NOC:Biological Data Analysis and Visualization with R

Lecture 1 - Introduction

Lecture 2 - Setting up R

Lecture 3 - Basic functions in R

Lecture 4 - Basic data structures in R

Lecture 5 - Data structures and File I/O

Lecture 6 - Basic statistical tests with R

Lecture 7 - Correlation Analysis

Lecture 8 - Analysis of Variance (ANOVA)

Lecture 9 - Basic data visualization techniques

Lecture 10 - Data visualization

Lecture 11 - Visualization with vioplot and ggplot2

Lecture 12 - R packages for plotting and data organization

Lecture 13 - Data transformation in R

Lecture 14 - Bioconductor packages

Lecture 15 - Flow cytometry data analysis in R/Bioconductor

Lecture 16 - Gene expression analysis and co-expression network

Lecture 17 - WGCNA package and Data Download

Lecture 18 - WGCNA hands-on: Data preprocessing

Lecture 19 - WGCNA hands-on: Soft-threshold

Lecture 20 - WGCNA: Module gene expression

Lecture 21 - Introduction to ChIP-seq

Lecture 22 - ChIP-seq data analysis

Lecture 23 - ChIP-seq data analysis: Peak calling

Lecture 24 - Peak calling and Visualization

Lecture 25 - ChIP-seq data analysis: bigWig/bw files

Lecture 26 - Regression models on Biological data

Lecture 27 - Predictive models with linear regression

Lecture 28 - Multicollinearity

Lecture 29 - Lasso regression

Lecture 30 - Non-linear regression

Lecture 31 - Dimensionality reduction

Lecture 32 - Principal Component Analysis (PCA)

Lecture 33 - PCA analysis hands-on

Lecture 34 - PCA analysis using ‘PCAtools’

Lecture 35 - UMAP analysis

Lecture 36 - Classification of biological samples

Lecture 37 - Penalized and Stepwise Logistic regression

Lecture 38 - Decision trees

Lecture 39 - Classification and Regression trees

Lecture 40 - Random Forests