There are a number of methods/algorithms that can be applied to scrutinize the significant genes from the RNA expression data. Depending on whether data is normalized or not, these methods can be applied. For instance, if data follow normal distribution, we can use various types of T-test method (Welch method, Wilcoxon, etc); if data is non-normalized (i.e. Raw read count, read count values), we can apply DeSeq, Deseq2, EdgeR, etc. Thus, depending upon the data type, one can choose a method. In the previous lesson, we already talked about how we can apply the T-test and how we can extend the RNA-seq pipeline to a differential expression pipeline.
Differential Expression
A common use of expression assays is to look for differences in expression levels of genes or other objects of interest between two experimental conditions, such as a wildtype vs knockout. In order to do this, the data needs to be transformed from a list of reads mapping to genomic coordinates into a table of counts. Once transformed, a test can be performed to look for statistically significant differences in expression level.
These tools calculate the abundance of each gene expressed in a RNA-Seq sample. Some software is also designed to study the variability of genetic expression between samples (differential expression). Quantitative and differential studies are largely determined by the quality of reads alignment and accuracy of isoforms reconstruction.
More info on differential expression.
In this video, you will learn how we can apply the DeSeq2 pipeline and perform enrichment and GSEA analysis when we have gene expression in the form of raw count or read count values.
To learn more about differential gene expression analysis and biological annotation pipeline, visit: https://learn.omicslogic.com/Learn/course-5-transcriptomics/lesson/09-t2-differential-gene-expression-and-gene-enrichment-analysis
In this lesson, you will learn step wise hands-on analysis and learn to interpret the results obtained after the completion of the pipeline, which include a number of outputs, such as:
- Volcano plot : Visual Representation of differential expressed genes
- Deseq_all.txt : Differential expressed genes in the Tabular format
- Enrichment and GSEA plots: You can find details regarding each plot in the next section.