Professional Course

Statistical Inference and Modeling for High-throughput Experiments

edX, Online
Length
4 weeks
Price
149 USD
Next course start
Start anytime See details
Delivery
Self-paced Online
Length
4 weeks
Price
149 USD
Next course start
Start anytime See details
Delivery
Self-paced Online
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Course description

Statistical Inference and Modeling for High-throughput Experiments

In this course you’ll 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 empirical bayes along with some examples of how these are used in practice. We provide R programming examples in a way that will help make the connection between concepts and implementation.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

Upcoming start dates

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  • Self-paced Online
  • Online
  • English

Who should attend?

Prerequisites

PH525.1x and PH525.2x or basic programming, intro to statistics, intro to linear algebra

Training content

  • Organizing high throughput data
  • Multiple comparison problem
  • Family Wide Error Rates
  • False 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

Course delivery details

This course is offered through Harvard University, a partner institute of EdX.

2-4 hours per week

Costs

  • Verified Track -$149
  • Audit Track - Free

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