Rafael A. Irizarry Department of Biostatistics, JHU rafa@jhu

1. Use of Mixture Model in a genome-wide DNA microarray-based genetic screen for components of the NHEJ Pathway in Yeast Rafael A. Irizarry Department of Biostatistics, JHU rafa@jhu.edu

3. Damaged DNA Yku70p/Yku80p (DNA-PK ) DNA end binding Nucleolytic processing Rad50p/Mre11p/Xrs2p Ligation Lig4p/Lif1p Repaired DNA

4. CEN/ARS aatt ttaa URA3 NHEJ Defective A DOWNTAG kanR UPTAG CEN/ARS B URA3 MCS Circular pRS416 EcoRI linearized PRS416 Transformation into deletion pool Select for Ura+ transformants Genomic DNA preparation PCR Cy5 labeled PCR products Cy3 labeled PCR products Oligonucleotide array hybridization

5. 5718 mutants 3 replicates on each slide 5 Haploid slides, 4 Diploid slides Haploids are divided into 2 downtags, 3 uptag (2 of which replicate uptags) Diploids are divided into 3 uptags (2 of which are replicates) and 2 uptags Data

6. Which mutants are NHEJ defective? • Find mutants defective for transformation with linear DNA • Dead in linear transformation (green) • Alive in circular transformation (red) • Look for spots with large log(R/G)

12. Improvement to usual approach • Take into account that some mutants are dead and some alive • Use a statistical model to represent this • Mixture model? • With ratio’s we lose information about of R and G separately • Look at them separately (absolute analysis)

18. Warning • Absolute analyses can be dangerous for competitive hybridization slides • We must be careful about “spot effect” • Big R or G may only mean the spot they where on had large amounts of cDNA • Look at some facts that make us feel safer

19. R1 R2 R3 G1 G2 G3 R1 1.00 0.95 0.95 0.94 0.90 0.90 R2 0.95 1.00 0.96 0.90 0.95 0.91 R3 0.95 0.96 1.00 0.91 0.92 0.95 G1 0.94 0.90 0.91 1.00 0.96 0.96 G2 0.90 0.95 0.92 0.96 1.00 0.97 G3 0.90 0.91 0.95 0.96 0.97 1.00 Correlation between replicates

20. Correlation between red, green, haploid, diplod, uptag, downtag RHD RHU RDD RDU GHD GHU GDD GDU RHD 1.00 0.59 0.56 0.32 0.95 0.58 0.54 0.37 RHU 0.59 1.00 0.38 0.56 0.58 0.95 0.40 0.58 RDD 0.56 0.38 1.00 0.58 0.54 0.39 0.92 0.64 RDU 0.32 0.56 0.58 1.00 0.33 0.53 0.58 0.89 GHD 0.95 0.58 0.54 0.33 1.00 0.62 0.56 0.39 GHU 0.58 0.95 0.39 0.53 0.62 1.00 0.41 0.58 GDD 0.54 0.40 0.92 0.58 0.56 0.41 1.00 0.73 GDU 0.37 0.58 0.64 0.89 0.39 0.58 0.73 1.00

21. The mean squared error across slides is about 3 times bigger than the mean squared error within slides BTW

22. We use a mixture model that assumes: There are three classes: Dead Marginal Alive Normally distributed with same correlation structure from gene to gene Mixture Model

23. Each x = (r1,…,r5,g1,…,g5) will have the following effects: Individual effect: same mutant same expression (replicates are alike) Genetic effect: same genetics same expression PCR effect : expect difference in uptag, downtag Random effect justification

24. Does it fit?

25. Does it fit?

26. Define a t-test that takes into account if mutants are dead or not when computing variance For each gene compute likelihood ratios comparing two hypothesis: alive/dead vs.dead/dead or alive/alive What can we do now that we couldn’t do before?

27. QQ-plot for new t-test

28. Better looking than others

32. 1 YMR106C 9.5 47 69.2 a a 100 2 YOR005C 19.7 35 44.9 a d 100 3 YLR265C 6.1 32 35.8 a m 100 4 YDL041W 10.4 32 35.6 a m 100 5 YIL012W 12.2 31 21.7 a a 100 6 YIL093C 4.8 29 30.8 a a 100 7 YIL009W 5.6 29 -23.5 a a 100 8 YDL042C 12.9 29 32.1 a d 100 9 YIL154C 1.8 28 91.3 m m 82 10 YNL149C 1.7 27 93.4 m d 71 11 YBR085W 2.5 26 -15.8 a a 84 12 YBR234C 1.7 26 87.5 m d 75 13 YLR442C 6.1 26 -100.0 a a 100

33. Siew Loon Ooi Jef Boeke Forrest Spencer Jean Yang Acknowledgements