Enhancing Statistical Rigor In Genomic Data Science
7Jingyi Jessica Li (UCLA)
https://simons.berkeley.edu/talks/enhancing-statistical-rigor-genomic-data-science
Statistics in the Big Data Era
"The rapid development of genomics technologies has propelled fast advances in genomics data science. While new computational algorithms have been continuously developed to address cutting-edge biomedical questions, a critical but largely overlooked aspect is the statistical rigor. In this talk, I will introduce our recent work that aims to enhance the statistical rigor by addressing three issues: 1. large-scale feature screening (i.e., enrichment and differential analysis of high-throughput data) relying on ill-posed p-values; 2. double-dipping (i.e., statistical inference on biasedly altered data); 3. gaps between black-box generative models and statistical inference."