Method to automatically decode microarray images

Abstract

A method of automatically identifying the microarray chip corners and probes, even if there are no probes at the corners, in a high density and high resolution microarray 5 scanned image having an image space, wherein the method minimizes the error distortions in the image arising in the scanning process by applying to the image a multipass corner finding algorithm comprising: (a) applying a Radon transform to an input microarray image to project the image into an angle and distance space where it is possible to find the orientation of the straight lines; (b) applying a fast Fourier transform to the projected image 10 of (a) to find the optimal tilting angle of the projected image; (c) determining the optimal first and last local maxima for the optimal tilting angle; (d) back projecting the determined first and last local maxima to the image space to find the first approximation of the first and last column lines of the image; (e) rotating the image and repeating steps (a) through (d) to find the first approximation of the top and bottom row lines of the image; (f) determining 15 the first approximation of the four corners of the image from the intersection of the column and row lines; (g) applying a heuristic for determining if the first approximation of step (f) is sufficient; and (h) optionally trimming the scanned image around the first approximation of the four corners and repeating steps (a) through (f).

Description

[0001] Cross References to Related Applications [0002] Applicants claim priority to provisional application Serial No. 60 / 868,129, filed December 1, 2006. technical field [0003] The present invention relates to methods for automatically determining the position of probe spots on microarray images so that the image data can be converted into a measure of biological activity. Background technique [0004] DNA microarray technology is relatively new and is developing rapidly. Currently, it is widely used in systematic studies of global gene expression projects: using microarrays to infer gene function, measuring polymorphisms in gene copy number, and genomic DNA-protein interactions. Extracting data from microarray images has many inherent problems: inconsistent hybridization leads to uneven probe intensities and geometries; placement of the chip into the scanner is not fixed, meaning the corners of the chip can be anywhere ; Scanners often distort the resulting images, a...

AI Technical Summary

Problems solved by technology

Extracting data from microarray images has many inherent problems: inconsistent hybridization leads to uneven probe intensities and geometries; placement of the chip into the scanner is not fixed, meaning the corners of the chip can be anywhere ;scanners often distort the resulting images, which creates problems for converting these images into the required data; since there are many different design patterns at the same time, benchmarks can be placed in many different combinations

Brandle et al. [Ref. 1] and Uehara and Kakadiaris [Ref. 6] both present methods for all the functions needed to convert an image to the desired value, and both advocate the use of the Radon transform (see below), but as far as we know However, no method has been proposed for correcting the keystone problem in microarray images

The software tool BioDiscovery Imagene 7.0 claims to automatically find sites and place grids even in batch-mode processing for multiple arrays, however these algorithms are not currently publicly available (http: / / www / biodiscovery.com / index / imagene-cgh)

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