Imaging microarrays

• US 20030124589A1
• Filed: 10/11/2002
• Published: 07/03/2003
• Est. Priority Date: 10/12/2001
• Status: Active Grant

• Current Assignee: Abbott Molecular Incorporated (Abbott Laboratories)
• Original Assignee: Vysis, Inc. (Abbott Laboratories)
• Inventors: Piper, James R.
• Granted Patent: US 7,636,636 B2
• Time in Patent Office: Days
• US Class Current: 435/6
• CPC Class Codes: G06T 2207/10056 Microscopic image
• CPC Class Codes: G06T 2207/10064 Fluorescence image
• CPC Class Codes: G06T 2207/30072 Microarray; Biochip, DNA array; Well plate
• CPC Class Codes: G06T 5/002 Denoising; Smoothing noise processing or correction adapted to be used in an image pickup device containing and electronic image sensor H04N23/81, H04N25/60 - H04N25/67
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Abstract

A method of obtaining a corrected image of a microarray includes acquiring an image of a microarray including a target spot, and processing the image to correct for background noise and chip misalignment. The method also includes analyzing the image to identify a target patch, edit debris, and correct for ratio bias; and detecting single copy number variation in the target spot using an objective statistical analysis that includes a t-value statistical analysis. The method provides statistically robust computational processes for accurately detecting genomic variation at the single copy level.

Claims

1. A method of obtaining a corrected image of a microarray, the method comprising:

acquiring an image of a microarray including a target spot;

processing the image to correct for background noise and chip misalignment;

analyzing the image to identify a target patch, edit debris, and correct for ratio bias; and

,

detecting single copy number variation in the target spot using an objective statistical analysis that includes a t-value statistical analysis.

2. The method of claim 1, wherein the target spot is Deoxyribonucleic Acid (DNA).

3. The method of claim 1, further comprising preparing a genomic material prior to acquiring the image and generating an array of the genomic material on a solid substrate.

4. The method of claim 1, wherein the genomic material includes a range between 50 and 200 kbp.

5. The method of claim 3, wherein preparing the genomic material includes isolating the genomic material by an extraction process followed by nick translation labeling and polymerase chain reaction (PCR) labeling.

6. The method of claim 1, wherein the genomic microarray includes a test genomic material marked with green receptors having a first wavelength and a reference genomic material marked with red receptors having a second wavelength, the test and reference genomic materials forming the target spot by a hybridization process.

7. The method of claim 6, wherein the hybridization process is a comparative genomic hybridization (CGH) process.

8. The method of claim 6, further comprising measuring a fluorescent signal intensity of the target spot from the test genomic material and the reference genomic material, wherein the fluorescent signal intensity of the first wavelength is proportional to a copy number of the test genomic material and the fluorescent signal intensity of the second wavelength is proportional to a copy number of the reference genomic material.

9. The method of claim 1, wherein subsequent to acquiring the image of the genomic microarray and prior to correcting for the background noise, processing the image includes automatically detecting misalignment of the genomic microarray and correcting for rotation of the genomic microarray.

10. The method of claim 9, wherein correcting for rotation of the genomic microarray includes applying a bicubic interpolation computation for pixel values of the image.

11. The method of claim 1, wherein correcting of the background noise includes:

computing the acquired image;

computing a minimum image based on the computed acquired image;

computing a maximum image based on the computed minimum image;

subtracting a background image from the computed acquired image to obtain a resulting image; and

,

optimizing the correction for the background noise by identifying a background peak of the resulting image and subtracting the resulting image to obtain a mean value of zero for a corrected image.

12. The method of claim 11, wherein the corrected image includes pixels having signed values.

13. The method of claim 1, wherein identifying a target patch includes:

obtaining an image of a theoretical set of patches; and

,

cross-correlating the image of the theoretical set of patches to a counterstained image.

14. The method of claim 12, wherein following identifying a target patch, the method further comprises:

computing a threshold by analyzing a pixel intensity of the counterstained image to determine an initial segmentation of the target spot; and

,

performing a process for spot shape analysis and spot segmentation.

15. The method of claim 14, further comprising:

performing a spot identification analysis; and

performing an artifact spot exclusion process wherein an artifact spot is automatically excluded.

16. The method of claim 1, wherein editing of debris includes automatically removing spot debris by recognizing and excluding spot pixels within a target spot having outlying intensity relative to a majority of the spot pixels.

17. The method of claim 16, wherein editing of debris includes:

transforming test and reference intensities to polar coordinates having as a first coordinate the overall intensity of spot signals and having as a second coordinate the ratio;

applying a standard statistical test for outliers to the first and second coordinates; and

,

analyzing the shape of excluded spot pixels.

18. The method of claim 1, further comprising computing a ratio based on a test and a reference fluorescence signal intensity value associated with the target spot.

19. The method of claim 18, wherein correcting for ratio bias includes measuring a raw ratio representing a ratio of total green fluorescence intensity from the test fluorescence signal intensity value and a total red fluorescence intensity from the reference fluorescence signal intensity value.

20. The method of claim 19, further comprising normalizing the raw ratio by mathematical computation.

21. The method of claim 20, wherein correcting for ratio bias includes:

determining whether data relating to the corrected image is known or unknown;

if the data is known, applying a first ratio bias equation, R=r<sup>−

B</sup>, where R is the ratio, and r the reference intensity; and

,

if the data is unknown, applying a second ratio bias equation, R_corrected=R_original/R_predicted, where R_corrected is a corrected ratio, R_original is an original ratio, and R_predicted is a predicted ratio.

22. The method of claim 1, wherein the objective statistical analysis includes a t-value statistical analysis.

23. The method of claim 1, wherein the objective statistical analysis includes a t-value statistical analysis, the t-value statistical analysis including:

computing a ratio value using a test;

reference data and a test;

test data;

computing a variance value for the test;

reference data and the test;

test data;

applying an adjusted t-statistical test derived from the ratio and variance values; and

,

applying a final t-value equation for detecting single copy number variation.

24. The method of claim 23, wherein the adjusted t-statistical test includes an equation,

25. The method of claim 24, wherein the final t-value equation is

26. The method of claim 1, wherein the objective statistical analysis includes a target-modal statistical analysis.

27. The method of claim 26, wherein the target-modal statistical analysis includes:

plotting modal distribution data;

determining whether the distribution of the modal distribution data is significantly normal and normalizing the distribution data if the distribution is not significantly normal;

computing an estimated target mean and a lower 95% value for a confidence limit interval; and

reducing the confidence limit interval by a median confidence interval over the spots and calculating a confidence estimate for each spot.

28. The method of claim 27, further comprising estimating a relative copy number of a genomic sequence from the target spot using the target-modal statistical analysis.

29. A computer program product residing on a computer readable medium having instructions stored thereon which;

when executed by the processor, cause the processor to;

acquire an image of a microarray including a target spot;

process the image to correct for background noise and chip misalignment;

analyze the image to identify the target patch, edit debris, and correct for ratio bias; and

,

detect single copy number variation in the target spot using an objective statistical analysis.

30. The computer program product of claim 29, wherein the target spot is Deoxyribonucleic Acid (DNA).

31. The computer program product of claim 29, further causing the processor to:

measure a fluorescent signal intensity of the target spot from the test genomic material and the reference genomic material, wherein the fluorescent signal intensity of the first wavelength is proportional to a copy number of the test genomic material and the fluorescent signal intensity of the second wavelength is proportional to a copy number of the reference genomic material.

32. The computer program product of claim 29, wherein subsequent to causing the processor to acquire the image of the genomic microarray and prior to causing the processor to process the image for correcting the background noise, causing the processor to process the image further includes automatically causing the processor to detect misalignment of the genomic microarray and correct rotation of the genomic microarray.

33. The computer program product of claim 29, wherein the genomic material includes a range between 50 kbp and 200 kbp.

34. The computer program product of claim 29, wherein the genomic microarray includes a test genomic material marked with green receptors having a first wavelength and a reference genomic material marked with red receptors having a second wavelength, both test and reference genomic materials forming the target spot by a hybridization process.

35. The computer program product of claim 32, wherein causing the processor to correct for rotation of the genomic microarray includes causing the processor to apply a bicubic interpolation computation for pixel values of the image.

36. The computer program product of claim 29, further causing the processor to:

compute a ratio based on a test and a reference fluorescence signal intensity value associated with the target spot.

37. The computer program product of claim 35, wherein correcting for ratio bias includes measuring a raw ratio representing a ratio of total green fluorescence intensity from the test fluorescence signal intensity value and a total red fluorescence intensity from the reference fluorescence signal intensity value.

38. The computer program product of claim 37, further causing the processor to normalize the raw ratio by mathematical computation.

39. The computer program product of claim 38, wherein correcting for ratio bias includes:

determining whether data relating to the image is known or unknown;

if the data is known, applying a first ratio bias equation, R=r<sup>−

B</sup>, where R is the ratio, and r the reference intensity; and

,

if the data is unknown, applying a second ratio bias equation, R_corrected=R_original/R_predicted, where R_corrected is a corrected ratio, R_original is an original ratio, and R_predicted is a predicted ratio.

40. The computer program product of claim 29, wherein the objective statistical analysis includes a t-value statistical analysis.

41. The computer program product of claim 40, wherein the t-value statistical analysis includes:

computing a ratio value using a test;

reference data and a test;

test data;

computing a variance value for the test;

reference data and the test;

test data;

applying an adjusted t-statistical test derived from the ratio and variance values; and

,

applying a final t-value equation for detecting single copy number variation.

42. The computer program product of claim 41, wherein the adjusted t-statistical test includes an equation,

43. The computer program product of claim 42, wherein the final t-value equation is

44. The computer program product of claim 29, wherein the objective statistical analysis includes a target-modal statistical analysis.

45. The computer program product of claim 44, wherein the target-modal statistical analysis includes:

plotting modal distribution data;

determining whether the distribution of the modal distribution data is significantly normal and normalizing the distribution data if the distribution is not significantly normal;

computing an estimated target mean and a lower 95% value for a confidence limit interval; and

reducing the confidence limit interval by a median confidence interval over the spots and calculating a confidence estimate for each spot.

46. A processor and a memory configured to:

acquire an image of a microarray including a target spot;

process the image to correct for background noise and chip misalignment;

analyze the image to identify a target patch, edit debris, and correct for ratio bias; and

,

detect single copy number variation in the target spot using an objective statistical analysis.

47. A system comprising:

means for acquiring an image of a microarray including a target spot;

means for processing the image to correct for background noise and chip misalignment;

means for analyzing the image to identify a target patch, edit debris, and correct for ratio bias; and

,

means for detecting single copy number variation in the target spot using an objective statistical analysis.