A python module to normalize microarray data by the quantile adjustment method - Baber, Ibrahima; Tamby, Jean Philippe; Manoukis, Nicholas C.; Sangaré, Djibril; Doumbia, Seydou; Traoré, Sekou F.; Maiga, Mohamed S.; Dembélé, Doulaye (Infection, genetics and evolution, 2011)
▶ Microarray technology is fundamental to current research on drug treatments. ▶ We have implemented a Python module which can normalize two-color microarray data and can be utilized via a custom HTML front end. ▶ The source code of this software is available to the research community for modification and customization. Microarray technology is widely used for gene expression research targeting the development of new drug treatments. In the case of a two-color microarray, the process starts with labeling DNA samples with fluorescent markers (cyanine 635 or Cy5 and cyanine 532 or Cy3), then mixing and hybridizing them on a chemically treated glass printed with probes, or fragments of genes. The level of hybridization between a strand of labeled DNA and a probe present on the array is measured by scanning the fluorescence of spots in order to quantify the expression based on the quality and number of pixels for each spot. The intensity data generated from these scans are subject to errors due to differences in fluorescence efficiency between Cy5 and Cy3, as well as variation in human handling and quality of the sample. Consequently, data have to be normalized to correct for variations which are not related to the biological phenomena under investigation. Among many existing normalization procedures, we have implemented the quantile adjustment method using the python computer language, and produced a module which can be run via an HTML dynamic form. This module is composed of different functions for data files reading, intensity and ratio computations and visualization. The current version of the HTML form allows the user to visualize the data before and after normalization. It also gives the option to subtract background noise before normalizing the data. The output results of this module are in agreement with the results of other normalization tools.