Visualisation of distributions and clusters using ViSOMs on gene expression data

Swapna Sarvesvaran, Hujun Yin

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


    Microarray datasets are often too large to visualise due to the high dimensionality. The self-organising map has been found useful to analyse massive complex datasets. It can be used for clustering, visualisation, and dimensionality reduction. However for visualisation purposes the SOM uses colouring schemes as a means of marking cluster boundaries on the map. The distribution of the data and the cluster structures are not faithfully portrayed. In this paper we applied the recently proposed visualisation induced Self-Organising Map (ViSOM), which directly preserves the inter-point distances of the input data on the map as well as the topology. The ViSOM algorithm regularizes the neurons so that the distances between them are proportional in both the data space and the map space. The results are similar to the Sammon mappings but with improved details on gene distributions and the flexibility to nonlinearity. The method is more suitable for larger datasets. © Springer-Verlag Berlin Heidelberg 2004.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    PublisherSpringer Nature
    Number of pages6
    Publication statusPublished - 2004
    EventIntelligent Data Engineering and Automated Learning (IDEAL’04) -
    Duration: 1 Jan 1824 → …


    ConferenceIntelligent Data Engineering and Automated Learning (IDEAL’04)
    Period1/01/24 → …


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