BioMed Central Genomics
BackgroundLarge collections of paraffin-embedded tissue represent a rich resource to test hypotheses based on gene expression patterns; however, measurement of genome-wide expression is cost-prohibitive on a large scale. Using the known expression correlation structure within a given disease type (in this case, high grade serous ovarian cancer; HGSC), we sought to identify reduced sets of directly measured (DM) genes which could accurately predict the expression of a maximized number of unmeasured genes.
Dartmouth Digital Commons Citation
Rudd, James; Zelaya, René A.; Demidenko, Eugene; Goode, Ellen L.; Greene, Casey S. Greene S.; and Doherty, Jennifer A., "Leveraging Global Gene Expression Patterns to Predict Expression of Unmeasured Genes" (2015). Dartmouth Scholarship. 600.