Deterministic projection by growing cell structure networks for visualization of high-dimensionality datasets.
Recent advances in clinical proteomics data acquisition have led to the generation of datasets of high complexity and dimensionality. We present here a visualization method for high-dimensionality datasets that makes use of neuronal vectors of a trained growing cell structure (GCS) network for the projection of data points onto two dimensions. The use of a GCS network enables the generation of the projection matrix deterministically rather than randomly as in random projection. Three datasets were used to benchmark the performance and to demonstrate the use of this deterministic projection approach in real-life scientific applications. Comparisons are made to an existing self-organizing map projection method and random projection. The results suggest that deterministic projection outperforms existing methods and is suitable for the visualization of datasets of very high dimensionality.
Wong
Jason W H
JW
Physical and Theoretical Chemistry Laboratory, Department of Chemistry, Oxford University, South Parks Road, Oxford OX1 3QZ, UK. jason.wong@chem.ox.ac.uk
Cartwright
Hugh M
HM
D000465:Algorithms; D002468:Cell Physiological Phenomena; D049109:Cell Proliferation; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome