Asymmetric latent semantic indexing for gene expression experiments visualization Articles uri icon

publication date

  • August 2016

issue

  • 4

volume

  • 14

international standard serial number (ISSN)

  • 0219-7200

electronic international standard serial number (EISSN)

  • 1757-6334

abstract

  • We propose a new method to visualize gene expression experiments inspired by the latent semantic indexing technique originally proposed in the textual analysis context. By using the correspondence word-gene document-experiment, we define an asymmetric similarity measure of association for genes that accounts for potential hierarchies in the data, the key to obtain meaningful gene mappings. We use the polar decomposition to obtain the sources of asymmetry of the similarity matrix, which are later combined with previous knowledge. Genetic classes of genes are identified by means of a mixture model applied in the genes latent space. We describe the steps of the procedure and we show its utility in the Human Cancer dataset.

keywords

  • Latent semantic indexing; Kernel; Gene expression; Asymmetric similarity; Approximation; Matrices; Network