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


Chapter 11 - Introduction to Bioconductor

Chapter 12 - Genomic Annotation with Bioconductor

Chapter 13 - Genome-scale hypothesis testing with Bioconductor

Chapter 14 - Visualization of genome scale data

Chapter 15: Pursuing scalability in genomic analysis: parallelism and out-of-memory data

Chapter 16: Multi-omic data integration

Chapter 17: Fostering reproducible genome-scale analysis


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

Acknowledgments