Feature intensity reconstruction of biological probe array

• US 8,009,889 B2
• Filed: 06/27/2007
• Issued: 08/30/2011
• Est. Priority Date: 06/27/2006
• Status: Active Grant

• Current Assignee: Affymetrix, Inc. (Thermo Fisher Scientific Incorporated)
• Original Assignee: Affymetrix, Inc. (Thermo Fisher Scientific Incorporated)
• Inventors: Hubbell, Earl A.
• Primary Examiner(s): Repko; Jason M
• Assistant Examiner(s): Thomas; Mia M
• Application Number:
  US11/769,600
• Publication Number:
  US 20080232657A1
• Time in Patent Office: 1,525 Days
• Field of Search: 382128-133, 702/20
• US Class Current: 382/129
• CPC Class Codes: G06V 20/695 Preprocessing, e.g. image segmentation
• CPC Class Codes: G06V 2201/04 Recognition of patterns in DNA microarrays
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Abstract

The invention provides methods and systems for reconstructing feature intensities from pixel level data. In certain embodiments, the invention uses an empirically determined transfer function to construct a theoretical estimate of pixel level data and then iteratively updates feature intensities based on a minimum multiplicative error between the pixel level data and the theoretical estimate of the pixel level data.

Claims

1. A method of modifying image data from a biological probe array to determine a final feature intensity for a feature, wherein said feature is comprised of a set of pixels said method comprising:

a. obtaining sample image data comprising a set of observed pixel values from the biological probe array wherein each observed pixel value is a measure of the intensity for a single pixel in the set of pixels and calculating an observed feature intensity for said feature from the observed pixel values;

b. estimating a theoretical pixel intensity for each pixel in the set of pixels using;

(i) an empirically based transfer function that relates the distance of each pixel from the feature center with an intensity value that is proportional to the observed feature intensity, (ii) a background intensity value for said feature, and (iii) an estimated feature intensity for said feature;

c. determining an optimized multiplicative error function using a multiplicative error function and a standard deviation function, wherein said multiplicative error function comprises calculating multiplicative error values that are a ratio of the observed pixel values and said theoretical pixel intensity and the standard deviation function comprises comparing the multiplicative error values to a selected value and removing pixels having multiplicative error values that are greater than the selected value;

d. determining an updated feature intensity by adjusting the observed feature intensity by an update factor, wherein said update factor is calculated by assigning a weight value for each pixel in the set of pixels and using the weight values to define a weight function and multiplying the optimized multiplicative error function with the weight function; and

e. using the updated feature intensity as the final feature intensity if the updated feature intensity has converged to a unique value or repeating steps b to e using the updated feature intensity as the estimated feature intensity.

2. The method of claim 1, wherein, if steps b to e are repeated, the steps further comprise of:

f. multiplying the transfer function by the update factor to provide an updated transfer function; and

g. multiplying the background factor by the update factor to provide an updated background factor, wherein the updated transfer function is used in place of the transfer function and the updated background factor is used in place of the background factor.

3. The method of claim 2, wherein said weight function is limited to 10 pixels from the set of pixels.

4. The method of claim 1, wherein said background value is initialized as the lowest observed value greater than zero for a pixel in the set of observed pixel values of the said feature.

5. The method of claim 1, wherein said transfer function is empirically determined.

6. The method of claim 1, wherein said transfer function further incorporates a function of distance from the center of said feature.

7. The method of claim 1, wherein the center of said feature is updated as an iterative step within a model by choosing an updated center that best fits the data for feature extraction.

8. A computer program product for modifying feature intensity of image data from a biological probe array comprising:

a processor to perform;

a. obtaining sample image data comprising a set of observed pixel values from the biological probe array wherein each observed pixel value is a measure of the intensity for a single pixel in the set of pixels and calculating an observed feature intensity for said feature from the observed pixel values;

b. estimating a theoretical pixel intensity for each pixel in the set of pixels using;

(i) an empirically based transfer function that relates the distance of each pixel from the feature center with an intensity value that is proportional to the observed feature intensity, (ii) a background intensity value for said feature, and (iii) an estimated feature intensity for said feature;

c. determining an optimized multiplicative error function using a multiplicative error function and a standard deviation function, wherein said multiplicative error function comprises calculating multiplicative error values that are a ratio of the observed pixel values and said theoretical pixel intensity and the standard deviation function comprises comparing the multiplicative error values to a selected value and removing pixels having multiplicative error values that are greater than the selected value;

d. determining an updated feature intensity by adjusting the observed feature intensity by an update factor, wherein said update factor is calculated by assigning a weight value for each pixel in the set of pixels and using the weight values to define a weight function and multiplying the optimized multiplicative error function with the weight function; and

e. using the updated feature intensity as the final feature intensity if the updated feature intensity has converged to a unique value or repeating steps b to e using the updated feature intensity as the estimated feature intensity.

9. The computer program product of claim 8, wherein, if steps b to e are repeated, the steps further comprise:

f. multiplying the transfer function by the update factor to provide an updated transfer function; and

g. multiplying the background factor by the update factor to provide an updated background factor, wherein the updated transfer function is used in place of the transfer function and the updated background factor is used in place of the background factor.

10. The computer program product of claim 9, wherein said weight function is limited to 10 pixels from the set of pixels.

11. The computer program product of claim 8, wherein said background value is initialized as the lowest observed value greater than zero for a pixel in the set of observed pixel values of the said feature.

12. The computer program product of claim 8, wherein said transfer function is empirically determined.

13. The computer program product of claim 8, wherein said transfer function further incorporates a function of distance from the center of said feature.

14. The computer program product of claim 8, wherein the center of said feature is updated as an iterative step within a model by choosing an updated center that best fits the data for feature extraction.

15. A system for modifying feature intensity from image data of a biological probe array comprising:

a processor; and

a memory coupled with the processor, the memory storing a plurality of machine instructions that cause the processor to perform logical steps;

wherein the logical steps comprise;

a. obtaining sample image data comprising a set of observed pixel values from the biological probe array wherein each observed pixel value is a measure of the intensity for a single pixel in the set of pixels and calculating an observed feature intensity for said feature from the observed pixel values;

b. estimating a theoretical pixel intensity for each pixel in the set of pixels using;

(i) an empirically based transfer function that relates the distance of each pixel from the feature center with an intensity value that is proportional to the observed feature intensity, (ii) a background intensity value for said feature, and (iii) an estimated feature intensity for said feature;

c. determining an optimized multiplicative error function using a multiplicative error function and a standard deviation function, wherein said multiplicative error function comprises calculating multiplicative error values that are a ratio of the observed pixel values and said theoretical pixel intensity and the standard deviation function comprises comparing the multiplicative error values to a selected value and removing pixels having multiplicative error values that are greater than the selected value;

d. determining an updated feature intensity by adjusting the observed feature intensity by an update factor, wherein said update factor is calculated by assigning a weight value for each pixel in the set of pixels and using the weight values to define a weight function and multiplying the optimized multiplicative error function with the weight function; and

e. using the updated feature intensity as the final feature intensity if the updated feature intensity has converged to a unique value or repeating steps b to e using the updated feature intensity as the estimated feature intensity.

16. The system of claim 15, wherein, if steps b to e are repeated, the steps further comprise:

f. multiplying the transfer function by the update factor to provide an updated transfer function; and

g. multiplying the background factor by the update factor to provide an updated background factor, wherein the updated transfer function is used in place of the transfer function and the updated background factor is used in place of the background factor.

17. The system of claim 16, wherein said weight function is limited to 10 pixels from the set of pixels.

18. The system of claim 15, wherein said background value is initialized as the lowest observed value greater than zero for a pixel in the set of observed pixel values of the said feature.

19. The system of claim 15, wherein said transfer function is empirically determined.

20. The system of claim 15, wherein said transfer function further incorporates a function of distance from the center of said feature.

21. The system of claim 15 wherein the center of said feature is updated as an iterative step within a model by choosing an updated center that best fits the data for feature extraction.

22. A computer readable medium comprising computer-executable instructions for performing the method of modifying feature intensity of image data from a biological probe array, wherein said medium is selected from the group consisting of CD-ROM, floppy disk, tape, flash memory, system memory and hard drive, comprising:

a. obtaining sample image data comprising a set of observed pixel values from the biological probe array wherein each observed pixel value is a measure of the intensity for a single pixel in the set of pixels and calculating an observed feature intensity for said feature from the observed pixel values;

b. estimating a theoretical pixel intensity for each pixel in the set of pixels using;

(i) an empirically based transfer function that relates the distance of each pixel from the feature center with an intensity value that is proportional to the observed feature intensity, (ii) a background intensity value for said feature, and (iii) an estimated feature intensity for said feature;

c. determining an optimized multiplicative error function using a multiplicative error function and a standard deviation function, wherein said multiplicative error function comprises calculating multiplicative error values that are a ratio of the observed pixel values and said theoretical pixel intensity and the standard deviation function comprises comparing the multiplicative error values to a selected value and removing pixels having multiplicative error values that are greater than the selected value;

d. determining an updated feature intensity by adjusting the observed feature intensity by an update factor, wherein said update factor is calculated by assigning a weight value for each pixel in the set of pixels and using the weight values to define a weight function and multiplying the optimized multiplicative error function with the weight function; and

e. using the updated feature intensity as the final feature intensity if the updated feature intensity has converged to a unique value or repeating steps b to e using the updated feature intensity as the estimated feature intensity.

23. The computer readable medium comprising computer-executable instructions for performing the method of claim 22 wherein, if steps b to e are repeated, the steps further comprise:

f. multiplying the transfer function by the update factor to provide an updated transfer function; and

g. multiplying the background factor by the update factor to provide an updated background factor, wherein the updated transfer function is used in place of the transfer function and the updated background factor is used in place of the background factor.

24. The computer readable medium comprising computer-executable instructions for performing the method of claim 23, wherein said weight function is limited to 10 pixels from the set of pixels.

25. The computer readable medium comprising computer-executable instructions for performing the method of claim 22, wherein said background value is initialized as the lowest observed value greater than zero for a pixel in the set of observed pixel values of the said feature.

26. The computer readable medium comprising computer-executable instructions for performing the method of claim 22, wherein said transfer function is empirically determined.

27. The computer readable medium comprising computer-executable instructions for performing the method of claim 22, wherein said transfer function further incorporates a function of distance from the center of said feature.

28. The computer readable medium comprising computer-executable instructions for performing the method of claim 22, wherein the center of said feature is updated as an iterative step within a model by choosing an updated center that best fits the data for feature extraction.