RNA-seq-Analysis-with-R

In general, the analysis of differential expression for RNA-seq is divided into two steps (each one that in turn includes other steps):

QA:

• Count of gene-associated reads
• Normalization
• Unsupervised cluster analysis

Differential Expression Analysis

• Modeling of raw counts for each gene
• Reduced log2 fold changes
• Test the differential expression

Case study

In this case we are going to work with a dataset used by the article by Gerarduzzi et al., 2017 (DOI: 10.1172/jci.insight.90299). Basically, they want to know why mice overexpressing the Smoc2 gene are more likely to develop renal fibrosis. In this case, two sample groups are being tested:

• 3 Mice without fibrosis and with overexpression of Smoc2
• 4 mice with fibrosis and with overexpression of Smoc2 Essentially, one wants to know if gene expression is differential between groups.

View as HTML:

https://alejandro2195.github.io/RNA-seq-Analysis-with-R/