In one type of gene expression analysis, fluorescently tagged messenger RNA from different cells are hybridized to a microscopic array of thousands of complimentary DNA spots that correspond to different genes. Illuminated spots emit different color light, indicating which genes are expressed (e.g., green=control, red=sample, yellow=both).
In this case study, MATLAB, the Image Processing and Signal Processing toolboxes were used to determine the green intensities from a small portion of a microarray image containing 4,800 spots. A 10x10 pattern of spots was detected by averaging rows and columns to produce horizontal and vertical profiles. Periodicity was determined automatically by autocorrelation and used to form an optimal length filter for morphological background removal. A rectangular grid of bounding boxes was defined. Each spot was individually addressed and segmented by thresholding to form a mask. The mask was used to isolate each spot from surrounding background. Individual spot intensity was determined by integrating pixel intensities. Finally, integrated intensities were tabulated and saved to a data file for subsequent statistical analysis to determine which genes matter most.
With the MATLAB code and example image in this package you can follow the steps used for this application.
Robert Bemis (2023). DNA MicroArray Image Processing Case Study (https://www.mathworks.com/matlabcentral/fileexchange/2573-dna-microarray-image-processing-case-study), MATLAB Central File Exchange. Recuperado .
Compatibilidad con la versión de MATLAB
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Inspiración para: fuzzy k means clustering
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|Versión||Publicado||Notas de la versión|
Algorithm more robust. Problem solution more complete by measuring spot intensity for both color planes and computing expression levels. Update also highlights new Publish capabilities of MATLAB 7.