STATISTICS COLLOQUIUM
W. Evan Johnson, PhD
Associate Professor of Medicine & Biostatistics
Associate Chief, Division of Computational Biomedicine
Boston University School of Medicine
Addressing unwanted heterogeneity in genomic data:
applications in RNA-sequencing and prediction
Abstract
The presence of batch effects in genomic data may unfavorably impact a broad set of applications, including differential expression detection and the accuracy of genomic prediction models. In practice, batch effects are usually addressed by specifically designed software such as ComBat, which merge the data from different batches, estimate batch effects and remove them from the data. However, these established methods are not sufficient to address all challenges and scenarios in batch effect adjustment, including RNA-sequencing, single RNA-sequencing, prediction problems, and when there is a need for one batch to serve as a reference (e.g. biomarker training). This presentation will discuss these applications and present novel methods for improved handling of batch effects in these contexts. Software tools and solutions will also be presented, and methods/tools will be illustrated using examples from tuberculosis and cancer research.
DATE: Wednesday, November 13, 2019
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 344
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu