Highlighting Differential Gene Expression between Two Condition Microarrays through Multidimensional Scaling Comparison of LesihmaniaInfantum Genomic Data Similarity Matrices

  • Víctor Andrés Vera-Ruiz
  • Liliana López-Kleine
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 93)


Classical methods for differential gene expression between two microarray conditions often fail to detect interesting and important differences, because these appear too little compared to the expected variability. Data fusion has proved to highlight weak differences as it allows identifying genes associated to different biological conditions. However, data fusion often leads to a new representation of data, as for example in similarity matrices. Measuring distances between similarities for each gene is not a straightforward task, and methods for this would be useful in order to find potential genes for further research. Here, we present two different kernel methods based on multidimensional scaling and principal component analysis to measure distances between genes through an example on L. infantum microarrays comparing promastigote and amastigote stages. These methods are flexible and can be applied to any organism for which microarray and other genomic data is available.


microarray data differentially expressed genes kernel PCA Multidimensional scaling Laplacian matrix 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Víctor Andrés Vera-Ruiz
    • 1
  • Liliana López-Kleine
    • 1
  1. 1.Statistics DepartmentUniversidad Nacional de Colombia (Sede Bogotá)Colombia

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