The Model-Based Study of the Effectiveness of Reporting Lists of Small Feature Sets Using RNA-Seq Data

In this study, we compare the performance of ranking feature sets derived from a model of RNA-Seq data with that of a multivariate normal model of gene concentrations using 3 measures: (1) ranking power, (2) length of extensions, and (3) Bayes features. This is the model-based study to examine the effectiveness of reporting lists of small feature sets using RNA-Seq data and the effects of different model parameters and error estimators. The results demonstrate that the general trends of the parameter effects on the ranking power of the underlying gene concentrations are preserved in the RNA-Seq data, whereas the power of finding a good feature set becomes weaker when gene concentrations are transformed by the sequencing machine.

Source: Cancer Informatics - June 12, 2017 Category: Cancer & Oncology Authors: Eunji Kim Ivan Ivanov Jianping Hua Johanna W Lampe Meredith AJ Hullar Robert S Chapkin Edward R Dougherty Source Type: research