign “Present”, “Absent”, or “Marginal” call for transcript • Every probe pair in a probe SET has a potential “vote” for presence call

13. Discrimination score • Probe pairs “vote” via discrimination score (R) • R compared to a predetermined threshold: Tau • R > Tau = present • R < Tau = absent • Voting result expressed as p-value • Reflects confidence of expression call

14. Altering Tau • You can fine tune Tau yourself within MAS 5.0 • Increase Tau: reduce “false positives”, may also reduce number of TRUE present calls • Our rule: use the default!

15. Calculation of R R = (PM - MM) / (PM + MM) • (PM – MM): • intensity difference of probe pair • (PM + MM): • overall hybridization intensity • R value closer to 1: lower p-value (detection call is more significant) • PM >> MM • R value close to 0 or negative: higher p-value (detection call is less significant) • MM >/= PM • One-sided Wilcoxon’s Signed Rank test used to determine Detection p-value

16. Calculating signal • One-Step Tukey Biweight Estimate • Yields robust weighted mean • Relatively insensitive to even extreme outliers • Signal intensity value is created • related to amount of transcript present for that gene

17. Thank goodness for software!!! • MAS 5.0 does these calculations for you • .CHP file • Basic analysis in MAS 5.0, but it won’t handle replicates • Import MAS 5.0 (.CHP) data into GeneSifter • web based microarray data analysis software package designed BY biologists FOR biologists

18. How do we want to analyze this data? • Pairwise analysis is most appropriate • Control vs. DMSO • List of genes that are “upregulated” or “downregulated” • Determine fold up or down cutoffs • What is significant? • 1.5 fold up/down? • 2 fold up/down? • 10 fold up/down?

19. Normalization • “Normalizing” data allows comparisons ACROSS different chips • Intensity of fluorescent markers might be different from one batch to the other • Normalization allows us to compare those chips without altering the interpretation of changes in GENE EXPRESSION

20. Statistics • Statistical tests allow us to determine how SIGNIFICANT the data are • t-test statistic • compares the means of two groups while taking into account the standard deviations of those means • p value (probability value) of </= 0.05 • (only 5 times out of 100 or less will the change in gene expression be due to chance, rather than a REAL change)

21. Present or absent? • Can do analysis on genes that are considered “absent” under all conditions • ONE transcript should be “present” in a pairwise analysis

22. Thresholds/cutoffs • What is a significant change in gene expression? • Some think 2 fold at the lowest • Judgement call • Can also set upper limit of expression changes • Remember we are talking about changes in mRNA expression • does that always mean more protein?

23. The output • Run analysis, get output of a GENE LIST • List indicates what genes are up or down regulated • p values for t-test • Graphs of signal levels • Absolute numbers not as important here as the trends you see • Now what????

24. Follow the links • Click on a gene • Find links to other databases • Follow links to discover what the protein does • Now the fun part begins….

25. Back to Biology • Do the changes you see in gene expression make sense BIOLOGICALLY? • If they don’t make sense, can you hypothesize as to why those genes might be changing? • Leads to many, many more experiments

26. Validation • Not enough to just do microarrays • Usually “validate” microarray results via some other technique • rt-PCR • TaqMan • Northern analysis • Protein level analysis • No technique is perfect…

27. Why microarrays? • Ask a single question, and get more answers than you dreamed of! • Can assess GLOBAL changes in gene expression under a certain experimental condition • Can discover new pathways, gene regulation, the possibilities are almost endless

28. Caveat… • There is NO standard way to analyze microarray data • Still figuring out how to get the “best” answers from microarray experiments • Best to combine knowledge of biology, statistics, and computers to get answers

29. One last note • Microarrays are “cutting edge” technology • You now have experience doing a technique that most Ph.D.s have never done • Looks great on a resume…