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Statistical Inference and Modeling for High-throughput Experiments
Harvard University via edX
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Overview
In this course youll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. We provide several examples of how these concepts are applied in next generation sequencing and microarray data. Finally, we will discuss hierarchical models and...
Syllabus
Organizing high throughput data
Multiple comparison problem
- Family Wide Error RatesFalse Discovery Rate
- Error Rate Control procedures
- Bonferroni Correction
- q-values
- Statistical Modeling
- Hierarchical Models and the basics of Bayesian Statistics
- Exploratory Data Analysis for High throughput data
Harvard University via edX
4 hours
$219.00 Certificate Available
English
On-Demand
Intermediate
Instructor
HarvardX
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