Computational Genomics with R Book Club
Welcome
Book club meetings
Pace
1
Introduction to Genomics
1.1
Genes, Genomes, and Genomics
1.2
Gene regulation
1.2.1
Transcriptional regulation
1.2.2
Post-transcriptional regulation
1.3
Genetic mutations
1.4
Sequencing
1.5
Resources
1.6
Meeting Videos
1.6.1
Cohort 1
1.6.2
Cohort 2
2
Introduction to R for Genomic Data Analysis
2.1
Genomic data analysis (a high level view)– in R!
2.2
Getting started with R and Bioconductor
2.3
Data stuctures and data types
2.4
Reading and writing data
2.5
Plots: Base R vs ggplot
2.6
Functions and Loops
2.7
Resources
2.8
Meeting Videos
2.8.1
Cohort 1
2.8.2
Cohort 2
3
Statistics for Genomics
3.1
Introduction
3.2
How to summarize collection of data points: The idea behind statistical distributions
3.2.1
Measuring the measuring central tendency
3.2.2
Measurements of variation
3.2.3
Statistical distributions
3.2.4
Confidence intervals
3.3
How to test for differences between samples
3.3.1
Randomization
3.3.2
t-test
3.3.3
Multiple testing
3.3.4
Moderated t-tests
3.4
Relationship between variables: Linear models and correlation
3.4.1
The cost or loss function approach
3.4.2
The “maximum likelihood” approach
3.4.3
Linear algebra and closed-form solution to linear regression
3.4.4
How to estimate the error of the coefficients
3.4.5
Accuracy of the model
3.4.6
Regression with categorical variables
3.4.7
Regression pitfalls
3.5
Meeting Videos
3.5.1
Cohort 1
3.5.2
Cohort 2
4
Exploratory Data Analysis with Unsupervised Machine Learning
4.1
Very helpful youtube explanations
4.2
Chapter 4 Exercises
4.3
Meeting Videos
4.3.1
Cohort 1
4.3.2
Cohort 2
5
Predictive Modeling with Supervised Machine Learning
5.1
Types of Learning and Chapter Code Preparation
5.2
Data pre-processing
5.3
Split the data
5.4
Some unsupervised learning on training data to initially cluster data
5.5
Precision, Specificity, and Sensitivity
5.6
Actual prediciting k (model tuning)
5.7
Supervised algorithm #1: Random forest
5.8
Supervised Algorithm #2: Logistic Regression and Regularization
5.9
Other algorithms
5.10
Prediciting continuous variables by regression
5.11
Meeting Videos
5.11.1
Cohort 1
5.11.2
Cohort 2
6
Operations on Genomic Intervals and Genome Arithmetic
6.1
Genomic Ranges
6.1.1
GRanges objects
6.1.2
Getting genomic data into R as a table
6.1.3
Manipulating GRange Objects
6.2
BAM/SAM and reading HTS data
6.3
Rle Vector compression
6.4
SummerizedExperiment
6.5
Visualize Genomic Intervals
6.6
Meeting Videos
6.6.1
Cohort 2
7
Quality Check, Processing and Alignment of High-throughput Sequencing Reads
7.1
.fasta vs .fastq
7.2
Filtering and trimming reads with the QuasR package
7.3
Mapping aligned reads to the genome (also with QuasR)
7.4
Meeting Videos
7.4.1
Cohort 2
8
RNA-seq Analysis
8.1
Processing, alignment, quantification
8.2
Normalization of the read counts
8.3
Exploratory analysis
8.4
Differential expression analysis
8.5
Diagnostic plots
8.6
Functional enrichment analysis
8.7
Accounting for additional sources of variation
8.8
Meeting Videos
8.8.1
Cohort 2
9
ChIP-seq analysis
9.1
SLIDE 1
9.2
Meeting Videos
9.2.1
Cohort 2
10
DNA methylation analysis using bisulfite sequencing data
10.1
SLIDE 1
10.2
Meeting Videos
10.2.1
Cohort 2
11
Multi-omics Analysis
11.1
SLIDE 1
11.2
Meeting Videos
11.2.1
Cohort 2
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Computational Genomics with R Book Club
Chapter 5
Predictive Modeling with Supervised Machine Learning
Learning objectives:
Cleaning and preparing data for supervised learning; splitting data set
Perform unsupervised learning to cluster data
Supervised learning