1. Felix Naef & Marcelo Magnasco, GL meeting, Nov. 19 2001 felix@funes.rockefeller.edu Outline Background subtraction Probeset statistics Excursions into GeneChip data analysis

2. Background estimation estimate both mean B and fluctuations  needed in low-intensity regime includes light reflection from substrate, photodetector dark current, some cross- hybridization (i.e. small residues) by the CLT, background is expected to be a Gaussian variable

3. idea: B is insensitive to MM and visible at low intensity select probes such that |PM-MM| <  (locally?) use  =50 (new) or 100 (old settings) P(PM) or P(MM) is convolution of Gaussian and step function “+” = 0 B  B Real P(PM)

4. example:  ) dependence on 

5. trick for dealing with negative values

6. PM vs. MM distribution MM>PM make a histogram in this region make a histogram in this region zoom

7. PM vs. MM histogram

8. MM>PM across different chips MM>PM not concentrated at low intensities: 27% of probe pairs with MM>PM are in the top quartile

9. probe pairs trajectories (~80 chips) take all (PM, MM) for a given probe set center of mass (x,y) ellipsoid of inertia >    and   histogram the cm’s color code acc. to s =   /  (min(x, y  ~ noise detrending

10. all probe sets blue : large s green : mid red : small

11. probes with ‘well’ defined trajectories (eccentricity > 3) ~1/3 of probes blue : large green : mid red : small

12. PM within a probe set Are the brightness of the probes reasonably uniform? Or do different probes have very different hybridization efficiencies?

13. So what can possibly be happening? sequence dependent hybridization efficiencies are kinetic effects important? cross-hybridization beyond what is detectable by MM probes this is hard to assess without sequence info sequence dependent fabrication efficiencies? variable probe densities

14. Composite scores What have we learned from previous slides? MM are not consistently behaving as expected -What about not using them ? The probe set intensities vary over decades -difficult to estimate absolute intensities using ‘averages’ (alternative: Li and Wong) - we focus on ratio scores

15. Outline of algorithm 1.estimate background (mean and std) 2.discard noisy and saturated probes use either only PM or PM-MM as raw intensities 3.average the remaining log-ratios in an outlier robust way (robust regression to intercept), SE 4.normalize by centering (event. local) log- ratio distribution