Integrating diverse genomic data using gene sets.
We introduce and evaluate data analysis methods to interpret simultaneous measurement of multiple genomic features made on the same biological samples. Our tools use gene sets to provide an interpretable common scale for diverse genomic information. We show we can detect genetic effects, although they may act through different mechanisms in different samples, and show we can discover and validate important disease-related gene sets that would not be discovered by analyzing each data type individually.
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02115, USA.
Svitlana
S
Tyekucheva
Luigi
L
Marchionni
Rachel
R
Karchin
Giovanni
G
Parmigiani
D019540:Area Under Curve; D002877:Chromosomes, Human / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology; D003198:Computer Simulation; D030541:Databases, Genetic; D018628:Gene Dosage; D015972:Gene Expression Regulation, Neoplastic; D052138:Genes, Neoplasm; D005813:Genes, Synthetic; D023281:Genomics / Q000379:methods*; D005909:Glioblastoma / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D006801:Humans; D008957:Models, Genetic; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012984:Software