A Graphical Users Interface to Normalize Microarray Data

Microarray technology is becoming an essential tool in functional genomics. The possibility of monitoring the expression level of thousands of genes simultaneously, as the response to a particular biological condition, gives to the biologists the chance to widen the aims of their experiments and opens a door to the understanding of cellular transcription processes. In order to extract valuable information from the big amount of data that microarrays experiments generate, suitable and powerful statistical and computational methods are required. An example of the eort of statisticians and computer scientists is the release of the first Bioconductor software and the increasing number of functions for microarray data analysis implemented

[1]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[2]  Martin Vingron,et al.  Variance stabilization applied to microarray data calibration and to the quantification of differential expression , 2002, ISMB.

[3]  S. Dudoit,et al.  Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. , 2002, Nucleic acids research.

[4]  W. Cleveland.  Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .

[5]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Jin Hyun Park,et al.  Normalization for cDNA Microarray Data on the oral cancer , 2002 .

[7]  A. Schulze,et al.  Navigating gene expression using microarrays — a technology review , 2001, Nature Cell Biology.

[8]  Antony Unwin.  R objects, two interfaces! (R objects to interfaces?) , 2001 .

[9]  Terence P. Speed,et al.  Normalization for cDNA microarry data , 2001, SPIE BiOS.

[10]  Jonathan Pevsner,et al.  Local mean normalization of microarray element signal intensities across an array surface: quality control and correction of spatially systematic artifacts. , 2002, BioTechniques.

[11]  P. Brown,et al.  DNA arrays for analysis of gene expression. , 1999, Methods in enzymology.

[12]  Gary A. Churchill,et al.  Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..

[13]  Charles L. Kooperberg,et al.  Improved Background Correction for Spotted DNA Microarrays , 2002, J. Comput. Biol..

[14]  G. Churchill,et al.  Experimental design for gene expression microarrays. , 2001, Biostatistics.

[15]  T. Kepler,et al.  Normalization and analysis of DNA microarray data by self-consistency and local regression , 2002, Genome Biology.

[16]  Partha S. Vasisht.  Computational Analysis of Microarray Data , 2003 .