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Statistical Inference and Modeling for High-throughput Experiments

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Overview

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.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

PH525.3x: Statistical Inference and Modeling for High-throughput Experiments

PH525.4x: High-Dimensional Data Analysis

PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays

PH525.6x: High-performance computing for reproducible genomics

PH525.7x: Case studies in functional genomics

This class was supported in part by NIH grant R25GM114818.

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Taught by

Michael Love and Rafael Irizarry

Reviews for edX's Statistical Inference and Modeling for High-throughput Experiments 4.7 Based on 3 reviews

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• 4 star 33%
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Did you take this course? Share your experience with other students.

• 1
Brandt P
4.0 2 years ago
by completed this course, spending 4 hours a week on it and found the course difficulty to be medium.
(Note I took these before the recent reorganization. I believe most of the material from the first few courses has remained relatively the same.)

This is the third course in the PH525 sequence offered by HarvardX. This course ended up being a bit of a surprise to me, as it was far more difficult than the previous two courses (PH525.1x and PH525.2x). Whereas previously, the lectures were at a higher level than the assignments, the assignments in this course were more difficult than the material covered in the lectures, and there was quite a bit less hand- holding compared to previ…
4 people found
Alun R
5.0 3 years ago
is taking this course right now.
Jinwook J
5.0 2 years ago
by completed this course.