Simple Methods for Peak Detection in Times Series Microarray Data

1. Simple Methods for Peak Detection in Times Series Microarray Data I. Azzini R. Dell’Anna F. Ciocchetta F. Demichelis A. Sboner Bioinformatics Group, SRA, ITC-Irst Department of Information and C.T.Trento University, Italy E. Blanzieri A. Malossini Department of Information and Communication Technology Trento University

2. Preliminary Analysis • Visual inspection of images • There are blurs on the images • We used alternative sw for image analysis • TIGR SpotFinder • Scananalyse • We reapplied the GenePixPro 3.0 quality control algorithm on a sample of images • Conclusions • From preliminary analysis did not emerged evidence againts reliability of original measures. • use QC_Dataset for further analysis

3. Our analysis problem • To detect and characterize genes that present peaks and singularities over the time series. • Motivations: • Primary: Peak genes could play an intriguing role • Secondary: artifacts detection

4. Our approach • Detection of spike genes • Apply a series of simple methods based on discrete derivative and integral. • Characterization of genes • Functional Classification using Multiclass SVM

5. Outline of the talk • Preliminary analysis • The analysis problem • Methods for peak detection • M-SVM for oligo classification • Results • Discussion

6. QC_Dataset Our notation: X0,t=E(o,t)

7. Missing value managment(data imputing) • Up to 2 adjacent missing values were replaced by interpolation • Oligos with more adjacent missing values were discarded • Extrapolation for TP1 and TP48 (For functional classification)

8. Methods for peak detection None of the methods is 100% precise and 100% accurate

9. The derivative method M1

10. Methods for peak detection The combination of M1, M2 and M3 are less prone to detect ramps Instead of peaks

11. The integral method M4

12. Methods for peak detection

13. Detection procedure • Each method M1-M6 scores the oligos. • We selected the oligos that were ranked among the first ten by at least one method

15. Detection procedure • We discarded oligos whose signal to noise ratio is less of 2 • The S/N ratio is higher w.r.t. the one adopted in original work • We need such a filter to discard extremely noisy signals • We visual inspect all the oligos of the table and discarded the ones that does not present peaks

16. opfblob0072 n128_25 f65819_1 m364_2 m12963_1 n159_34 ks244_7 n128_61 opfm60504 l1_28 ET ks75_15 ET c154 b593 b597 n176_5 opfh0008 opfblob0105 b541 n132_108 m50253_2 ks1030_4 OM n128_33 f71224_1 opfh0022 e17542_1 Detection procedure:Selected genes

17. Functional Classification (M-SVM) • Multiclass Support Vector Machine • Pairwise classification (N-1)*N/2 classifiers for N classes. • Majority vote • Schema for replacement of missing values • Trained on data of Table S2 • 530 samples and 14 functional classes • LOO accuracy is 73% • We applied the classifiers to the complete_dataset and scored the results depending on the voting.

18. Results Table

19. Results Table

20. Results Table

21. The single oligonucleotide MAL7P1.88-f71224_1.

22. The single oligonucleotide PFL0035c-l1_28.

23. The multiple oligonucleotides PFB0935w (b593, b597).

24. The single oligonucleotide f65819_1.

25. Significant peaks or artifacts? • We tested: • Data Quality (from preliminary analysis) • We discarded oligos with low signal to noise ratios • The peaks have different width and amplitude (not consistent with synchronization induced artifact)

26. How are the peaksdistributed over time? • Plasmodium falciparum has different phases during the 48 hours cycle IDC (Ring, Trophozoide, Schizont) • The peaks that we detected seems to concetrate in specific time points. • We used Kolgomorov-Smirnov test for ruling out uniform distribution

27. How are the peaksdistributed over time?

28. Time vs. functional classes

29. Discussion • The peaks do not distributed uniformely over time • There is a (possibly) interesting high number of peaks near a transition phase.

30. Conclusions • We presented • Methods for peak detection • Functonal classificaton via M-SVM • The peaks do not distribute uniformely over time

31. Azzini* R. Dell’Anna F. Ciocchetta F. Demichelis A. Sboner Bioinformatics Group, SRA, ITC-Irst Department of Information and C.T.Trento University, Italy E. Blanzieri A. Malossini Department of Information and Communication Technology University of Trento

32. Biological Interpretation • Critical issue about our analysis

33. opfblob0072 n128_25 f65819_1 m364_2 m12963_1 n159_34 ks244_7 n128_61 opfm60504 l1_28 ks75_15 c154 b593 b597 n176_5 opfh0008 opfblob0105 b541 n132_108 m50253_2 ks1030_4 n128_33 f71224_1 opfh0022 e17542_1 opfblob0072 n128_25 f65819_1 m364_2 m12963_1 n159_34 ks244_7 n128_61 opfm60504 l1_28 ks75_15 c154 b593 b597 n176_5 opfh0008 opfblob0105 b541 n132_108 m50253_2 ks1030_4 n128_33 f71224_1 opfh0022 e17542_1 Selected genes

34. opfblob0072

35. n128_25

36. f65819_1

37. m364_2

38. m12963_1

39. n159_34

40. ks244_7

41. n128_61

42. opfm60504

43. l1_28

44. ks75_15

45. c154

46. b593

47. b597

48. n176_5

49. opfh0008

50. opfblob0105