Cluster analysis and data visualization of large-scale gene expression data.
The discovery of any new gene requires an analysis of the expression context for that gene. Now that the cDNA and genomic sequencing projects are progressing at such a rapid rate, high throughput gene expression screening approaches are beginning to appear to take advantage of that data. We present a strategy for the analysis for large-scale quantitative gene expression measurement data from time course experiments. Our approach takes advantage of cluster analysis and graphical visualization methods to reveal correlated patterns of gene expression from time series data. The coherence of these patterns suggests an order that conforms to a notion of shared pathways and control processes that can be experimentally verified.
Institute for Computational Sciences and Informatics, George Mason University, Fairfax, VA 22030, USA. gmichael@osf1.gmu.edu
G S
GS
Michaels
D B
DB
Carr
M
M
Askenazi
S
S
Fuhrman
X
X
Wen
R
R
Somogyi
D001483:Base Sequence; D001921:Brain / Q000196:embryology / Q000254:growth & development / Q000378:metabolism*; D016000:Cluster Analysis; D003198:Computer Simulation; D018076:DNA, Complementary; D018507:Gene Expression Regulation, Developmental; D016045:Human Genome Project; D006801:Humans; D008957:Models, Genetic; D009419:Nerve Tissue Proteins / Q000235:genetics; D012333:RNA, Messenger / Q000235:genetics; D020133:Reverse Transcriptase Polymerase Chain Reaction / Q000379:methods; D012984:Software; D013116:Spinal Cord / Q000196:embryology / Q000254:growth & development / Q000378:metabolism*
D018076:DNA, Complementary; D009419:Nerve Tissue Proteins; D012333:RNA, Messenger