Visit Us At

Kenbelt Park, Nairobi, Kenya

Email Us On

[email protected]

Call Us On

+254 113245615

"Africa's BIOTECH COMPANY"
Outsourec yur clinical trials in Africa

Differential Gene Expression Analysis using R

In this course, the student gets hands-on skills on how to analyze RNA SEQ data in order to identify genes that are upregulated or downregulated. This is helpful in comparing diseased vs healthy tissues, identification of biomarkers for precision medicine and drug discovery, and understanding developmental processes. The objective of the Differential Gene Expression Analysis using R Course is to enhance the researchers’ capacity to conduct world class scientific research and boost their institution’s profile as a leading diagnostic and scientific center.

At the end of the Differential Gene Expression Analysis using R Course, the participant will be able to:
Differential Gene Expression Analysis using R
  • Learn the science, methods, and applications of RNASeq
  • Retrieve data from public RNASeq databases
  • Perform quality control of RNASeq data
  • Align RNASEQ datasets against a reference genome
  • Quantify read counts for each gene
  • Perform differential gene expression analysis using edgeR using QL F, glmQLFTest, glmTreat, tests, heat map clustering,
  • Visualize RNASeq data using heat maps, bar plots, scatter plots, volcano plots, and Upset plots.
  • Perform pathway analysis including gene ontology (GO) and KEGG pathway analysis and gene enrichment
Using the most powerful software, you will gain the latest skills in bioinformatics analysis.
This is the default Tab content. Feel free to delete it.
This is a 5-day training program covering the following topics:
  • Introduction to RNA Sequencing
  • Introduction to Linux commandline
  • Introduction to R
  • Quality control of RNA sequences
  • Obtaining Reference datasets from NCBI
  • Building the Genome Index
  • Alignment of RNA sequences against the reference sequences
  • Quantifying read counts for each gene
  • Differential Gene Expression (DGE) using edgeR
  • Differential Gene Expression Analysis
  • Pathway Analysis
  • Gene set enrichment analysis
The course is ideal for students and researchers working in the pharma, biotech, and medical industries. The following terms and conditions apply.

  • Minimum Entry Requirements: a bachelor’s degree in bioinformatics, genetics, molecular biology, biomedical science, microbiology or any related field. Basic knowledge of the Linux commandline and R is useful but not mandatory.
  • Email: [email protected] for fee inquiry or fill the contact form in the “Enroll” button
Kindly fill the form details below to register for your course of interest.

Short Courses