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Course 1: Introduction to Programming and bulk & Single Cell RNAseq Analysis in R
Course 1: Introduction to Programming and bulk & Single Cell RNAseq Analysis in R
Overview
Overview
Mode of Communication: Bangla
Duration: 4 months
Frequency: Once a week (every Saturday)
Live Session Time:
Lecture (3 hours):
8:00 AM ~ 11:00 AM (USA/NY),
1:00 PM ~ 4:00 PM (UK),
7:00 PM ~ 10:00 PM (BD)
Total Classes: 48 hours of live lectures and support
Assignments: Weekly (12 assignments total)
Community Coding Support: Through Google Classroom
Projects: Comprehensive projects
Contact: oxfordbiodiscoveryventures@gmail.com
Prerequisites: None. This course is tailored for complete beginners
E-Certificates will be provided after successful completion of the course
E-Certificates will be provided after successful completion of the course
Module 1
Module 1
Introduction to Programming in R for Biologists
Introduction to Programming in R for Biologists
Week 1: Introduction to R
Overview of R and its ecosystem.,Installing R and RStudio.,Basic R syntax, data types, and variable assignments.
Week 2: Data Structures in R
Creation, manipulation, and operations on vectors, ,lists, matrices and dataframe
Week 3: Control Structures and Functions
Conditional statements, loops, user-defined functions.
Week 4: Data Import, Export, and Cleaning
Import/export data from various formats, cleaning techniques
Week 5: Data Visualization in R
Basic plotting, ggplot2 graphics.
Week 6: Advanced Data Visualization in R
Advanced ggplot2 graphics.
Week 7: Basic Statistical Coding in R
Mean, median, variance, probability distributions. ANOVA, t-tests, chi-square tests, linear regression.
Week 8: Comprehensive Project 1
Data analysis and visualization using R.
Module 2
Module 2
Bulk RNAseq Analysis in R
Bulk RNAseq Analysis in R
Week 9: Introduction to RNAseq
RNA sequencing technology, experiment design, data acquisition
Week 10: RNAseq Preprocessing
FastQC, alignment to a reference genome, quantification.
Week 11: Quality Control and Normalisation
Performing data quality control and different techniques for normalisation
Week 12: Differential Gene Expression Analysis and Downstream Analysis
Performing differential expression analysis, interpreting results.
Module 3
Module 3
Single Cell RNAseq Analysis in R
Single Cell RNAseq Analysis in R
Week 13: Introduction to scRNAseq
Preprocessing, normalization, introduction to Seurat.
Week 14: Advanced scRNAseq Analysis
Clustering, visualization, identifying cell types.
Week 15: Whole scRNAseq from scratch
scRNAseq analysis on a real dataset.
Week 16: Advanced Gene Expression Project
Final Project Presentation Discussion, Present Ideas for Projects
Week 17: Project Presentation
Project Presentation,
Discussion on bioinformatics careers, additional tools (Python, Linux), future learning.
Instructor
Instructor
Mohammad Umer Sharif Shohan
DPhil Student
Department of Biochemistry,
University of Oxford,
Oxford, United Kingdom
Address
Address
Oxford, Oxfordshire,
United Kingdom
Email
Email
oxfordbiodiscoveryventures@gmail.com
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