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Self-Organizing Map and Other Clustering Methods in Transcriptomics

  • Xuhua Xia
Chapter

Abstract

Self-organizing map (SOM) is an artificial neural network algorithm, having been used frequently with transcriptomic data analysis, in particular for clustering co-expressed genes as a basis to infer co-regulated genes. It can be applied to any set of objects as long as a distance function can be defined between objects. SOM is numerically illustrated together with a simple UPGMA method to contrast between the two. A less known application of SOM is in discovering heterogeneous motifs present in a set of sequences, making it more general than Gibbs sampler in de novo motif discovery. These two approaches, one with a (gene × expression) matrix as input and the other with a set of sequences as input (where each sequence may contain multiple but heterogeneous protein-binding sites), are illustrated.

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Copyright information

© Springer Science+Business Media LLC 2018

Authors and Affiliations

  • Xuhua Xia
    • 1
  1. 1.University of Ottawa CAREG and Biology DepartmentOttawaCanada

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