Chapter 0 - Introduction

Chapter 1 - Inference

Chapter 2 - Exploratory Data Analysis

Chapter 3 - Robust Statistics

Chapter 4 - Matrix Algebra

Chapter 5 - Linear Models

Chapter 6 - Inference for High-Dimensional Data

Chapter 7 - Statistical Modeling

Chapter 8 - Distance and Dimension Reduction

Chapter 9 - Practical Machine Learning

Chapter 10 - Batch Effects

525.5x: Introduction to Bioconductor: Annotation and analysis

Setup and basics on biological background (Week 1)

Focus on data structure and management (Week 2)

Focus on genomic ranges (Week 3a)

Focus on genomic annotation (Week 3b)

Testing genome-scale hypotheses (Week 4)

525.6x: High-performance computing for reproducible genomics with Bioconductor

Visualization of genome scale data (Week 1)

Scalable genomic analysis (Week 2)

Multi-omic data integration (Week 3)

Fostering reproducible genome-scale analysis (Week 4)

Legacy material from 2015 Introduction to Bioconductor

RNA-seq data analysis

Variant Discovery and Genotyping

ChIP-seq data analysis

DNA methylation data analysis

Footnotes for all lectures