The advent of next-generation sequencing (NGS) brought the opportunity for researchers to investigate and assess biological variability at the genomic level in applications such as measuring changes of RNA abundance and measuring changes in DNA methylation. NGS also introduced severe biases and unwanted technical variability that can cause perceived differences between samples, irrespective of the biological variation. These differences are often due to changes in experimental conditions that are hard or impossible to control and confusing them with biological variability can lead to false discoveries in downstream analyses. Statistical methods play a key role in not only removing this technical variation from noisy genomics data, but also detecting and quantifying interesting biological variation. Here, I will discuss this important role statistics plays in the analysis of genomics data by giving several examples of biological challenges faced by researchers and data-driven solutions developed to answer those challenges.
Stephanie Hicks is a Postdoctoral Research Fellow under the direction of Rafael Irizarry in the Department of Biostatistics and Computational Biology at Dana-Farber Cancer Institute and the Department of Biostatistics at Harvard School of Public Health in Boston, MA. She received her B.S. in Mathematics from Louisiana State University and her M.A. and Ph.D. from the Department of Statistics at Rice University in Houston, TX under the direction of Marek Kimmel, Ph.D. (Departments of Statistics and Bioengineering, Rice University) and Sharon Plon, M.D., Ph.D. (Departments of Pediatrics and Molecular and Human Genetics, Baylor College of Medicine). Her research interests focus around developing statistical methods and tools in application for genomics and epigenomics data. Currently she is focused on methods for processing and analyzing DNA methylation and gene expression data using microarrays and next-generation sequencing.
Dept of Biostatistics and Computational Biology at Dana-Farber Cancer Institute and Dept of Biostatistics at Harvard School of Public Health in Boston