Feature Intensity Reconstruction of Biological Probe Array

• US 20080232657A1
• Filed: 06/27/2007
• Published: 09/25/2008
• 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.
• Granted Patent: US 8,009,889 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06F 18/00 Pattern recognition
• 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 feature intensity of image data from a biological probe array comprising:

a. obtaining one or more sample image data of a feature comprising a set of observed pixel values from a biological probe array;

b. determining a theoretical pixel intensity with a transfer function representing the proportion of the intensity in a set of pixels due to a feature, a feature intensity of said feature, and a background value of 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 said theoretical pixel intensity and said set of observed pixel values; and

d. determining said feature intensity with an update rule for said feature intensity using a weight function representing the weight of said pixel and the optimized multiplicative error function where said update rule for said feature intensity iteratively generates a unique value to which said feature intensity converges.

2. The method of claim 1, wherein the steps further comprise of:

e. determining an update rule for said transfer function using said weight function and said optimized multiplicative error function; and

f. determining a transfer value of said transfer function where said update rule for said transfer function iteratively generates a unique value to which said transfer value converges.

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

4. The method of claim 1, wherein said background value is initialized as the lowest observed value of said pixel on 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 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 said center that best fit the data for feature extraction.

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

a. obtaining one or more sample image data of a feature comprising a set of observed pixel values from a biological probe array;

b. determining a theoretical pixel intensity with a transfer function representing the proportion of the intensity in a set of pixels due to a feature, a feature intensity of said feature, and a background value of 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 said theoretical pixel intensity and said set of observed pixels values;

d. determining said feature intensity with an update rule for said feature intensity using a weight function representing the weight of said pixel and the optimized multiplicative error function where said update rule for the said feature intensity iteratively generates a unique a value to which said feature intensity converges.

9. The computer program product of claim 8, wherein the steps further comprise of:

e. determining an update rule for said transfer function using said weight function and said optimized multiplicative error function;

f. determining a transfer value of said transfer function where said update rule for said transfer function iteratively generates a unique value to which said transfer value converges.

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

11. The computer program product of claim 8, wherein said background value is initialized as the lowest observed value of said pixel on 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 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 said center that best fits the data for feature extraction.

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

a processor; and

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

wherein the logical steps comprise;

a. obtaining one or more sample image data of a feature comprising a set of observed pixel values from a biological probe array;

b. determining a theoretical pixel intensity with a transfer function representing the proportion of the intensity in a set of pixels due to a feature, a feature intensity of said feature, and a background value of said feature;

c. determining an optimized multiplicative error function using a multiplicative error function and a standard deviation function, wherein said multiplicative error function comprising of said theoretical pixel intensity and said set of observed pixels values;

d. determining said feature intensity with an update rule for said feature intensity using a weight function representing the weight of said pixel and the optimized multiplicative error function where said update rule for said feature intensity iteratively generates a unique a value to which said feature intensity converges.

16. The system of claim 15, wherein the steps further comprise of:

e. determining an update rule for said transfer function using said weight function and said optimized multiplicative error function;

f. determining the a transfer value of said transfer function where said update rule for said transfer function iteratively generates a unique value to which said transfer value converges.

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

18. The system of claim 15, wherein said background value is initialized as the lowest observed value of said pixel on 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 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 said 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 comprising:

a. obtaining one or more sample image data of a feature comprising a set of observed pixel values from a biological probe array;

b. determining a theoretical pixel intensity with a transfer function representing the proportion of the intensity in a set of pixels due to a feature, a feature intensity of said feature, and a background value of 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 said theoretical pixel intensity and said set of observed pixels values;

d. determining said feature intensity with an update rule for said feature intensity using a weight function representing the weight of said pixel and the optimized multiplicative error function where said update rule for said feature intensity iteratively generates a unique a value to which said feature intensity converges.

23. The computer readable medium comprising computer-executable instructions for performing the method of claim 23 wherein the steps further comprise of:

e. determining an update rule for said transfer function using said weight function and said optimized multiplicative error function;

f. determining the a transfer value of said transfer function where the said update rule for said transfer function iteratively generates a unique value to which said transfer value converges.

25. The computer readable medium comprising computer-executable instructions for performing the method of claim 23, wherein said back ground value is initialized as the lowest observed value of said pixel on said feature.

26. The computer readable medium comprising computer-executable instructions for performing the method of claim 23, wherein said transfer function is empirically determined.

27. The computer readable medium comprising computer-executable instructions for performing the method of claim 23, wherein said transfer function further incorporates a function of distance from center of said feature.

28. The computer readable medium comprising computer-executable instructions for performing the method of claim 23, wherein the center of said feature is updated as an iterative step within a model by choosing said center that best fits the data for feature extraction.

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