Image analysis of high-density synthetic DNA microarrays

• US 20030087289A1
• Filed: 09/30/2002
• Published: 05/08/2003
• Est. Priority Date: 10/12/2001
• Status: Abandoned Application

• Current Assignee: Duke University
• Original Assignee: Duke University
• Inventors: Zuzan, Harry, Johnson, Valen E.
• Application Number:
  US10/261,570
• Publication Number:
  US 20030087289A1
• Time in Patent Office: Days
• US Class Current: 435/6
• CPC Class Codes: G06T 2207/30072 Microarray; Biochip, DNA array; Well plate
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06V 10/30 Noise filtering
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Abstract

Methods, systems, and computer program products for analyzing images of high density microarray chips analyze the image by estimating background using a blurring kernel and/or a spatial multivariate statistical model of the background. The methods, systems, and computer program products can employ a multivariate statistical model and/or a blurring kernel to obtain more representative hybridization intensity results, particularly for pixels in boundary regions of the probe cells. The methods allow for alternative microarray configurations of nucleic acid probes and do not require the use of mismatch probes and can be independent of the type of nucleotide sequence used. Associated microarrays and systems are also described.

Claims

1. A method for evaluating an image of a hybridized microarray, comprising:

obtaining an image of a microarray having a plurality of individual probe cells;

estimating the background intensity in the image based on a multivariate statistical model comprising at least one of;

(a) a blurring kernel used to deconvolute blur in the image; and

(b) a multivariate statistical spatial model for the background; and

determining estimated intensity values of pixels in the image by based on data from the background estimation.

2. A method according to claim 1, wherein the statistical model includes a Markov random field to model the spatial distribution of the background.

3. A method according to claim 1, wherein the statistical model includes a blurring kernel and the step of estimating considers intensity values of pixels in boundary regions of the probe cells undergoing analysis.

4. A method according to claim 2, wherein the step of estimating comprises using Gibbs sampling methods.

5. A method according to claim 2, wherein the statistical model comprises a blurring kernel used to deconvolute the image of the probe cell in the image to thereby more closely represent the intensities of the edge portions of the physical probe cell on the microarray.

6. A method according to claim 1, further comprising the step of mapping a spatial distribution of the background intensity across the image.

7. A method according to claim 1, further comprising the step of identifying an image artifact or abnormality based on the background intensity data.

8. A method according to claim 1, wherein the step of estimating is carried out on a plurality of images of different microarrays independent of a nucleotide sequence layout thereon.

9. A method according to claim 5, wherein the blurring kernel is adjusted to represent a probe cell which is not square.

10. A method according to claim 1, further comprising ranking the results of the hybridization based after the intensity values are adjusted by background data provided by the step of estimating.

11. A method according to claim 1, wherein the step of estimating comprises calculating an individual background estimation value for each pixel in at least a selected portion of the image, and said method comprises obtaining first estimated intensity values of the pixels and then calculating second adjusted estimated intensity values based on the data obtained in the step of estimating the background.

12. A method according to claim 1, wherein the step of estimating comprises logarithmically transforming individual pixel intensities.

13. A method for evaluating an image of an expressed microarray, comprising:

obtaining an image of an expressed microarray having a plurality of individual probe cells;

estimating the locations of each probe cell undergoing analysis, each probe cell location being and regions proximate thereto-defining pixels influenced by the fluorescence or lack of fluorescence of the probe cell;

determining first estimated pixel intensity values for pixels in the probe cell locations;

estimating the intensity of the background for pixels in the image to estimate a spatial distribution of the background intensity in the image; and

for each pixel in the image, reducing the first estimated pixel intensity value to a second estimated pixel intensity value based on the data provided by the estimated intensity of background.

14. A method according to claim 13, further comprising analyzing the estimated background in the image to identify an abnormality or artifact in the image.

15. A method according to claim 14, wherein the step of analyzing comprises determining whether the intensity of background illumination is substantially constant or makes an abrupt change across the probe cell in the image to assess whether there is an abnormality.

16. A method according to claim 13, wherein the step of estimating employs a multivariate spatial model of the background.

17. A method according to claim 13, wherein the step of estimating employs a blurring kernel to deconvolute the effect of blur in the image to more closely represent the features in the image.

18. A method for evaluating an image of an HSDM, comprising:

obtaining an image of an HSDM having a plurality of individual probe cells;

estimating the location of each probe cell undergoing analysis in the image, the probe cell location including a plurality of pixels;

obtaining first estimated pixel intensity values for pixels in a region associated with the probe cell in the image;

estimating background intensity for each probe cell region to obtain a spatial distribution of the background intensity in the image, wherein the step of estimating is performed such that the background intensity of the pixels can vary pixel to pixel in the image; and

determining second estimated pixel intensity values for each probe cell by reducing the first estimated pixel intensity value by its corresponding estimated background intensity.

19. A method according to claim 18, wherein the estimated background intensity value is calculated individually for each pixel in the image.

20. A method according to claim 19, wherein the step of determining the second estimated pixel intensity value comprises subtracting its corresponding estimated background value from the corresponding first estimated intensity value to generate an image adjusted at pixel level resolution for background contributions.

21. A method according to claim 18, wherein the step of estimating employs a blurring kernel to deconvolute the blur in the image.

22. A method according to claim 18, wherein the step of estimating employs a predetermined multivariate statistical spatial model which considers distributional parameters which contribute to background in the image.

23. A method according to claim 22, wherein the statistical model comprises a Markov random field.

24. A method of analyzing data obtained from an image of an hybridized microarray, comprising:

analyzing image data by using a blurring kernel to deconvolute the blurred probe cells in the image to more closely represent the intensity of the fluorescence over the entire probe cell; and

generating a revised image with adjusted pixel intensity values based on the analysis of the image data.

25. A method according to claim 24, further comprising estimating the background intensity by using a spatial multivariate model of the background.

26. A method according to claim 25, further comprising adjusting the intensity values of pixels in the image based on data obtained by the background estimation and evaluating the results of hybridization of the probe cell locations in the image without considering mismatch probe sets.

27. A method according to claim 25, wherein the step of analyzing is carried out independent of the sequence of the nucleotides on the microarray.

28. A computer program product for analyzing an image of a hybridized nucleic acid microarray chip, the computer program product comprising:

a computer readable storage medium having computer readable program code embodied in said medium, said computer-readable program code comprising;

computer readable program code that obtains data of an image of the intensities of a hybridized microarray having a plurality of individual probe cells;

computer readable program code that determines first estimated intensity values of pixels in the image;

computer readable program code that calculates estimates the intensity of background for pixels in the image based on at least one of;

(a) a multivariate statistical spatial model of the background; and

(b) a blurring kernel to deconvolute the blurring in the image; and

computer readable program code that determines second estimated intensity values of pixels in the image by correcting the first estimated values based on data obtained by the estimated intensity of background.

29. A computer program product according to claim 28, wherein said computer program code for rendering the multivariate statistical spatial model includes a Markov random field.

30. A computer program product according to claim 28, wherein said computer program product further comprises computer readable program code for logarithmically transforming the intensity data of the pixels.

31. A computer program product according to claim 28, wherein said computer program product for calculating estimates the intensity of background illumination comprises both the blurring kernel and the multivariate statistical spatial model.

32. A computer program product according to claim 31, wherein said computer readable program code for the blurring kernel revises the image intensity data of the image to more closely represent the intensities of the entire probe cell.

33. A computer program product according to claim 31, wherein said computer program product further comprises computer readable program code for electronically mapping a spatial distribution of the background across the image.

34. A computer program product according to claim 31, wherein said computer program product further comprises computer readable program code for identifying an image artifact or abnormality.

35. A computer program product for analyzing data representing an image of a hybridized nucleic acid microarray chip, the computer program product comprising:

a computer readable storage medium having computer readable program code embodied in said medium, said computer-readable program code comprising;

computer readable program code that obtains intensity data of an image of a hybridized microarray having a plurality of probe cells; and

computer readable program code that calculates an estimated spatial distribution of the intensity of the background in the image, the estimated intensity of the background being determined based on pixels in the image which correspond to locations on the array which have active nucleic acid probes.

36. A system for analyzing images of hybridized arrays of nucleic acid probes, comprising:

a processor; and

means for estimating background in an image using a predetermined spatial multivariate statistical model for the background.

37. A microarray having a substrate and a plurality of nucleic acid probe cells positioned on a primary surface thereof, wherein said probe cells have a hexagonal shaped perimeter.

38. An array of oligonucleotide probes immobilized on a solid support, wherein said array has a hybridization surface which is substantially free of mismatch probes.

39. An array of oligonucleotide probes immobilized on a solid support, wherein said array is sized at about 1.28 cm×

1.28 cm or less, and wherein said array comprises at least about 400,000 individual perfect match probe cells thereon.

40. An array of oligonucleotide probes according to claim 39, wherein said array has a hybridization surface which is substantially free of mismatch probes, and wherein each of said probes are sized to cover an area on the hybridization surface which is about 21.5 μ

m×

25 μ

m or less.

41. An array of oligonucleotide probes immobilized on a solid support, wherein said array has a hybridization surface which is substantially free of mismatch probes, and wherein said probes are sized to cover an area on the hybridization surface which is about 21.5 μ

m×

21.5 μ

m or less.

42. An array according to claim 41, wherein said array comprises at least about 400,000 individual perfect match probe cells thereon.

43. A method of classifying the results of hybridization in expression probe arrays of nucleic acid probes, comprising:

estimating the background intensity in an image of a hybridized microarray using at least one of a blurring kernel to deconvolute blur in the image and a spatial multivariate model for the background;

adjusting the image intensity based on data provided by said estimating step;

ranking the probe cells based on the adjusted intensity, wherein said step of ranking is carried out without regard to information from mismatch probes; and

classifying the results of the hybridization based on said step of ranking.