Exploring Gene Expression R for Interpreting Biological Data
https://DevCourseWeb.com
Published 6/2023
Created by Abdul Rehman Ikram
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 10 Lectures ( 1h 41m ) | Size: 964 MB
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.
