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

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?