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Full-Text Articles in Medicine and Health Sciences
A Universal Reference Sample Derived From Clone Vector For Improved Detection Of Differential Gene Expression, Rishi L. Khan, Gregory E. Gonye, Guang Gao, James S. Schwaber
A Universal Reference Sample Derived From Clone Vector For Improved Detection Of Differential Gene Expression, Rishi L. Khan, Gregory E. Gonye, Guang Gao, James S. Schwaber
Department of Pathology, Anatomy, and Cell Biology Faculty Papers
Background
Using microarrays by co-hybridizing two samples labeled with different dyes enables differential gene expression measurements and comparisons across slides while controlling for within-slide variability. Typically one dye produces weaker signal intensities than the other often causing signals to be undetectable. In addition, undetectable spots represent a large problem for two-color microarray designs and most arrays contain at least 40% undetectable spots even when labeled with reference samples such as Stratagene's Universal Reference RNAsTM.
Results
We introduce a novel universal reference sample that produces strong signal for all spots on the array, increasing the average fraction of detectable …
The Molecular Portraits Of Breast Tumors Are Conserved Acress Microarray Platforms, Zhiyuan Hu, Cheng Fan, Daniel S. Oh, J. S. Marron, Xiaping He, Bahjat F. Qaqish, Chad Livasy, Lisa A. Carey, Evangeline Reynolds, Lynn Dressler, Andrew Nobel, Joel Parker, Matthew G. Ewend, Lynda R. Sawyer, Junyuan Wu, Yudong Liu, Rita Nanda, Maria Tretiakova, Alejandra Ruiz Orrico, Donna Dreher, Juan P. Palazzo, Laurent Perreard, Edward Nelson, Mary Mone, Heidi Hansen, Michael Mullins, John F. Quackenbush, Matthew J. Ellis, Olufunmilayo I. Olopade, Philip S. Bernard, Charles M. Perou
The Molecular Portraits Of Breast Tumors Are Conserved Acress Microarray Platforms, Zhiyuan Hu, Cheng Fan, Daniel S. Oh, J. S. Marron, Xiaping He, Bahjat F. Qaqish, Chad Livasy, Lisa A. Carey, Evangeline Reynolds, Lynn Dressler, Andrew Nobel, Joel Parker, Matthew G. Ewend, Lynda R. Sawyer, Junyuan Wu, Yudong Liu, Rita Nanda, Maria Tretiakova, Alejandra Ruiz Orrico, Donna Dreher, Juan P. Palazzo, Laurent Perreard, Edward Nelson, Mary Mone, Heidi Hansen, Michael Mullins, John F. Quackenbush, Matthew J. Ellis, Olufunmilayo I. Olopade, Philip S. Bernard, Charles M. Perou
Department of Pathology, Anatomy, and Cell Biology Faculty Papers
Background
Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list.
Results
A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes …