Gene Expression Analysis using R Programming
Gene Expression Analysis, R Programming, Bioinformatics, Differential Expression, Data Preprocessing, Data Visualization, Statistical Analysis, Pathway Analysis, Biomarker Discovery, Molecular Biology.
About Program:
Program on Gene Expression Analysis using R Programming is a comprehensive and practical program designed to equip participants with the essential skills and knowledge to conduct sophisticated gene expression analyses. Gene expression analysis plays a pivotal role in understanding the molecular basis of biological processes and diseases, and R programming offers a powerful and flexible platform for performing these analyses.
Aim: The aim of Gene Expression Analysis using R Programming is to provide participants with the necessary knowledge and skills to effectively explore, analyze, and interpret gene expression data using the R programming language. This program seeks to empower researchers, biologists, bioinformaticians, and students with a powerful computational toolset to gain valuable insights into the molecular basis of biological processes and diseases.
Program Objectives:
- Introduce participants to the fundamentals of gene expression analysis.
- Familiarize attendees with R programming for bioinformatics.
- Teach data preprocessing and quality control techniques for gene expression data.
- Enable participants to perform differential expression analysis using R.
- Train participants in creating effective visualizations of gene expression data.
- Provide an understanding of statistical interpretation and significance in gene expression analysis.
- Introduce pathway analysis methods to interpret gene expression results in a biological context.
- Offer hands-on experience through practical exercises and real-world datasets.
- Promote best practices to ensure accurate and reliable gene expression analysis.
- Facilitate collaborative learning and knowledge-sharing among participants.
What you will learn?
Day 1:
- Introduction to RNA seq and R
- Data types structures in R
- Installing packages in R
Day 2:
- Quality control and preprocessing
- Read Mapping using HISAT2
- Quantification of reads using FeatureCount
Day 3:
- Differential Expression for RNA-seq using DESeq2
- Visualization DEG and generation of heatmap
- Perform functional analysis on gene lists with R-based tools
- Pathway analysis
Installation Requirements
- Download the most recent versions of R and RStudio for your laptop:
- Packages to be installed: FASTQC, Trimmomatic, HTSeq2, FeatureCount
Fee Plan
Intended For :
Graduates, Post Graduates, Research Scholars, Academicians, Industry Professionals Bioinformatics, Computational Biology, Biology and Life Sciences,Biotechnology and Pharmaceutical Sciences, Medical and Clinical Research, Genomics and Genetics,
Career Supporting Skills
Program Outcomes
- Proficiency in R programming for gene expression analysis.
- Hands-on data analysis skills with real-world gene expression datasets.
- Interpretation of gene expression patterns to identify differentially expressed genes.
- Data visualization and effective presentation of gene expression analysis results.
- Understanding of data quality control and normalization for reliable analysis.
- Interpretation of gene expression data in the context of biological pathways and networks.
- Application of statistical methods for gene expression analysis and hypothesis testing.
- Knowledge of best practices and reproducibility in gene expression analysis.
- Collaborative learning and networking with peers and instructors.
- Confidence in conducting independent gene expression analysis using R programming.
