Advanced RNAseq Analysis

Overview

This module teaches you to use various R-based tools for RNAseq data analysis. You will learn about mapping (kallisto), differential expression analysis (DESeq2, edgeR, sleuth), annotation, clustering, GO analysis and other biological inference procedures.

RNAseq is a major application of high-throughput sequencing technologies (NGS), but researchers in biology often struggle with its data analysis component. This module teaches you to use various R-based tools for RNAseq data analysis. You will learn about mapping (kallisto), differential expression analysis (DESeq2, edgeR, sleuth), annotation, clustering, GO analysis and other biological analyses of data.

Questions You Will Be Able to Answer after This Module

Prerequisites

You need to complete NGS - RNAseq Analysis using R before this module.

Module Length

Three sessions of 2 hours each.

Book/Tutorial

Topics

  1. Counting using kallisto
  2. Merging results and annotation using dplyr,
  3. Differential expression analysis (DESeq2, edgeR, limma-voom, sleuth),
  4. Clustering and heatmap plots,
  5. Annotation process,
  6. GO analysis.

Sessions

First Session

image (Guassian distribution)

Running Kallisto in linux

Statistical analysis - DESeq, etc.

Second Session

image (Linear regression)

Clustering and heatmap

Biological analysis

Third Session

image (clustering and heatmap)

Running full pipeline

Class Style

The classes will be conducted through online interactive chat session.

Testimonials

You can read the testimonials for our summer R classes here.

Cost

$125 (20% discount for premium members).

Register

Please sign up for the module at the following link. At present no payment is necessary to register.

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