Fundamental Skills in Bioinformatics 

Brief introduction to the course

Lecture: Programming and R

Lecture: Introduction to RStudio

Coding Lecture: First contact with RStudio

Introduction

Lecture: Data types in R

Lecture: Data structures in R

Coding Lecture: Data types in R - atomic and vectors

Coding Lecture: Data types in R - lists and matrices

Coding Lecture: Data types in R - data frames

Lecture: Introduction to Control Flow

Lecture: Loops

Coding Lecture: If statements

Coding Lecture: loop statements

Lecture: Loading and Writing

Coding Lecture: Loading and Writing

Basics + where to learn more

Setting up R

Available data sets to be used in the course.

Introduction to R Quiz

Data Types in R Quiz

Control Flow in R Quiz

Loading and Writing in R Quiz

Introduction to Module 2

Lecture: Logical values, logical vectors and operations with them.

Coding Lecture: Logical Vectors, part 1.

Coding Lecture: Logical Vectors, part 2.

Lecture: Data Quality Control.

Coding Lecture: Quality Control.

Lecture: Exploratory Data Analysis.

Coding Lecture: EDA part 1.

Coding Lecture: EDA part 2.

Lecture: Correlation

Coding Lecture: correlation in R

Lecture: Linear Models

Coding Lecture: example of a linear model

Coding Lecture: evaluation of a linear model in R

Lecture: t-test & ANOVA

Coding Lecture: t-test.

Coding Lecture: ANOVA

Introduction to the dataset: Data set 4.

Guided analysis.

Lecture: R packages

How do R programming assignments work?

Programming Assignment Basics Quiz

Exploratory Data Analysis and Visualization in R

Operating with logical values and matrices

Quality control of the data

Correlation analysis

Linear models

t-test and ANOVA

First analysis of an expression dataset.

Introduction to the module

Lecture: Python and R

The Python ecosystem

Python installation and environments

Jupyter Lab

Lecture: Python native data structures

Coding Lecture: Fundamentals in data types

Coding Lecture: Lists and Tuples

Coding Lecture: Sets and Dictionaries

Lecture: flow control and functions.

Coding Lecture: if conditions, for and while loops.

Coding Lecture: declare and using functions in Python

Lecture: overview of modules in Python

Lecture: numpy

Coding Lecture: numpy

Lecture: pandas

Coding Lecture: pandas

Coding lecture: pandas for data exploration

Coding Lecture: Visualization

Free online Python resources

Python primitive values and data structures

Python syntax: for, if statements and functions

The numpy package

The pandas package

Python data structures

Python control flow

The NumPy package

The pandas package

Visualization with the pandas package

Overview of the week

Lecture: Introduction to the case study

Lecture: RNA-seq technology and data normalisation

Coding Lecture: Loading and normalizing RNA-seq data

Lecture: Principal Component Analysis

Coding Lecture: PCA analysis in R for RNA-seq data

Lecture: Finding differentially expressed genes

Coding Lecture: Differential expression analysis in R

Lecture: From RNA-seq to scRNA-seq

Lecture: Representing scRNA-seq experiments in Python

Coding Lecture: Loading a scRNA-seq experiment in Python

Lecture: Preprocessing scRNA-seq data

Coding Lecture: scRNA-seq preprocessing

Lecture: UMAP and dimensionality reduction in single-cell studies

Lecture: Cell type identification

Coding Lecture: Clustering and cell type identification with Python

Coding Lecture: scRNA-seq analysis in R

Lecture: bioAI

Thanks (for all the fish)

Relevant material for Week 4

Reference resources for single-cell analysis in Python

Lecture: Representing scRNA-seq experiments in Python

scRNA-seq preprocessing

Clustering and cell type indentification with Python

Analysis of bulk RNAseq CD4+ T-cell data

The anndata package: managing scRNA-seq data in Python

scRNA-seq preprocessing with the scanpy package

Cell type identification