Sanger-driven MDSLocalize - a comparative study for genomic data

Multidimensional scaling (MDS) methods are designed to establish a one-to-one correspondence of input-output relationships. While the input may be given as high-dimensional data items or as adjacency matrix characterizing data relations, the output space is usually chosen as low-dimensional Euclidean, ready for visualization. MDSLocalize, an existing method, is reformulated in terms of Sanger's rule that replaces the original foundations of computationally costly singular value decomposition. The derived method is compared to the recently proposed high-throughput multi-dimensional scaling (HiT-MDS) and to the well-established XGvis system. For comparison, real-value gene expression data and corresponding DNA sequences, given as proximity data, are considered. Keywords. MDS, dimension reduction, proximity data, visualization.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Marc Strickert, Nese Sreenivasulu, Udo Seiffert
• Keywords: Costly Singular Value, ESANN 2006, ESANN 2007, Matrix Characterizing Data, Proximity Data