Statistics for Genomics (Spring 2012)

Lectures Links Datasets Class Info

Class outline:
Number Date Lecture Title Resources Description
Lecture 1 3/26/2013 Introduction
Lab 1 3/28/2013 Introduction to R [PDF],[R] We will cover an introduction to R, including data structure and management.
Lecture 2 4/2/2013 Statistics: Testing and Inference [PDF],[Video] Illustrative example: Differential Expression Analysis with Microarrays
Lab 2 4/4/2013 R: Testing and Inference [R] Differential Expression Analysis using R
Lecture 4A 4/9/2013 Microarray technologies [PDF] Background on microarrays technologies
Lecture 4B 4/9/2013 Sequencing technologies [PDF], [Video] Background on sequencing technologies
Lab 3 4/11/2013 Sequencing file formats and R [R] Exercises for sequencing data
Lecture 5A 4/16/2013 Distance and Clustering [PDF], [Video] Popular methods for clustering genomics experiments
Lecture 5B 4/16/2013 Useful Plots [PDF],[Video] Examples of plots using in genomics data...and not so useful ones
Lab 4 4/18/2013 Downloading Data [R] Downloading data from GEO and data cleaning
Lecture 6A 4/23/2013 Microarray Background Correction [PDF] How background signal influences microarray intensities
Lecture 6B 4/23/2013 Normalization [PDF],[Video] Normalization of expression arrays
Lab 5 4/25/2013 Annotating variants See 'Data' Below Tools for annotating variants
Lecture 7 4/30/2013 Advanced Differential Expression [PDF] ,[Video] More advanced methods for determining differential expression in high-throughput experiments
Lab 6 5/2/2013 Differential expression in R [R] Performing differential expression analysis in R using limma
Lab 7 5/7/2013 Analzying RNAseq in R [R] Using R to analyzed RNAseq data from alignment files



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