Identification of novel stem cell markers using gap analysis of gene expression data.
We describe a method for detecting marker genes in large heterogeneous collections of gene expression data. Markers are identified and characterized by the existence of demarcations in their expression values across the whole dataset, which suggest the presence of groupings of samples. We apply this method to DNA microarray data generated from 83 mouse stem cell related samples and describe 426 selected markers associated with differentiation to establish principles of stem cell evolution.
Krzyzanowski
Paul M
PM
Molecular Medicine, Ottawa Health Research Institute, 501 Smyth Road, Ottawa, Ontario, K1H 8L6, Canada. pkrzyzanowski@ohri.ca
Andrade-Navarro
Miguel A
MA
D000465:Algorithms; D000818:Animals; D002454:Cell Differentiation; D019295:Computational Biology / Q000379:methods; D003627:Data Interpretation, Statistical; D053595:Embryonic Stem Cells / Q000378:metabolism*; D004777:Environment; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D005819:Genetic Markers; D051379:Mice; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D010802:Phylogeny; D013234:Stem Cells / Q000166:cytology
D005819:Genetic Markers