tailieunhanh - Báo cáo y học: ":Identification of novel stem cell markers using gap analysis of gene expression data"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài:Identification of novel stem cell markers using gap analysis of gene expression data. | Open Access Method Identification of novel stem cell markers using gap analysis of gene expression data Paul M Krzyzanowski and Miguel A Andrade-Navarro Addresses Molecular Medicine Ottawa Health Research Institute 501 Smyth Road Ottawa Ontario K1H 8L6 Canada. Taculty of Medicine University of Ottawa 451 Smyth Road Ottawa Ontario K1H 8M5 Canada. Correspondence Paul M Krzyzanowski. Email pkrzyzanowski@ Published 17 September 2007 Received 4 May 2007 Genome Biology 2007 8 R193 doi gb-2007-8-9-rl93 Accepted I7 September 2007 The electronic version of this article is the complete one and can be found online at http 2007 8 9 R193 2007 Krzyzanowski and Andrade-Navarro. licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract 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. Background Gene expression microarrays allow thousands of transcripts in a cellular sample to be quantified simultaneously. For reviews of the technology and applications see the reports by Heller 1 and Sloughton 2 . Continuing improvements in microarray technology in terms of transcript density technical robustness and cost have led to widespread usage of arrays in experiments. The size of single studies has grown and can encompass the analysis of up to hundreds of arrays simultaneously 3-5