Spectral Map Analysis of Microarray Data

Specific aspects of analyzing microarray data are described, which include size and shape of gene expression profiles, taking logarithms, and the biplot graphic for visualizing associations between genes and cells. Three methods of factor analysis are presented that find application to microarray data: principal component analysis, correspondence analysis, and spectral map analysis. It is shown that these three methods differ only in the way the data are preprocessed and that spectral map analysis has advantages over the other two methods. Two graphical devices that are helpful in exploring and interpreting microarray data are also described.

[1]  K. Gabriel,et al.  The biplot graphic display of matrices with application to principal component analysis , 1971 .

[2]  C. Spearman.  General intelligence Objectively Determined and Measured , 1904 .

[3]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[4]  Scott Chapman,et al.  Using biplots to interpret gene expression patterns in plants , 2002, Bioinform..

[5]  Erling B. Andersen,et al.  Analysis of Contingency Tables , 1987 .

[6]  Dhammika Amaratunga,et al.  Exploration and Analysis of DNA Microarray and Protein Array Data , 2003, Wiley series in probability and statistics.

[7]  Paul J. Lewi,et al.  Spectral mapping, a personal and historical account of an adventure in multivariate data analysis☆ , 2005 .

[8]  Susan R. Wilson,et al.  Use of Principal Component Analysis and the GE‐Biplot for the Graphical Exploration of Gene Expression Data , 2005, Biometrics.

[9]  Gilbert Saporta,et al.  L'analyse des données , 1981 .

[10]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[11]  Raymond J Carroll,et al.  DNA Microarray Experiments: Biological and Technological Aspects , 2002, Biometrics.

[12]  Lewi Pj,et al.  Spectral mapping, a technique for classifying biological activity profiles of chemical compounds. , 1976 .

[13]  Axel V. Nielsen,et al.  Contributions to the History of the Hertzsprung‐Russell Diagram , 1964 .

[14]  Geert Molenberghs,et al.  Graphical Exploration of Gene Expression Data: A Comparative Study of Three Multivariate Methods , 2003, Biometrics.

[15]  R. Clarke,et al.  Theory and Applications of Correspondence Analysis , 1985 .

[16]  Michael Greenacre,et al.  Distributional Equivalence and Subcompositional Coherence in the Analysis of Contingency Tables, Ratio-Scale Measurements and Compositional Data , 2005 .

[17]  P. Jolicoeur,et al.  Size and shape variation in the painted turtle. A principal component analysis. , 1960, Growth.

[18]  René Descartes,et al.  Discours de la méthode : pour bien conduire sa raison et chercher la vérité dans les sciences , 1950 .