Microarray Image Analysis: From Image Processing Methods to Gene Expression Levels Estimation
Microarray image processing leads to the characterization of gene expression levels simultaneously, for all cellular transcripts (mRNAs) in a single experiment. The calculation of expression levels for each microarray spot/gene is a crucial step to extract valuable information. By measuring the mRNA...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9178722/ |
| _version_ | 1818557769369780224 |
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| author | Bogdan Belean Robert Gutt Carmen Costea Ovidiu Balacescu |
| author_facet | Bogdan Belean Robert Gutt Carmen Costea Ovidiu Balacescu |
| author_sort | Bogdan Belean |
| collection | DOAJ |
| description | Microarray image processing leads to the characterization of gene expression levels simultaneously, for all cellular transcripts (mRNAs) in a single experiment. The calculation of expression levels for each microarray spot/gene is a crucial step to extract valuable information. By measuring the mRNA levels for the whole genome, the microarray experiments are capable to study functionality, pathological phenotype, and response of cells to a pharmaceutical treatment. The processing of the extensive number of non-homogeneous data contained in microarray images is still a challenge. We propose a density based spatial clustering procedure driven by a level-set approach for microarray spot segmentation together with a complete set of quality measures used to evaluate the proposed method compared with existing approaches for gene expression levels estimation. The set of quality measures used for evaluation include: regression ratios, intensity ratios, mean absolute error, coefficient of variation and fold change factor. We applied the proposed image processing pipeline to a set of microarray images and compared our results with the ones delivered by Genepix, using the aforementioned quality measures. The advantage of our proposed method is highlighted by a selection of up-regulated genes that had been identified exclusively by our approach. These genes prove to add valuable information regarding the biological mechanism activated as a response of Arabidopsis T to pathogen infection. |
| first_indexed | 2024-12-14T00:03:55Z |
| format | Article |
| id | doaj.art-e463727e657740b0baae565746cbdd2a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T00:03:55Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e463727e657740b0baae565746cbdd2a2022-12-21T23:26:09ZengIEEEIEEE Access2169-35362020-01-01815919615920510.1109/ACCESS.2020.30198449178722Microarray Image Analysis: From Image Processing Methods to Gene Expression Levels EstimationBogdan Belean0https://orcid.org/0000-0002-5984-1352Robert Gutt1Carmen Costea2Ovidiu Balacescu3Center of Advanced Research and Technologies for Alternative Energies, National Institute for Research and Development of Isotopic and Molecular Technologies, Cluj-Napoca, RomaniaCenter of Advanced Research and Technologies for Alternative Energies, National Institute for Research and Development of Isotopic and Molecular Technologies, Cluj-Napoca, RomaniaDepartment of Mathematics, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaDepartment of Genetics, Genomics and Experimental Pathology, The Oncology Institute, Prof. Dr. Ion Chiricuta, Cluj-Napoca, RomaniaMicroarray image processing leads to the characterization of gene expression levels simultaneously, for all cellular transcripts (mRNAs) in a single experiment. The calculation of expression levels for each microarray spot/gene is a crucial step to extract valuable information. By measuring the mRNA levels for the whole genome, the microarray experiments are capable to study functionality, pathological phenotype, and response of cells to a pharmaceutical treatment. The processing of the extensive number of non-homogeneous data contained in microarray images is still a challenge. We propose a density based spatial clustering procedure driven by a level-set approach for microarray spot segmentation together with a complete set of quality measures used to evaluate the proposed method compared with existing approaches for gene expression levels estimation. The set of quality measures used for evaluation include: regression ratios, intensity ratios, mean absolute error, coefficient of variation and fold change factor. We applied the proposed image processing pipeline to a set of microarray images and compared our results with the ones delivered by Genepix, using the aforementioned quality measures. The advantage of our proposed method is highlighted by a selection of up-regulated genes that had been identified exclusively by our approach. These genes prove to add valuable information regarding the biological mechanism activated as a response of Arabidopsis T to pathogen infection.https://ieeexplore.ieee.org/document/9178722/Gene expressionlevel-set segmentationclusteringhaustorium formation |
| spellingShingle | Bogdan Belean Robert Gutt Carmen Costea Ovidiu Balacescu Microarray Image Analysis: From Image Processing Methods to Gene Expression Levels Estimation IEEE Access Gene expression level-set segmentation clustering haustorium formation |
| title | Microarray Image Analysis: From Image Processing Methods to Gene Expression Levels Estimation |
| title_full | Microarray Image Analysis: From Image Processing Methods to Gene Expression Levels Estimation |
| title_fullStr | Microarray Image Analysis: From Image Processing Methods to Gene Expression Levels Estimation |
| title_full_unstemmed | Microarray Image Analysis: From Image Processing Methods to Gene Expression Levels Estimation |
| title_short | Microarray Image Analysis: From Image Processing Methods to Gene Expression Levels Estimation |
| title_sort | microarray image analysis from image processing methods to gene expression levels estimation |
| topic | Gene expression level-set segmentation clustering haustorium formation |
| url | https://ieeexplore.ieee.org/document/9178722/ |
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