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A package based on a normalization method that estimates both biological and nonrelevant effects in an alternate, iterative manner, accurately eliminating irrelevant effects. ITALICS uses a normalization algorithm for Affymetrix SNP arrays. This method was designed for the normalization and analysis of DNA copy number and significantly outperformed other methods, such as CNAT 3.0, CNAT 4.0, CNAG and GIM, in terms of SNR and can also be used to correct for experimental artifacts due to spatial effects. This method was validated by QPCR and accurately detected the breakpoints in genomic profiles. It could therefore be used to improve the characterization of samples in genomic studies.

MANOR / CGH Micro-Array NORmalization

A package based on an automatic method for the spatial normalization of array-CGH data. This method makes it possible to delineate and to eliminate and/or correct areas affected by spatial bias. It is based on the combination of a spatial segmentation algorithm called NEM (Neighborhood Expectation Maximization) and spatial trend estimation. MANOR is designed for the spatial normalization of BAC CGH-array data, preventing the misinterpretation of experimental artifacts as biologically relevant outliers in the genomic profile. This method was developed for array-CGH experiments, and gave very good results. However, it can be applied to any microarray experiment having the same types of spatial effect.


A method for normalizing allelic estimates of one tumor sample based on estimates from a single matched normal. TumorBoost increases the power to detect somatic copy-number events (including copy-neutral LOH) in the tumor from allelic signals of Affymetrix, Illumina and alike origins. Because each SNP is normalized separately, TumorBoost does not require prior knowledge about copy number change points or copy number regions, and its complexity is linear in the number of SNPs.