Shady Grove, 7E032/034
Qunhua Li, Ph.D.
Associate Professor, Department of Statistics
Pennsylvania State University
Reproducibility is essential for the reliability of scientific discoveries. It is especially important for findings from high-throughput biological experiments, as these experiments are known to be noisy and their accuracy is difficult to assess due to lack of ground truth.
In this talk, the author presents several statistical methods that were developed for addressing the reproducibility issues in high-throughput experiments. This includes how to evaluate reproducibility of replicate experiments, how to assess reproducibility of findings from these experiments, and how to use reproducibility as a criterion to choose optimal experimental parameters for constructing reliable high-throughput workflow. The author discusses these methods in the context of ChIP-seq, ChIP-exo and Hi-C data and demonstrates the importance of modeling special structures in the data to avoid confounding effects in reproducibility assessment.
**The mission of the Biostatistics Branch (BB) is to be an outstanding biostatistics unit that can contribute to the understanding of cancer etiology and to improve public health by the development and application of quantitative methods. The BB Investigators develop statistical methods and data resources to strengthen observational studies, intervention trials, and laboratory investigations of cancer.**