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Udemy - Exploring Gene Expression R For Interpreting Biological Data
Exploring Genetic Insights and Unlocking Biological Patterns through Data Analysis with R Programming language
What you'll learn
Possess a solid understanding of bioinformatics principles and methodologies.
Be proficient in using R for data manipulation, analysis, and visualization in the context of bioinformatics.
Acquire knowledge of data manipulation, visualization, and statistical analysis techniques using R.
Gain expertise in differential gene expression analysis using Deseq2.
Perform genomic and transcriptomic analysis, such as genome assembly, gene expression analysis, and differential expression.
Be able to convert gene IDs to gene symbols for improved interpretability.
Have the skills to create compelling visualizations of gene expression data using ggplot2.
Be equipped with the knowledge and techniques to analyze scRNA-seq data using the R Seurat pipeline.
Requirements
Basic Biology Knowledge: Familiarity with basic biological concepts such as genes, proteins, DNA, and biological processes. Understanding of genomics and transcriptomics principles is helpful.
Programming Fundamentals: Prior experience with programming fundamentals is desirable, preferably in a language like Python or R. Knowledge of variables, functions, control structures, and basic data manipulation will provide a strong foundation for learning R programming.
Statistics and Data Analysis: An understanding of basic statistical concepts, such as descriptive statistics, hypothesis testing, and data visualization, is advantageous. This knowledge will assist students in effectively analyzing and interpreting biological data.
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FILE LIST
Filename
Size
~Get Your Files Here !/1. Introduction/1. Introduction to Bioinformatics and R Exploring the Intersection of Biology.mp4
67.2 MB
~Get Your Files Here !/1. Introduction/2. Getting Started with R Installation and Variables Understanding.mp4
104.7 MB
~Get Your Files Here !/1. Introduction/3. Working with R Packages Installing, Loading, and Exploring Bioinformatics.mp4
76.2 MB
~Get Your Files Here !/2. Bioinformatics Analysis in R/1. Differential Gene Expression Analysis with Deseq2 Preparing Data.mp4
27.5 MB
~Get Your Files Here !/2. Bioinformatics Analysis in R/2. Deseq2 Code Understanding.mp4
190.6 MB
~Get Your Files Here !/2. Bioinformatics Analysis in R/3. Converting Ensembl Gene IDs to Gene Symbols Using R Techniques and Packages.mp4
131.2 MB
~Get Your Files Here !/2. Bioinformatics Analysis in R/4. Visualizing Gene Expression Data Creating Stunning Plots with ggplot2.mp4
79.9 MB
~Get Your Files Here !/2. Bioinformatics Analysis in R/5. Introduction to Single-Cell RNA Sequencing (scRNA-seq) Data Analysis.mp4
52.9 MB
~Get Your Files Here !/2. Bioinformatics Analysis in R/6. Exploring scRNA-seq Code Cell Trajectories and Gene Expression Dynamics.mp4
233.9 MB
~Get Your Files Here !/2. Bioinformatics Analysis in R/7. GitHub Source Code.html
