TM4: a free, open-source system for microarray data management and analysis
AI Saeed, V Sharov, J White, J Li, W Liang… - …, 2003 - Future Science
Biotechniques, 2003•Future Science
BioTechniques 34: 374-378 (February 2003) supported, MADAM is being adapted to read
and write MAGE-ML, the XML data exchange format being developed by an international
consortium of leading public databases and microarray research centers. A MAGEML
version of MADAM should be available by the end of this year and will facilitate submission
of microarray data to public repositories such as Array Express and GEO. Image analysis is
a crucial step in the microarray process. TIGR Spotfinder (Figure 1B) was designed for the …
and write MAGE-ML, the XML data exchange format being developed by an international
consortium of leading public databases and microarray research centers. A MAGEML
version of MADAM should be available by the end of this year and will facilitate submission
of microarray data to public repositories such as Array Express and GEO. Image analysis is
a crucial step in the microarray process. TIGR Spotfinder (Figure 1B) was designed for the …
BioTechniques 34: 374-378 (February 2003) supported, MADAM is being adapted to read and write MAGE-ML, the XML data exchange format being developed by an international consortium of leading public databases and microarray research centers. A MAGEML version of MADAM should be available by the end of this year and will facilitate submission of microarray data to public repositories such as Array Express and GEO. Image analysis is a crucial step in the microarray process. TIGR Spotfinder (Figure 1B) was designed for the rapid, reproducible, and computer-aided analysis of microarray images and the quantification of gene expression. TIGR Spotfinder reads paired 16-bit TIFF image files generated by most microarray scanners. Semi-automatic grid construction defines the areas of the slide where spots are expected. Automatic and manual grid adjustments help to ensure that each rectangular grid cell is centered on a spot. A histogram segmentation method defines the boundaries between each spot and the surrounding local background. Spot intensities are calculated as an integral of non-saturated pixels, although other options including spot medians are available. Local background is subtracted from each intensity value. These calculated intensities, along with each spot’s position on the array, spot area, background values, and qualitycontrol flags, are written to a TIGR ArrayViewer (“. tav”) file format, a Microsoft ExcelŽ workbook, or the database. Reusable grid geometry files and automatic grid adjustment allow the user to analyze large quantities of images in a consistent and efficient manner. To complement the automated methods, particularly in noisy areas of the slide, the user may manually identify or discard spots. Quality-control views allow the user to assess systematic biases in the data. TIGR Spotfinder is written in C++.
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
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