Philosophy of Data Science | Jingyi Jessica Li | Advancing Statistical Genomics

Jingyi Jessica Li | Advancing Statistical Genomics Watch it on….    YouTube       Podbean Jingyi Jessica Li (UCLA) describes common statistical pitfalls in genomic data analysis & the statistical reasoning required to correct these mistakes. Common themes throughout include: Hypothesis-driven science & critical scientific reasoning over data p-values and non-sensical null hypotheses/distributions the value of appearing statistically rigorous researchers cutting intellectual corners & digging themselves into local minima   Episode Topics 0:00 A major advancement in genomic data leads to new statistical techniques 2:15 Hypothesis-driven science & hypothesis-free data analysis 2:55 A ChIP Seq Example 8:00 Misformulation of sampling variability 16:55 A false analogy: the permutation test 19:03 Losing my p-value religion: the value of statistical packaging 24:30 The Clipper Framework for false discovery rate control 31:50 Non-parametric developments 37:55 Inferred covariates 46:00 PseudotimeDE: inferences of differential gene expression along cell pseudotime 47:10 Selective inference 49:25 What biological/physiological data will be incorporated in the future? 52:30 Statistics, computer science, data science, ML, biology 57:05 Machine learning and prediction 1:01:30 Sophisticated models vs sophisticated research 1:07:45 Peer review in science 1:13:05 Hypothesis-driven science vs cutting intellectual corners 1:18:12 What topic should the statistics community debate?