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M3G: maximum margin microarray gridding.

Bariamis D, Iakovidis DK, Maroulis D - BMC Bioinformatics (2010)

Bottom Line: The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.The results show that M3G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts.The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Informatics and Telecommunications, University of Athens, Athens, Greece. d.bariamis@di.uoa.gr

ABSTRACT

Background: Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.

Methods: In this paper we propose M3G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.

Results: The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M3G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels.

Conclusions: The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.

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Related in: MedlinePlus

Example of successful gridding in the presence of a large and bright artefact.
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Figure 8: Example of successful gridding in the presence of a large and bright artefact.

Mentions: Fig. 8 illustrates the gridding that results from the application of M3G on a microarray image area that includes a large and bright artefact. Even in the vicinity of the artefact, the gridding is not affected by its presence. Fig. 9 illustrates the resulting gridding for three more such images, including a detailed view of the area around each artefact. Despite the presence of these artefacts, the proposed method achieves successful gridding in all those cases.


M3G: maximum margin microarray gridding.

Bariamis D, Iakovidis DK, Maroulis D - BMC Bioinformatics (2010)

Example of successful gridding in the presence of a large and bright artefact.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2823709&req=5

Figure 8: Example of successful gridding in the presence of a large and bright artefact.
Mentions: Fig. 8 illustrates the gridding that results from the application of M3G on a microarray image area that includes a large and bright artefact. Even in the vicinity of the artefact, the gridding is not affected by its presence. Fig. 9 illustrates the resulting gridding for three more such images, including a detailed view of the area around each artefact. Despite the presence of these artefacts, the proposed method achieves successful gridding in all those cases.

Bottom Line: The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.The results show that M3G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts.The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Informatics and Telecommunications, University of Athens, Athens, Greece. d.bariamis@di.uoa.gr

ABSTRACT

Background: Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.

Methods: In this paper we propose M3G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.

Results: The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M3G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels.

Conclusions: The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.

Show MeSH
Related in: MedlinePlus