Bulk RNA-Seq Data Analysis
For a typical bulk RNA-seq experiment, once the raw sequencing data are available, the standard analysis package consists of:
- quality assessment of the raw data
- alignment of the reads to genome/transcriptome
- data mining, including principal component analysis and clustering
- identification of differentially expressed genes between treatment groups using a multivariate statistical model
- pathway annotation and visualization of differentially expressed genes
Example volcano plot showing the results of a statistical test for scoring differential gene expression between two groups of samples.
The advanced data analysis package will always be custom-tailored to your project according to your needs. For example, such a package can contain:
- a thorough literature review of the biological/medical context or the experiment
- a deeper investigation of genes relevant in the context
- assessment of the effect of additional sample characteristics on gene expression patterns
- based on all observed effects, more advanced statistical models will be set up to find genes that differentially express genes even at lower effect sizes
- more detailed annotation and biological/clinical interpretation of the differentially expressed genes
Example heatmap showing the clustering of samples and genes based on highly variable genes.
