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.
Identification of novel stem cell markers using gap analysis of gene expression data
P. Krzyzanowski,Miguel Andrade
Published 2007 in Genome Biology
ABSTRACT
PUBLICATION RECORD
- Publication year
2007
- Venue
Genome Biology
- Publication date
2007-09-17
- Fields of study
Biology, Medicine
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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