Jingyi Jessica Li | Advancing Statistical Genomics
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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?