CSAMA'19
Statistics and Computing in Genome Scale Biology

Friday (Day5)

Lecture slides

• Assessing gene essentiality using pooled CRISPR screens - Katharina Imkeller
• Machine Learning Classification - Robert Gentleman
• Gene Set Enrichment - Martin Morgan
  - Worked Example
• Working with Large Data - Martin Morgan
• Big Data Strategy for transcriptional regulatory networks - Vince Carey

Single-cell Presentations

• SMART-seq 2
• Drop-seq and 10X Genomics Chromium 3’ sequencing
• 10x Genomics Chromium 5’ sequencing
• SPLit-Seq

Lab materials

• Interactive visualization of SummarizedExperiment objects with iSEE
• Efficient and Parallel R
• Machine Learning (Rscript) diabetes.csv
• Single-cell workflow. We recommend looking at Simple Single Cell workflow. This has many chapters so you can jump in to the task you're most interested in. You may not have the exact datasets used in these chapters, but should have the pbmc3k and pbmc4k datasets found in TENxPBMCData.

Thursday (Day4)

Lecture slides

• Visualization and graphics - Wolfgang Huber
• Mass spectrometry and proteomics - Laurent Gatto
• Metabolomics - Johannes Rainer
• Approaching mastery of distance, dimension reduction, clustering and classification in genomic applications - Vince Carey
• Bioconductor Packages For Cached File Management - Lori Sheperd

Lab materials

• Bioconductor tools for proteomics
• Preprocessing of untargeted metabolomics data
  • Zip file with R Markdown document, data, etc.
• BiocFileCache and Annotation Resources
  • Answers
• Single-cell group projects

Wednesday (Day3)

Lecture slides

• Experimental Design - Charlotte Sonneson
• eQTLs and sQTLs - Robert Gentleman
• Single-cell data analysis (Part 1) - Davide Risso
• Single-cell transcriptomics analysis for pedestrians - Simon Anders
  - This R markdown requires lots of tools & packages you weren't asked to install, so don't worry if you can't run this.
- You can see a rendered version at https://www.zmbh.uni-heidelberg.de/anders/div/sc_pedestrian.html

Tuesday (Day2)

Lecture slides

• RNA-seq quantification - Charlotte Sonneson
• Normalization and differential expression - Simon Anders (R Markdown) (R Script)
• Introduction to single-cell sequencing - Katharina Imkeller
• Annotation Resources - Part 1 - Lori Sheperd
• Annotation Resources - Part 2 - Johannes Rainer

Lab materials

• RNA-seq workflow
  (R Markdown document)

Evening Session

• Creating and Maintaining R Packages - Davide Risso
  - Example R File
• Efficient R Programming - Martin Morgan

Monday (Day1)

Lecture slides

• Introduction to R - Martin Morgan (Rmarkdown document)
• Linear models - Robert Gentleman
  
• Introduction to Bioconductor - Lori Sheperd (Rmarkdown document)
• Basics of hypothesis testing - Wolfgang Huber

Lab materials

• Introduction to R and Bioconductor
• Hypothesis Testing (Rscript)