Duke NGS Course (Summer 2015)¶

Assigned Reading
- Biology
- Statistics
- Bioinformatics
Lecture notes
- Biology
- Experimental Design
- Statistical Inference
- Bioinformatics
How to Claim and Access Your Virtual Machine
- What is a Virtual Machine?
- How to Claim a Virtual Machine
- How to Access the VM
Installing R, RStudio and IPython notebook with the R kernel
- Install R
- Install RStudio
- Install Jupyter
- Install the R kernel for Jupyter
- Installing R Graphics packages
- Check
Basic R in the Jupyter Notebook and RStudio
- The Notebook
- Rstudio
Introduction to R
- Loading libraries
- Installing libraries
- R as simple calculator
- Printing results
- Getting help
- Assigning to variables
- Creating and using sequences
- Vectorized operations
- Functions
- Graphics
- Introudction to simulations
Preparing Data for Analysis
- Do this
- Not this
- Do this
- Not this
- Round-trip from Excel to CSV and back to Excel
- After
Working with Data
- Scalars
- Vectors
- Matrices and Arrays
- Lists
- Data frames
- Creating a data frame from scrach
- Reading data from files or URLs to dataframes
Grouping and Aggregation
- Sorting data
- Trnasposing data
- Aggregation (Subgrouping)
- Reshaping data
- Merging data
- Eyeball data sett
- Combine gene data from two data sets
- Checking for duplicates
- Remove duplicates
- Merging
- Rearrange column order
- Sorting data
Hypothesis Testing and Power Calculations
- What is covered in this section
- Set random see
- Estimation
- Hypothesis testing
- Explicit calculation of statistic
- Example: Comparing proportions
- Sample size calculations
Probability distributions and Random Number Genereation
- Probability distributions
- Continuous Distributions
Writing Custom Functions
- Anatomy of a function
- Default arguments
- Curve fitting
- Program Logic
Functional Programming
- R apply
Linear Regression
- Simple Linear Models
- Discussion
- R Formula Syntax
- Rails Example
- Simulation
Using R for supervised learning
- Supervised learning problem
- Comments
- Who was predicted wrongly?
- LOOCV
Unsupervised Learning
- Preprocessing
- Dimension reduction
- Clustering
- Heatmaps
Unsupervised Learning and NGS
- MDS/PCA
- Heat Map
Multiple Testing
- Coin Toss Experiments
Counting Models/Discrete Distributions
- Binomial Distribution and Bernoulli Trials
- Binomial Distribution
- Negative Binomial
- Poisson Distribution
- Binomial and Poisson Distributions
Generalized Linear Models
- What Happens When \(\mathbb{E}(Y|x)\) is Not Linear?
- Logistic Regression
- Negative Binomial
Base Graphics
- Building Graphs
- Plotting Graphcs and Heatmpas
Comparing Base Graphics with ggplot2
- Basic plots
- Using ggplot2 (Grammar of Graphics)
- Chaining plotting functions
- More examples
- Plot aesthetics
- Adding fitted lines
- Using existing model fits
- Fitting a lgoistic
Coding Exercises
- Practice 1
- Practice 2
- Practice 3
- Practice 4
Introduction to DESeq2
- Importing and Inspecting Data
- Estimate Size Factors and Dispersion Parameters
- Size Factors
- Dispersion Parameters
- Differential Expression Analysis
- Converting/Normalizing Counts to “Expressions”
- FPM
- FPKM
- Regularized log transformation
- Import count data using basic tools

Indices and tables¶