Microarray Image Processing and Quality Control

Image processing is an important stage of every microarray experiment. Reliability of this stage strongly influences the results of data analysis performed on extracted gene expressions. Multiple methods related to array recognition, spot segmentation and measurement extraction have emerged in this area over past several years. Currently there are various commercial and freeware packages available, which perform microarray image analysis. This paper attempts to review microarray image analysis as a whole and to make some experimental comparison of several computational schemes for signal segmentation and measurement extraction. Also we provide a detailed discussion of automated image quality control for use with microarray images.

[1]  Andreas Rytz,et al.  The limit fold change model: A practical approach for selecting differentially expressed genes from microarray data , 2002, BMC Bioinformatics.

[2]  G. A. Whitmore,et al.  Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Jörg Rahnenführer,et al.  Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering , 2002, Bioinform..

[4]  Stephen R Quake,et al.  Significance and statistical errors in the analysis of DNA microarray data , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[5]  S. Hilsenbeck,et al.  Statistical analysis of array expression data as applied to the problem of tamoxifen resistance. , 1999, Journal of the National Cancer Institute.

[6]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[7]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[8]  M. Oh,et al.  Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. , 2001, Nucleic acids research.

[9]  M. Eisen,et al.  Gene expression informatics —it's all in your mine , 1999, Nature Genetics.

[10]  Soheil Shams,et al.  Information processing issues and solutions associated with microarray technology , 2000 .

[11]  Laurie J. Heyer,et al.  Exploring expression data: identification and analysis of coexpressed genes. , 1999, Genome research.

[12]  Y. Chen,et al.  Ratio-based decisions and the quantitative analysis of cDNA microarray images. , 1997, Journal of biomedical optics.

[13]  Terence P. Speed,et al.  Comparison of Methods for Image Analysis on cDNA Microarray Data , 2002 .

[14]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..