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 in analyzing DNA methylation data, including reading the raw data, normalization, and finding regions of differential methylation across multiple samples.
This class was supported in part by NIH grant R25GM114818.
This course is part of a larger set of 8 total courses running Self-Paced through September 15th, 2015:
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
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