Elite Bioinformatics Visualizations: Volcano & PCA

In the high-stakes world of the bioeconomy, data isn't just a number—it's the map to the next breakthrough. Today we are launching our Bioinformatics Visualization Suite, a set of elite, interactive components designed for deep scientific insight.

1. Differential Expression: The Volcano Plot

When analyzing RNA-Seq or proteomics data, we need to quickly identify genes that are both statistically significant and biologically meaningful.

The Volcano Plot below allows you to hover over individual genes to see their log2 fold change and p-values. Significant genes are automatically highlighted: Red for Upregulated and Blue for Downregulated.

Differential Expression Analysis

Significance vs. Fold Change (Threshold: p < 0.05, FC > 1)

2. Dimensionality Reduction: PCA Analysis

Biotech data is multidimensional. Principal Component Analysis (PCA) helps us understand how samples group together—whether by cell line, treatment group, or batch.

Below is a cluster visualization identifying 3 distinct sample populations using our new Scatter rendering engine.

Sample Clustering (PCA)

PC1 vs PC2 capturing 85% of total variance.

Why This Matters

By embedding these visualizations directly in our scientific reports, we enable:

Reproducibility: The data lives with the narrative.
Engagement: Readers interact with the results instead of just looking at static PDFs.
Speed: Go from raw CSV data to a published report in minutes.

Stay tuned for our upcoming Genome Coverage and Pathway Enrichment components!

Bioinfometrics