HTS2018

Slides

  • Slide sets

Unix Shell

  • Duke HTS Summer Course 2018
  • Introduction to Unix and bash
  • Bash Scratchpad
  • The Unix Shell: File and Directory Management
  • The Unix Shell: Working with Text
  • The Unix Shell: Finding Stuff
  • The Unix Shell: Regular Expressions
  • The Unix Shell: Writing Shell Scripts
  • The Unix Shell: Exercises with Solutions
  • Using bash in bioinformatics
  • Bash Exercise (Solutions)
  • Bash Exercise 2: Working with a GTF file (Solutions)

Bioinformatics

  • Introduction to FASTQ Files
  • What do quality scores mean?
  • What do quality scores mean?
  • Quality Control
  • Trimming and Filtering a FASTQ
  • Trimming and Filtering
  • Download and Index Genome
  • Mapping Reads to a Reference Genome
  • Hands On Bioinformatics Exercise #1
  • Counting Reads
  • Working with Paired-Reads
  • Making Generic Commands
  • Making a Pipeline
  • Working with Loops
  • Working with Loops
  • Looping with Globs
  • Hands On Bioinformatics Exercise #2
  • Prepare Data
  • Downloading Everything
  • Using IGV
  • Run with shorter intron limit
  • Hands On Bioinformatics Exercise #3
  • Demultiplex a Raw FASTQ
  • Raw Data
  • Bash Functions
  • MCT Racks
  • Magnets
  • Printing

Analysis of Pilot Data

  • About the notebook
  • About the purpose of the analysis
  • Create directories
  • Check all the files we need
  • Set up environment
  • Read in the count data output from STAR
  • Arrange the results from the count files
  • Store the results
  • The End
  • Set up environment
  • Read in results
  • Check the label between metadata and mapping results
  • Construct gene count matrix for each library
  • Metadata
  • Store the results
  • Visualize the mapping results
  • The End
  • Set Environment
  • Import Data & Create DESeq from Count Data
  • Inspect object & Slots of an S4 class
  • Estimate Size Factors and Dispersion Parameters
  • Differential Expression Analysis
  • Converting/Normalizing Counts to “Expressions”
  • FPM
  • Regularized log transformation
  • Variance Stabilizing Transformation (vst) and mean-variance modelling at the observational level (voom)
  • The End
  • Pilot Study: Interaction analysis with DESeq2

R Graphics and Data Manipulation

  • Introduction to R
  • R Graphics
  • R Graphics
  • R Graphics
  • R Graphics Exercise (Solutions)
  • Using dplyr for data manipulation
  • Using tidyr to create tidy data sets
  • Working with multiple files

R Statistical Analysis

  • Simulations and Statistical Inference
  • Estimation and Hypothesis Testing
  • Unsupervised and Supervised Learning
  • Unsupervised Learning
  • Supervised Learning

Bioconductor

  • R libraries and Bioconductor

Post-course Resources

  • High Priority
  • Maintaining Personal Files
  • Accessing Course Material
  • Docker Containers
  • Select Duke Computing Resources
  • Assorted Notes on Approaches to Reproducible Analysis
HTS2018
  • Docs »
  • Search

Built with Sphinx using a theme provided by Read the Docs.