Lattice based Clustering of Temporal Gene-Expression Matrices

Individuals show different cell classes when they are in the different stages of a disease, have different disease subtypes, or have different response to a treatment or environmental stress. It is important to identify the individuals’ cell classes, for example, to decide which disease subtype they have or how they will respond to a certain drug. In a temporal gene-expression matrix (TGEM) each row represents a time series of expression values of a gene. TGEMs of the same cell class should show similar gene-expression patterns. However, given a set of TGEMs, it can be difficult to classify matrices by cell classes. In this paper, we develop a tool called LABSTER (LAttice Based cluSTERing) to cluster gene-expression matrices by cell classes. Rather than treating each row or column as a vector, we create a Galois lattice for each matrix, which yields a natural distance function between gene expression matrices. Finally, we cluster based on these distances. A key advantage of our ...

• Type: Conference
• Year: 2007
• Where: SDM
• Authors: Yang Huang, Martin Farach-Colton
• Keywords: Cell Classes, Data Mining, Disease Subtype, Gene Expression, SDM 2007