Case study: RNA-seq data analysis

In the PH525 case studies, we will explore the data analysis of an experimental protocol in depth, using various open source software, including R and Bioconductor. We will explain how to start with raw data, and perform the standard processing and normalization steps to get to the point where one can investigate relevant biological questions. Throughout the case studies, we will make use of exploratory plots to get a general overview of the shape of the data and the result of the experiment.

We will learn the basic steps for analyzing an RNA-seq dataset for an organism with a well-defined genome and curated gene annotation. This will include read alignment, inferring presence of isoforms, counting reads in genes, exploring sample distances, differential expression analysis, and creating visual summaries.

This class was supported in part by NIH grant R25GM114818.

*Classes start April 27; all assignments due by 23 May 2015

This course is part of a larger set of 8 total courses:

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

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

PH525.3x: Advanced Statistics for the Life Sciences

PH525.4x: Introduction to Bioconductor

PH525.5x: Case study: RNA-seq data analysis

PH525.6x: Case study: Variant Discovery and Genotyping

PH525.7x: Case study: ChIP-seq data analysis

PH525.8x: Case study: DNA methylation data analysis

HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more here http://harvardx.harvard.edu/research-statement.

• Rafael Irizarry, Professor of Biostatistics Harvard T.H. Chan School of Public Health

• Michael Love, Postdoctoral Fellow Harvard T.H. Chan School of Public Health