Proceeding talk – Theme: Data.
Abstract
It is often the case in biological measurement data that results are given as a ranked list of quantities – e.g. gene differential expression (DE) genes. Existing tools that address statistical enrichment in ranked lists focus on identifying single factors associated with input ranks but do not address relationships between factors. We propose statistical and algorithmic approaches for selecting a sub-collection of factors that can be aggregated into one ranked list that is heuristically most associated with an input ranked list. We examine performance on simulated data and apply our approach to cancer datasets. DE association signal for individual miRNAs is too subtle to be detected but we find small sub-collections of miRNA that are statistically associated with gene DE in several types of cancer, suggesting miRNA cooperativity in driving disease processes. Many of our findings are consistent with known roles of individual miRNAs and others suggest previously unknown roles.
Authors
Dalia Cohn-Alperovich, CS Department, Technion-Israel Institute of Technology, Haifa, Israel; Microsoft R&D Center, Israel
Alona Rabner, Biology Department, Technion-Israel Institute of Technology, Haifa, Israel
Ilona Kifer, Microsoft R&D Center, Israel
Yael Mandel-Gutfreund, Biology Department, Technion-Israel Institute of Technology, Haifa, Israel
Zohar Yakhini, CS Department, Technion-Israel Institute of Technology, Haifa, Israel; Agilent Laboratories, Tel Aviv, Israel