Introduction
ArrayCyGHt is a web-based application tool for analysis and visualization of arrayCGH data. Full process of arrayCGH data analysis, from initial process with raw data to the final visualization of copy numer gain or loss, could be achieved on this arrayCyGHt system without any further software. The data processing steps are straightforward and streamlined in order to enhance feasibility. ArrayCyGHt is, therfore, particularly suited to the analysis of copy number aberrations in any kinds of data format and for any users with different level of bioinformatics knowledge.ArrayCyGHt has following features.
- Data import from tab-delimited text files
- Data preprocessing (filtering, normalization, log transformation, re-aligning the raw data to linear mapping order)
- Graphical representation of copy number gain or loss under user guided thresholds
- Direct linking the identified regions to public databse
- Flexible to other genomes besides human
Import of data
- Currently arrayCyGHt allows you to import data of single array.
Data file format
- File format is here.
- Users do not have to sort raw data in any way. ArrayCyGHt perform sorting according to array design and chromosomal location automatically.
Data import page
- You can import your pre-formatted data at the data import page.
- If you import two separated files, it takes some time to merge files at the arrayCyGHt server.
Loaded data
- This page shows the status of loaded files.
- When you revisit this page by clicking the back button of Microsoft Internet Explorer, it produces an error message. In spite of this error message, IExplorer users can still get the analysis result. Netscape and Firefox do not produce this error.
Parameters
- This step is implemented using limma package of R (Smyth, 2004). Detailed algorithm and help can be found in limma manual.
- Prior to data processing, information regarding array layout and normalization method should be determined by users.
- test/reference ratio: This defines how the signal ratio should be calculated (Green/Red or Red/Green).
- subtract background: Decide whether background is subtracted or not.
- array layout: Information about your array layout.
- normalization method: select normalization method.
- print-tip loess (Yang et al., 2001)
- The default normalization method.
- This method corrects print-tip and intensity-dependent bias.
- global loess
- For arrays which do not have print-tip groups.
- For small arrays with less than 150 spots per print-tip group.
- robustspline
- An empirical Bayes compromise between print-tip and global loess normalization
- none
- Do not normalize.
- print-tip loess (Yang et al., 2001)
Normalization
- This page shows resconstructed foreground and background images, MA plot and box plot of array.
- This page provides a link to download normalized data in tab-delimited text file. Downloaded data can be applied to other analysis tools.
Analysis
- This page shows a link to chromosome chart and an input box for threshold of clone selction.
- If clones are multiplicately spotted on an array, mean values of the clones are automatically calculated.
- Spots are aligned according to the mapping information (chromosome numbers, start position of clones).
- Thresholds can be determined by control hybridizations using normal genomic DNA. We recommend at least 5 control hybridizations. Two or three times the standard deviation of the log2 ratios from control hybridizations is usually determined to be the threshold (Leeuw et al., 2004; Aguirre et al., 2004; Veltman et al., 2002). If control hybridizations are not available, users have to set an arbitrary threshold.
- chromosome chart: plots of log2ratio vs. location of clones
- Because the number of chromosomes is determined by the data which are uploaded by users, this application is flexible to other genomes besides human.
- spot selction
- selection by user-guided threshold: you can select spots of which intensities are above or below the specified values. This selection provide links to Genome Browser of UCSC which shows annotation data of genome. These links are based on the mapping information, such as chromosome numbers, starting and end posiitons of clones.
- selection by user-guided threshold: you can select spots of which intensities are above or below the specified values. This selection provide links to Genome Browser of UCSC which shows annotation data of genome. These links are based on the mapping information, such as chromosome numbers, starting and end posiitons of clones.
Comparison study result
- Comparison of reliability and accuracy of arrayCyGHt with other applications using published arrayCGH data.
CASE 1
- Chung et al., 2004 Genome Research 14:188-196
- ArrayCyGHt analysis plot
- CGH-Explorer analysis plot
- aCGHsmooth analysis plot
- CGH-Miner analysis plot
CASE 2
- Chung et al., 2004 Genome Research 14:188-196
- ArrayCyGHt analysis plot
- CGH-Explorer analysis plot
- aCGHsmooth analysis plot
- CGH-Miner analysis plot
CASE 3
- Chung et al., 2004 Genome Research 14:188-196
- ArrayCyGHt analysis plot
- CGH-Explorer analysis plot
- aCGHsmooth analysis plot
- CGH-Miner analysis plot
References
- Aguirre, A.J., Brennan, C., Bailey, G., Sinha, R., Feng B., Leo, C., Zhang Y., Zhang J., Gans, J.D., Bardeesy, N., Cauwels, C., Cordon-Cardo, C., Redston, M.S., DePinho, R.A. and Chin L. (2004). High-resolution characterization of the pancreatic adenocarcinoma genome. Proc Natl Acad Sci U S A, 101(24):9067-72
- de Leeuw, R.J., Davies, J.J., Rosenwald, A., Bebb, G., Gascoyne, R.D., Dyer, M.J., Staudt, L.M., Martinez-Climent, J.A. and Lam, W.L. (2004). Comprehensive whole genome array CGH profiling of mantle cell lymphoma model genomes. Hum Mol Genet.,13(17):1827-37
- Smyth, G.K. (2004). Linear models and empirial Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3, No. 1, Article 3
- Veltman, J.A., Schoenmakers, E., Eussen, B.H., Janssen, I., Merkx, G., Cleef, B., Ravenswaaij, C.M., Brunner, H.G., Smeets, D. and Kessel, A.G. (2002). High-throughput analysis of subtelomeric chromosome rearrangements by use of array-based comparative genomic hybridization. Am J Hum Genet, 70:1269-76
- Yang, Y.H., Dudoit, S., Luu, P. and Speed, T.P. (2001). Normalization for cDNA microarray data. In Microarray: Optical Technologies and Informatics, Bittner, M.L., Chen, Y., Dorsel, A.N. and Dougherty, E.R. (eds), Proceeding of SPIE, Vol 4266, pages 141-152