Detecting Salient Changes in Gene Profiles

1. Detecting Salient Changes in Gene Profiles Sohei Okamoto University of Nevada, Reno Dr. Tanveer Syeda-Mahmood IBM Almaden Research Center Dr. George Bebis University of Nevada, Reno Dr. Dwight Egbert University of Nevada, Reno

2. OUTLINE • Motivation • Approach • Method • Difficulty • Future Work

3. MOTIVATION • Salient changes in gene expressions indicate important events such as onset of disease. • Salient changes in multiple gene profiles are similarities among them

4. APPROACH • Salient changes are maxima in vector magnitude function • Build scale-space bitmap, and find salient change contours • Choose optimal smoothing scale by objective functions

5. METHOD – Salient Changes • Finding maxima in vector magnitude function • Salient change is inflection point which has maximum slope

6. METHOD – Salient Changes Maxima and minima of derivative Maxima of vector magnitude Points obtained in input signal

7. METHOD – Scale-Space • Scale-space visualizes how salient changes preserved over increasing scale of smoothing • Gaussian smoothing with increasing standard deviation

8. METHOD – Scale-Space Result of increasing smoothing

9. METHOD – Scale-Space Input signal Vector magnitude Derivative of vector magnitude

10. METHOD – Scale-Space • Construct binary representation of vector magnitude derivative for each increasing scale

11. METHOD – Scale-Space • Negative-going zero-crossing contours in scale-space

12. METHOD – Optimal Scale Selection • Select optimal smoothing scale to ignore non-significant salient change with minimum error • Calculate objective functions and combine

13. METHOD – Optimal Scale Selection • Roughness – # of salient changes at each scale • Mean Square Error – Error between input and smoothed signal at each scale: • Combined objective function – take average of two functions

14. METHOD – Optimal Scale Selection • Find crossing point of two objective functions, which is also minimum of combined objective function

15. METHOD – Optimal Scale Selection • Trace back salient change contours exist at optimal scale to lowest scale

16. METHOD – Optimal Scale Selection • Salient changes found

17. DIFFICULTY • When there are relatively few salient change contours

18. DIFFICULTY • when one contour branch out to two at some lower scale

19. DIFFICULTY • When time points are few • Visualization of more than 3-dimensional signal

20. FUTURE WORK • Collect large amount of results using this method for validity • Adding analysis functionality as an event mining tool • Integration with other data mining tools

21. ACKNOWLEDGMENT • University of Nevada, Reno, Computer Science Department • UNR, Computer Vision Laboratory • UNR-CRCD Program in Computer Vision • IBM Almaden Research Center • National Science Foundation