1. MCGH Analyzer Hans A. Kestler André Müller MCGH Analyzer

2. Data processing steps • Scanning of the DNA chips (normal and switched) • 2 Channels (Cy 5 and Cy 3) • Build mean/median over the pixels • Further processing with MCGH Software MCGH Analyzer

3. MCGH software • Background reductioncalculate intensities according to the background • Quality control of the spotsreject spots not fitting the quality criteria • Accumulate spots to clones • Check testreject clones not fitting the visual options • Select control clones • Reduce control clones • Main calculation loop MCGH Analyzer

4. Overview MCGH Analyzer

5. Background reduction Background reduction to get intensities • No reduction • Fixed reduction • Local reduction • Global reduction • Local + Fixed reduction • Global + Fixed reduction Compute log Ratios • log( IntCy3 / IntCy5 ) • log( IntCy5 / IntCy3 ) MCGH Analyzer

6. Quality control Reject spots with • flags marked by the scanning software(bad, not found, absent, normal ...) • A background intensity brighter than the foreground (new!) • Min/Max reduction: • Reject the n smallest ratios • Reject the n largest ratios MCGH Analyzer

7. Spots to clones Accumulate the non-rejected spot values • Mean • Standard deviation • Median over • Intensities (Cy3, Cy5) • log Ratios New Feature:Reject clones with less than SpotLowerBound valid spots. MCGH Analyzer

8. Check test Reject clones if at least one of these conditions holds: • Me(di)an background intensity > Background upper bound • Me(di)an Cy3 Intensity < Me(di)an Cy3 background intensity x Intensity lower bound • Standard deviation Cy3 Ratio > Ratio SD upper bound MCGH Analyzer

9. Select control clones Only non-rejected clones will be selected as control clones. • Manual selectionSelect clones with id = ‚91‘ or ‚k‘ or ‚K‘ or ‚?91‘ as control clone • Automatic selection • No [AutoBand][CutoffPercentage] clones from the middle band • [AutoBand]Select band around the median MCGH Analyzer

10. Reduce control clones Some of the control clones will be rejected ... • [Cutoff Percentage]Reject the n smallest ratios • Without [Cutoff Band]Reject the n largest ratios • [Cutoff Band]Reject band around the median MCGH Analyzer

11. Main calculation loop • Calculate control means (the mean/median over all control clones/spots) • Normalize ratios (subtract control mean from the ratio) • Calculate tolerance value Ts standard deviation of the ratios of the observed clonenthe number of valid spots in this clonet value of the t-statistic significance niveau • [ Force T-Test ]Reject clones with T > [ Force T Value ] • [ C Check ]Replace tolerance values with possible greater values. • Find clone with maximum tolerance and reject it if its tolerance value T is > [ Force T Value ] • Perform [ T Test ] and evaluate result value. Everything has to be recalculated if a control clone will be rejected. MCGH Analyzer

12. The C Check The clone tolerance values are now recalculated according to the following scheme: If the new tolerance value is greater than the old T will be replaced by the new value MCGH Analyzer

13. The T Test If [ Force T ] is set, the value will be set to the [ Force T Value ] otherwise it is the greates tolerance value found in the clones. MCGH Analyzer

14. The T Test (2) Calculation of the result value R • [ T Test ] • No [ T Test ] : thresholding In this routine the test T > [ Force T Value ] will be performed repeatedly MCGH Analyzer

15. NCBI Clone Database • Integration of the NCBI “component” database • Automatically mapping of clone id’s to accession numbers, genomic clone locations and clone status information according to an up-to-date database • Direct import of the NCBI file format MCGH Analyzer

16. Database-generated Information Accession-Number Start-Base End-Base Clone-State MCGH Analyzer

17. Batch Processing • One ore more file pairs can be added to a session • All computations are performed simultaneous on the included datasets MCGH Analyzer

18. Diagrams functions • Ratio-profiles of multiple clone sets can be shown in one diagram MCGH Analyzer

19. Ideogram Browser 1 • Independent portable Java application • Automation from MCGH-Analyzer with JNI • Generation of ideogram drawings from the NCBI map database • Direct representation of gain and lost markers of multiple clone sets • Scalable and scrollable graphs MCGH Analyzer

20. Ideogram Browser 2 MCGH Analyzer

21. Software Structure 1 • Excel as convenient platform with widely known user interface for • Table representation • Diagram drawing • User interaction • Windows DLL written in C++ for high performance using COM automation • Platform-independent Java-Application for visualizing ideograms (can be docked to the DLL via JNI) MCGH Analyzer

22. Software Structure 2 MCGH Analyzer

23. Future Features • Copy number estimation • Global thresholds • Adaptive (local) thresholds • Wavelets • Adaptive weights smoothing • NCBI database online update • Interface to the R platform MCGH Analyzer