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Journal articles on the topic 'CDNA microarray'

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Published: 4 June 2021

Last updated: 10 March 2023

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1

Handley, Daniel, Nicoleta Serban, David G. Peters, and Clark Glymour. "Concerns About Unreliable Data from Spotted cDNA Microarrays Due to Cross-Hybridization and Sequence Errors." Statistical Applications in Genetics and Molecular Biology 3, no. 1 (January 6, 2004): 1–2. http://dx.doi.org/10.2202/1544-6115.1091.

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We discuss our concerns regarding the reliability of data generated by spotted cDNA microarrays. Two types of error we highlight are cross-hybridization artifact due to sequence homologies and sequence errors in the cDNA used for spotting on microarrays. We feel that statisticians who analyze microarray data should be aware of these sources of unreliability intrinsic to cDNA microarray design and use.

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2

Wang, Zidong, Bachar Zineddin, Jinling Liang, Nianyin Zeng, Yurong Li, Min Du, Jie Cao, and Xiaohui Liu. "cDNA microarray adaptive segmentation." Neurocomputing 142 (October 2014): 408–18. http://dx.doi.org/10.1016/j.neucom.2014.03.052.

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3

Pérez-Enciso, Miguel, Miguel A. Toro, Michel Tenenhaus, and Daniel Gianola. "Combining Gene Expression and Molecular Marker Information for Mapping Complex Trait Genes: A Simulation Study." Genetics 164, no. 4 (August 1, 2003): 1597–606. http://dx.doi.org/10.1093/genetics/164.4.1597.

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Abstract A method for mapping complex trait genes using cDNA microarray and molecular marker data jointly is presented and illustrated via simulation. We introduce a novel approach for simulating phenotypes and genotypes conditionally on real, publicly available, microarray data. The model assumes an underlying continuous latent variable (liability) related to some measured cDNA expression levels. Partial least-squares logistic regression is used to estimate the liability under several scenarios where the level of gene interaction, the gene effect, and the number of cDNA levels affecting liability are varied. The results suggest that: (1) the usefulness of microarray data for gene mapping increases when both the number of cDNA levels in the underlying liability and the QTL effect decrease and when genes are coexpressed; (2) the correlation between estimated and true liability is large, at least under our simulation settings; (3) it is unlikely that cDNA clones identified as significant with partial least squares (or with some other technique) are the true responsible cDNAs, especially as the number of clones in the liability increases; (4) the number of putatively significant cDNA levels increases critically if cDNAs are coexpressed in a cluster (however, the proportion of true causal cDNAs within the significant ones is similar to that in a no-coexpression scenario); and (5) data reduction is needed to smooth out the variability encountered in expression levels when these are analyzed individually.

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4

Yang, Y. H. "Analysis of cDNA microarray images." Briefings in Bioinformatics 2, no. 4 (January 1, 2001): 341–49. http://dx.doi.org/10.1093/bib/2.4.341.

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5

Emi, Mitsuru. "cDNA Microarray and SNP Analysis." Journal of Nippon Medical School 68, no. 5 (2001): 411–12. http://dx.doi.org/10.1272/jnms.68.411.

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6

Smyth, Gordon K., and Terry Speed. "Normalization of cDNA microarray data." Methods 31, no. 4 (December 2003): 265–73. http://dx.doi.org/10.1016/s1046-2023(03)00155-5.

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7

Mizukami, Satomi, Yoshiteru Suzuki, Emiko Kitagawa, and Hitoshi Iwahashi. "Standardization of cDNA microarray technology for toxicogenomics; essential data for initiating cDNA microarray studies." Chem-Bio Informatics Journal 4, no. 2 (2004): 38–55. http://dx.doi.org/10.1273/cbij.4.38.

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8

Liang, Mingyu, Amy G. Briggs, Elizabeth Rute, Andrew S. Greene, and Allen W. Cowley. "Quantitative assessment of the importance of dye switching and biological replication in cDNA microarray studies." Physiological Genomics 14, no. 3 (August 15, 2003): 199–207. http://dx.doi.org/10.1152/physiolgenomics.00143.2002.

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Dye switching and biological replication substantially increase the cost and the complexity of cDNA microarray studies. The objective of the present analysis was to quantitatively assess the importance of these procedures to provide a quantitative basis for decision-making in the design of microarray experiments. Taking advantage of the unique characteristics of a published data set, the impact of these procedures on the reliability of microarray results was calculated. Adding a second microarray with dye switching substantially increased the correlation coefficient between observed and predicted ln(ratio) values from 0.38 ± 0.06 to 0.62 ± 0.04 ( n = 12) and the outlier concordance from 21 ± 3% to 43 ± 4%. It also increased the correlation with the entire set of microarrays from 0.60 ± 0.04 to 0.79 ± 0.04 and the outlier concordance from 31 ± 6% to 58 ± 5% and tended to improve the correlation with Northern blot results. Adding a second microarray to include biological replication also improved the performance of these indices but often to a lesser degree. Inclusion of both procedures in the second microarray substantially improved the consistency with the entire set of microarrays but had minimal effect on the consistency with predicted results. Analysis of another data set generated using a different cDNA labeling method also supported a significant impact of dye switching. In conclusion, both dye switching and biological replication substantially increased the reliability of microarray results, with dye switching likely having even greater benefits. Recommendations regarding the use of these procedures were proposed.

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9

Kuzhali, S. Elavaar, and Suresh D. S. "Collaborative Priors with SVD for Denoising of cDNA Microarray Images." Indian Journal of Science and Technology 12, no. 37 (October 10, 2019): 1–15. http://dx.doi.org/10.17485/ijst/2019/v12i37/147036.

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10

Becker, Kevin G. "The sharing of cDNA microarray data." Nature Reviews Neuroscience 2, no. 6 (June 2001): 438–40. http://dx.doi.org/10.1038/35077580.

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11

Yue Wang, Jianping Lu, R. Lee, Zhiping Gu, and R. Clarke. "Iterative normalization of cDNA microarray data." IEEE Transactions on Information Technology in Biomedicine 6, no. 1 (March 2002): 29–37. http://dx.doi.org/10.1109/4233.992159.

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12

Welling, D. Bradley, John M. Lasak, Elena Akhmametyeva, Bobak Ghaheri, and Long-Sheng Chang. "cDNA Microarray Analysis of Vestibular Schwannomas." Otology & Neurotology 23, no. 5 (September 2002): 736–48. http://dx.doi.org/10.1097/00129492-200209000-00022.

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13

Roy, Sashwati, and Chandan K. Sen. "cDNA microarray screening in food safety." Toxicology 221, no. 1 (April 2006): 128–33. http://dx.doi.org/10.1016/j.tox.2005.12.025.

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14

Xiang, Charlie C., and Yidong Chen. "cDNA microarray technology and its applications." Biotechnology Advances 18, no. 1 (March 2000): 35–46. http://dx.doi.org/10.1016/s0734-9750(99)00035-x.

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15

Sahar, David E., George P. Yang, Michael T. Longaker, and Alden H. Harken. "Surgical application of cDNA microarray technique." Surgery 138, no. 3 (September 2005): 399–403. http://dx.doi.org/10.1016/j.surg.2005.01.011.

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16

Yang, Yee Hwa, and Terry Speed. "Design issues for cDNA microarray experiments." Nature Reviews Genetics 3, no. 8 (August 2002): 579–88. http://dx.doi.org/10.1038/nrg863.

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17

Burgueño, Juan, Jose Crossa, Daniel Grimanelli, Olivier Leblanc, and Daphne Autran. "Spatial Analysis of cDNA Microarray Experiments." Crop Science 45, no. 2 (March 2005): 748–57. http://dx.doi.org/10.2135/cropsci2005.0748.

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18

Folpe, Andrew L. "cDNA microarray study of synovial sarcomas." Advances in Anatomic Pathology 10, no. 4 (July 2003): 237. http://dx.doi.org/10.1097/00125480-200307000-00010.

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19

Washington, Kay. "cDNA Microarray Study of Biliary Atresia." Advances in Anatomic Pathology 11, no. 1 (January 2004): 65. http://dx.doi.org/10.1097/00125480-200401000-00007.

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20

Leung, Yuk Fai, and Duccio Cavalieri. "Fundamentals of cDNA microarray data analysis." Trends in Genetics 19, no. 11 (November 2003): 649–59. http://dx.doi.org/10.1016/j.tig.2003.09.015.

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21

LEE, PHILIP R., JONATHAN E. COHEN, ELISABETTA A. TENDI, ROBERT FARRER, GEORGE H. DE VRIES, KEVIN G. BECKER, and R. DOUGLAS FIELDS. "Transcriptional profiling in an MPNST-derived cell line and normal human Schwann cells." Neuron Glia Biology 1, no. 2 (May 2004): 135–47. http://dx.doi.org/10.1017/s1740925x04000274.

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cDNA microarrays were utilized to identify abnormally expressed genes in a malignant peripheral nerve sheath tumor (MPNST)-derived cell line, T265, by comparing the mRNA abundance profiles with that of normal human Schwann cells (nhSCs). The findings characterize the molecular phenotype of this important cell-line model of MPNSTs, and elucidate the contribution of Schwann cells in MPNSTs. In total, 4608 cDNA sequences were screened and hybridizations replicated on custom cDNA microarrays. In order to verify the microarray data, a large selection of differentially expressed mRNA transcripts were subjected to semi-quantitative reverse transcription PCR (LightCycler). Western blotting was performed to investigate a selection of genes and signal transduction pathways, as a further validation of the microarray data. The data generated from multiple microarray screens, semi-quantitative RT–PCR and Western blotting are in broad agreement. This study represents a comprehensive gene-expression analysis of an MPNST-derived cell line and the first comprehensive global mRNA profile of nhSCs in culture. This study has identified ∼900 genes that are expressed abnormally in the T265 cell line and detected many genes not previously reported to be expressed in nhSCs. The results provide crucial information on the T265 cells that is essential for investigation using this cell line in experimental studies in neurofibromatosis type I (NF1), and important information on normal human Schwann cells that is applicable to a wide range of studies on Schwann cells in cell culture.

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22

Leimena, Milkha M., Michiel Wels, Roger S. Bongers, Eddy J. Smid, Erwin G. Zoetendal, and Michiel Kleerebezem. "Comparative Analysis of Lactobacillus plantarum WCFS1 Transcriptomes by Using DNA Microarray and Next-Generation Sequencing Technologies." Applied and Environmental Microbiology 78, no. 12 (April 6, 2012): 4141–48. http://dx.doi.org/10.1128/aem.00470-12.

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ABSTRACTRNA sequencing is starting to compete with the use of DNA microarrays for transcription analysis in eukaryotes as well as in prokaryotes. The application of RNA sequencing in prokaryotes requires additional steps in the RNA preparation procedure to increase the relative abundance of mRNA and cannot employ the poly(T)-primed approach in cDNA synthesis. In this study, we aimed to validate the use of RNA sequencing (direct cDNA sequencing and 3′-untranslated region [UTR] sequencing) usingLactobacillus plantarumWCFS1 as a model organism, employing its established microarray platform as a reference. A limited effect of mRNA enrichment on genome-wide transcript quantification was observed, and comparative transcriptome analyses were performed forL. plantarumWCFS1 grown in two different laboratory media. Microarray analyses and both RNA sequencing methods resulted in similar depths of analysis and generated similar fold-change ratios of differentially expressed genes. The highest overall correlation was found between microarray and direct cDNA sequencing-derived transcriptomes, while the 3′-UTR sequencing-derived transcriptome appeared to deviate the most. Overall, a high similarity between patterns of transcript abundance and fold-change levels of differentially expressed genes was detected by all three methods, indicating that the biological conclusions drawn from the transcriptome data were consistent among the three technologies.

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23

Nobis, William, Xiaoning Ren, Steven P. Suchyta, Thomas R. Suchyta, Adroaldo J. Zanella, and Paul M. Coussens. "Development of a porcine brain cDNA library, EST database, and microarray resource." Physiological Genomics 16, no. 1 (December 16, 2003): 153–59. http://dx.doi.org/10.1152/physiolgenomics.00099.2003.

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Recent developments in expressed sequence tag (EST) and cDNA microarray technology have had a dramatic impact on the ability of scientists to study responses of thousands of genes to internal and external stimuli. In neurobiology, studies of the human brain have been expanding rapidly by use of functional genomics techniques. To enhance these studies and allow use of a porcine brain model, a normalized porcine brain cDNA library (PBL) has been generated and used as a base for EST discovery and microarray generation. In this report, we discuss initial sequence analysis of 965 clones from this resource. Our data revealed that library normalization successfully reduced the number of clones representing highly abundant cDNA species and overall clone redundancy. Cluster analysis revealed over 800 unique cDNA species representing a redundancy rate for the normalized library of 6.9% compared with 29.4% before normalization. Sequence information, BLAST results, and TIGR cluster matches for these ESTs are publicly available via a web-accessible database ( http://nbfgc.msu.edu ). A cDNA microarray was created using 877 unique porcine brain EST amplicons spotted in triplicate on glass slides. This microarray was assessed by performing a series of experiments designed to test hybridization efficiency and false-positive rate. Our results indicate that the PBL cDNA microarray is a robust tool for studies of brain gene expression using swine as a model system.

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24

Sin, Cheol-Kyung, Chae-Woo Lee, Sun-Ae Yoo, Hyoun-Min Youn, Kyung-Jeon Jang, Choon-Ho Song, Chang-Beohm Ahn, and Cheol-Hong Kim. "Genes expression by using cDNA Microarray in Whallak-tang." Journal of Korean Institute of Herbal Acupuncture 11, no. 4 (December 30, 2008): 5–14. http://dx.doi.org/10.3831/kpi.2008.11.4.005.

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25

Thomson, S. A. M., E. Kennerly, N. Olby, J. R. Mickelson, D. E. Hoffmann, P. J. Dickinson, G. Gibson, and M. Breen. "Microarray Analysis of Differentially Expressed Genes of Primary Tumors in the Canine Central Nervous System." Veterinary Pathology 42, no. 5 (September 2005): 550–58. http://dx.doi.org/10.1354/vp.42-5-550.

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The pathophysiologic similarities of many human and canine cancers support the role of the domestic dog as a model for brain tumor research. Here we report the construction of a custom canine brain-specific cDNA microarray and the analysis of gene expression patterns of several different types of canine brain tumor The microarray contained 4000 clones from a canine brain specific cDNA library including 2161 clones that matched known genes or expressed sequence tags (ESTs) and 25 cancer-related genes. Our study included 16 brain tumors (seven meningiomas, five glial tumors, two ependymomas, and two choroid plexus papillomas) from a variety of different dog breeds. We identified several genes previously found to be differentially expressed in human brain tumors. This suggests that human and canine brain tumors share a common pathogenesis. In addition, we also found differentially expressed genes unique to either meningiomas or the glial tumors. This report represents the first global gene expression analysis of different types of canine brain tumors by cDNA microarrays and might aid in the identification of potential candidate genes involved in tumor formation and progression.

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26

Zhao, Baiteng, Robert A. Bowden, Salomon A. Stavchansky, and Phillip D. Bowman. "Human endothelial cell response to gram-negative lipopolysaccharide assessed with cDNA microarrays." American Journal of Physiology-Cell Physiology 281, no. 5 (November 1, 2001): C1587—C1595. http://dx.doi.org/10.1152/ajpcell.2001.281.5.c1587.

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To assess the feasibility of using cDNA microarrays to understand the response of endothelial cells to lipopolysaccharide (LPS) and to evaluate potentially beneficial agents in treatment of septic shock, human umbilical vein endothelial cells were exposed to Escherichia coli LPS for 1, 4, 7, 12, or 24 h. Total RNA was isolated and reverse-transcribed into33P-labeled cDNA probes that were hybridized to human GeneFilter microarrays containing ∼4,000 genes. The mRNA levels of several genes known to respond to LPS changed after stimulation. In addition, a number of genes not previously implicated in the response of endothelial cells to LPS also appeared to be altered in expression. Nuclear factor-κB (NF-κB) was shown to play an important role in regulating genes identified from the microarray studies. Pretreatment of endothelial cells with a specific NF-κB translocation inhibitor eliminated most of the alterations in gene expression. Quantitative RT-PCR results independently confirmed the microarray results for monocyte chemotactic protein-1 and interleukin-8, and enzyme-linked immunosorbent assays demonstrated that augmented transcription was followed by translation and secretion.

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27

Pang, Jin-Song, Meng-Yuan He, and Bao Liu. "Construction of the Seed-Coat cDNA Microarray and Screening of Differentially Expressed Genes in Barley." Acta Biochimica et Biophysica Sinica 36, no. 10 (October 1, 2004): 695–700. http://dx.doi.org/10.1093/abbs/36.10.695.

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Abstract Some barley mutants can synthesize neither anthocyanins nor proanthocyanidins in the seed coat, which is related to several genes in locus Ant13, but the exact model of action remains unknown. We used the cDNA microarray technology with barley transcription-deficient mutant (ant13-152) that does not synthesize proanthocyanidins as the tester, and its wild type genotype (Triumph) as the driver, to study this question. Six-thousand and forty-eight clones from the wild type Morex testa+pericarp cDNA library were amplified using PCR, and the DNA fragments were spotted on commercial amino-modified glass slide as microarray. The mRNAs from the developing seed coat (8–15 days) of both the mutant and the wild-type barley plants were isolated, and labeled respectively with Cy3-dUTP and Cy5-dUTP when reversely transcribed to cDNAs. The labeled cDNAs were used as probes, mixed at the same molar concentration, and hybridized with the DNA fragments on the slide. Seventy clones exhibiting marked differential expression (ratio>4) were identified from the microarray. All the 25 cDNA clones that showed an over-expression in wild type in comparison to the mutant ant13-152 were sequenced. It was found that most of these overexpressing clones were transcription/translation and hordein-associated genes. These results have laid a solid material basis for further elucidation of the metabolic pathway in proanthocyanidin synthesis in barley and likely other plants.

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28

Rahnenführer, J. "Image Analysis for cDNA Microarrays." Methods of Information in Medicine 44, no. 03 (2005): 405–7. http://dx.doi.org/10.1055/s-0038-1633984.

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Summary Objectives: We characterize typical problems encountered in microarray image analysis and present algorithmic approaches dealing with background estimation, spot identification and intensity extraction. Validation of the quality of resulting measurements is discussed. Methods: We describe sources for errors in microarray images and present algorithms that have been specifically developed to deal with such experimental imperfections. Results: For the image analysis of hybridization experiments, discriminating spot regions from a background is the most critical step. Spot shape detection algorithms, intensity histogram methods and hybrid approaches have been proposed. The correctness of final intensity estimates is difficult to verify. Nevertheless, the application of sophisticated algorithms provides a significant reduction of the possible information loss. Conclusions: The initial analysis step for array hybridization experiments is the estimation of expression intensities. The quality of this process is crucial for the validity of interpretations from subsequent analysis steps.

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29

Kikuchi, Shoshi. "Rice Microarray Project in Japan." Asia-Pacific Biotech News 06, no. 24 (November 25, 2002): 920–26. http://dx.doi.org/10.1142/s021903030200191x.

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30

Hegde, P., R. Qi, K. Abernathy, C. Gay, S. Dharap, R. Gaspard, J. E. Hughes, E. Snesrud, N. Lee, and J. Quackenbush. "A Concise Guide to cDNA Microarray Analysis." BioTechniques 29, no. 3 (September 2000): 548–62. http://dx.doi.org/10.2144/00293bi01.

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31

Wang, J., L. Hu, S. R. Hamilton, K. R. Coombes, and W. Zhang. "RNA Amplification Strategies for cDNA Microarray Experiments." BioTechniques 34, no. 2 (February 2003): 394–400. http://dx.doi.org/10.2144/03342mt04.

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32

Zhou Qifeng, Zhou Qingqing, Yang XiaoQing, and Hong Wencai. "cDNA Microarray Images Gridding Based on Projection." Journal of Convergence Information Technology 6, no. 3 (March 31, 2011): 188–94. http://dx.doi.org/10.4156/jcit.vol6.issue3.21.

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33

Bao, Zhang, Ma Wenli, Shi Rong, Li Ling, Guo Qiuye, and Zheng Wenling. "Re-use of a stripped cDNA microarray." British Journal of Biomedical Science 59, no. 2 (January 2002): 112–13. http://dx.doi.org/10.1080/09674845.2002.11783645.

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34

Gupta, Sudhir. "Balanced Factorial Designs for cDNA Microarray Experiments." Communications in Statistics - Theory and Methods 35, no. 8 (August 2006): 1469–76. http://dx.doi.org/10.1080/03610920600694587.

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35

Statham, Victoria, Michael Bittner, Jeffrey Trent, and Richard A. Morgan. "Applying cDNA microarray technology to gene therapy." Nature Genetics 23, S3 (November 1999): 75. http://dx.doi.org/10.1038/14407.

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36

Ji-Gang, Zhang, Zhang Qin, and Yin Zong-Jun. "The normalization method for cDNA microarray data." Chinese Journal of Agricultural Biotechnology 3, no. 3 (December 2006): 195–99. http://dx.doi.org/10.1079/cjb2006113.

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AbstractThe widely used processing method for cDNA microarray data involves background correction, log-ratio transformation and data normalization before the statistical testing can be done. Here we propose a method that avoids the log-transformation step in view of its drawbacks, but goes directly to normalization after background correction. This method could better estimate the ‘noise’ effect by utilizing the information more effectively. Simulation studies were carried out to compare the feasibility and efficiency of this approach for eliminating experimental ‘noise’ with the log-ratio approach. Results showed that our approach worked well and the method was more robust and powerful than the log-ratio approach.

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37

Xiang, Daoquan, Raju Datla, Fengling Li, Adrian Cutler, Meghna R. Malik, Joan E. Krochko, Nirmala Sharma, et al. "Development of a Brassica seed cDNA microarray." Genome 51, no. 3 (March 2008): 236–42. http://dx.doi.org/10.1139/g07-115.

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Brassica species represent several important crops including canola ( Brassica napus ). Understanding of genetic elements that contribute to seed-associated functions will impact future improvements in the canola crop. Brassica species share a very close taxonomic and molecular relationship with Arabidopsis thaliana. However, there are several subtle but distinct seed-associated agronomic characteristics that differ among the oil seed crop species. To address these, we have generated 67 535 ESTs predominately from Brassica seeds, analyzed these sequences, and identified 10 642 unigenes for the preparation of a targeted seed cDNA array. A set of 10 642 PCR primer pairs was designed and corresponding amplicons were produced for spotting, along with relevant controls. Critical quality control tests produced satisfactory results for use of this microarray in biological experiments. The microarray was also tested with specific RNA targets from embryos, germinating seeds, and leaf tissues. The hybridizations, signal intensities, and overall quality of these slides were consistent and reproducible. Additionally, there are 429 ESTs represented on the array that show no homology with any A. thaliana annotated gene or any gene in the Brassica genome databases or other plant databases; however, all of these probes hybridized to B. napus transcripts, indicating that the array also will be useful in defining expression patterns for genes so far unique to Brassica species.

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38

Nagarajan, R., and M. Upreti. "Correlation Statistics for cDNA Microarray Image Analysis." IEEE/ACM Transactions on Computational Biology and Bioinformatics 3, no. 3 (July 2006): 232–38. http://dx.doi.org/10.1109/tcbb.2006.30.

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39

Bergemann, Tracy L., and Lue Ping Zhao. "Signal Quality Measurements for cDNA Microarray Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 7, no. 2 (April 2010): 299–308. http://dx.doi.org/10.1109/tcbb.2008.72.

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40

Li, Z. "DCIS versus IBC: A cDNA microarray study." European Journal of Cancer 38, no. 11 (March 2002): S117. http://dx.doi.org/10.1016/s0959-8049(02)80380-x.

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41

Beneš, Vladimı́r, and Martina Muckenthaler. "Standardization of protocols in cDNA microarray analysis." Trends in Biochemical Sciences 28, no. 5 (May 2003): 244–49. http://dx.doi.org/10.1016/s0968-0004(03)00068-9.

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42

Li, Huiyu, Shenghua Jie, Ping Zou, and Guolin Zou. "cDNA Microarray Analysis of Chronic Myeloid Leukemia." International Journal of Hematology 75, no. 4 (May 2002): 388–93. http://dx.doi.org/10.1007/bf02982130.

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43

Battistutti, W. B., M. Manavi, K. Pischinger, E. Kubista, and K. Czerwenka. "cDNA microarray application on cervical epithelial cells." International Journal of Gynecology & Obstetrics 70 (2000): B150. http://dx.doi.org/10.1016/s0020-7292(00)83205-1.

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44

Liatsis, P., M. A. Nazarboland, J. Y. Goulermas, X. J. Zeng, and E. Milonidis. "Automating the processing of cDNA microarray images." International Journal of Intelligent Systems Technologies and Applications 5, no. 1/2 (2008): 115. http://dx.doi.org/10.1504/ijista.2008.018170.

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45

Banerjee, Tathagata, and Rahul Mukerjee. "Optimal factorial designs for cDNA microarray experiments." Annals of Applied Statistics 2, no. 1 (March 2008): 366–85. http://dx.doi.org/10.1214/07-aoas144.

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46

Sterrenburg, E. "A common reference for cDNA microarray hybridizations." Nucleic Acids Research 30, no. 21 (November 1, 2002): 116e—116. http://dx.doi.org/10.1093/nar/gnf115.

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47

Ares, Manuel. "Microarray Slide Hybridization Using Fluorescently Labeled cDNA." Cold Spring Harbor Protocols 2014, no. 1 (December 26, 2013): pdb.prot080135. http://dx.doi.org/10.1101/pdb.prot080135.

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48

Wilson, D. L., M. J. Buckley, C. A. Helliwell, and I. W. Wilson. "New normalization methods for cDNA microarray data." Bioinformatics 19, no. 11 (July 21, 2003): 1325–32. http://dx.doi.org/10.1093/bioinformatics/btg146.

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49

Ridgway, Gerard R., and Simon J. Godsill. "Bayesian Image Modeling of cDNA Microarray Spots." IEEE Signal Processing Letters 14, no. 10 (October 2007): 653–56. http://dx.doi.org/10.1109/lsp.2007.896378.

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50

Larese, Mónica G., Pablo M. Granitto, and Juan C. Gómez. "Spot defects detection in cDNA microarray images." Pattern Analysis and Applications 16, no. 3 (August 12, 2011): 307–19. http://dx.doi.org/10.1007/s10044-011-0234-x.

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