MIDAS: Microarray Data Analysis System
Before the intensity values measured in TIGR Spotfinder can be compared, normalization is necessary. This critical step can help compensate for variability between slides and fluorescent dyes, as well as other systematic sources of error, by appropriately adjusting the measured array intensities. Data filtering can reduce the dataset by removing poor or questionable data, in addition to data deemed uninteresting or irrelevant to the analysis. TIGR’s Microarray Data Analysis System, a Java application, provides users an intuitive interface to design analysis protocols combining one or more normalization and filtering steps. In this way, data from many individual hybridizations can be treated in a uniform and reproducible manner. MIDAS reads “.tav” files generated by TIGR Spotfinder or retrieved from the database via MADAM. Normalization modules include locally weighted linear regression (lowess; (Cleveland and Devlin, 1988; Yang et al., 2002)) and total intensity normalization. These can be linked with filters, including low-intensity cutoff, intensity-dependent Z-score cutoffs, and replicate consistency trimming, creating a highly customizable method for preparing expression data for subsequent comparison and analysis. Data analysis methods are constructed using an intuitive graphical scripting language and can be saved for application to other datasets. MIDAS provides scatterplots that illustrate the effects of each algorithm on the data. When the normalization and filtering steps are complete, MIDAS outputs the data in tav format.
| Module | Reference |
| Total Intensity | Quackenbush, J. Microarray data normalization and transformation. Nature Genetics. Vol.32 supplement pp496-501 (2002). |
| Locfit (LOWESS) | Quackenbush, J. Microarray data normalization and transformation. Nature Genetics. Vol.32 supplement pp496-501 (2002). Yang, I.V. et al. Within the fold: assessing differential expression measures and reproducibility in microarray assays. Genome Biol. 3, research0062.1-0062.12 (2002). Cleveland, W.S. Robust locally weighted regression and smoothing scatterplots. J. Amer. Stat. Assoc. 74, 829-836 (1979). |
| Iterative Linear Regression | Finkelstein, D., Gollub, J., etc. Iterative linear regression by sector: renormalization of cDNA microarray data and cluster analysis weighted by cross homology. (2002) |
| Iterative Log-Mean Centering | Microarray Gene Expression Data Analysis -- A beginner's Guide, ISBN 1-40510-682-4, page55-56 |
| Ratio Statistics | Chen, Y., Dougherty, E.R. & Bittner, M.L. Ratio-based decisions and the quantitative analysis of cDNA microarray images. J. Biomed. Optics 2, 364-374 (1997). |
| Standard Deviation Regularization | Quackenbush, J. Microarray data normalization and transformation. Nature Genetics. Vol.32 supplement pp496-501 (2002). Yang, Y.H. et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 30, e15 (2002). |
| Slice Analysis (z-score filtering) | Quackenbush, J. Microarray data normalization and transformation. Nature Genetics. Vol.32 supplement pp496-501 (2002). Yang, I.V. et al. Within the fold: assessing differential expression measures and reproducibility in microarray assays. Genome Biol. 3, research0062.1-0062.12 (2002). |
| Flip Dye Consistency Checking | Quackenbush, J. Microarray data normalization and transformation. Nature Genetics. Vol.32 supplement pp496-501 (2002). Yang, Y.H. et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 30, e15 (2002). |
| MAANOVA | Kerr, Martin and Churchill. Analysis of Variance for Gene Expression Microarrays. Journal of Computational Biology, 7:819-837(2000) |
| T-test | Pan, W. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 18: 546-554. (2002). |
| SAM | Tusher, V.G., R. Tibshirani and G. Chu. Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences USA 98: 5116-5121 (2001). |
Copyright © 2005, Dana-Farber Cancer Institute, 44 Binney St, Boston, MA, USA.
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Last modified: 12/06/2005
Latest Version
Download the Latest Version: v2.22
Or download the program source code: v2.22 source
MIDAS Manual
The Manual is a detailed description of the workings of MIDAS.
System Requirements
MIDAS v2.22 runs on the Windows, Mac OSX or Linux operating systems and requires Java v1.4.1 or higher.
Download Java v1.4.2
Training Documents
The powerpoint slides here contain the slides our group uses to teach courses on the program.
FAQ
The frequently asked questions page is very helpful in troubleshooting problems in installation and usage of MIDAS.
Support
If the above resources don't answer your questions, please contact midas@tigr.org for more help.