Quantification & Differential Expression¶

The Quantification & DE module provides statistical assessment of gene, TE, and tRNA expression differences across experimental conditions.

Overview¶

Differential expression analysis identifies transcripts that change significantly between biological groups, providing insights into biological processes and molecular mechanisms.

Workflow¶

graph LR
    A[BAM Files] --> B[featureCounts]
    B --> C[DESeq2 Analysis]
    C --> D[Differential Expression Reports]

Gene Quantification (featureCounts)¶

featureCounts is used to quantify the abundance of gene transcripts from mapped BAM files.

Accurate: Handles overlapping features.
Efficient: High performance with large genomes.

Differential Expression (DESeq2)¶

DESeq2 is a leading Biocunductor package for differential expression analysis.

Features¶

Shrinkage Estimation: Accurately estimates dispersion and fold changes.
Statistical Tests: Robust identification of differentially expressed genes.
Normalization: Corrects for differences in library size and sequencing depth.

Result Visualization¶

DESeq2 results are visualized through:

Volcano Plots: Highlight significantly changing genes.
MA Plots: Visualize the relationship between expression level and fold change.
PCA Plots: Assess the similarity between biological replicates.

Parameters & Defaults¶

Parameter	Default	Description
`deseq2.test`	`Wald`	Statistical test (`Wald` or `LRT`).
`deseq2.variable`	`genotype`	The column in the sample sheet to use for analysis.
`deseq2.reference_level`	-	The baseline level of the `variable` (e.g., `WT`).

Results¶

Location	Description
`results/analysis/rdata/`	DESeq2 analysis objects and normalized counts.
`results/analysis/tables/`	Lists of differentially expressed genes, TEs, and tRNAs.
`results/analysis/pictures/`	Volcano plots, MA plots, and PCA plots.
`results/qc/multiqc/`	Integrated reports from the entire pipeline.