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# Statistical Inference and Modeling for High-throughput Experiments

### 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 two Professional Certificates and are self-paced:

Data Analysis for Life Sciences:

• 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

Genomics Data Analysis:

• PH525.5x: Introduction to Bioconductor
• PH525.6x: Case Studies in Functional Genomics

This class was supported in part by NIH grant R25GM114818.

### Taught by

Michael Love and Rafael Irizarry

## Reviews

4.5 rating, based on 4 Class Central reviews

Start your review of Statistical Inference and Modeling for High-throughput Experiments

• Brandt Pence

Brandt Pence 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...
• Anonymous
Taking the course now; I had another attempt a few months ago, I cancelled because my knowledge in statistics was low and also my problems with R version installation did not allow me to complete the exercises (had to upgrade my OS so that the new R version...
• Alun Ap Rhisiart

Alun Ap Rhisiart is taking this course right now.

• Jinwook

Jinwook completed this course.

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