This is an accompanying webpage to the paper: Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer Safiye Celik, Benjamin A Logsdon, Stephanie Battle, Charles W Drescher, Mara Rendi, R David Hawkins, Su-In Lee Abstract Patterns in expression data conserved across multiple independent disease studies are likely to
represent important molecular events underlying the disease. We present the INSPIRE method
to infer modules of co-expressed genes and the dependencies among the modules
from multiple expression datasets that may contain different sets of genes. We
show that INSPIRE infers more accurate models than existing methods to extract
low-dimensional representation of expression data. We demonstrate that applying INSPIRE to nine ovarian
cancer datasets leads to a new marker and potential driver of tumor-associated
stroma, HOPX, followed by
experimental validation. |