Dimensionality Reduction

2D PCA

Interactive 2D principal component analysis visualization. Explore sample clustering and variance decomposition across dormancy states with publication-quality scatter plots.

Key Features

2D scatter plot

PC1 vs PC2 with confidence ellipses

Scree plot

Variance explained bar chart

Biplot overlay

Gene loading vectors on sample plot

Multi-group

Support for 10+ color-coded groups

Color Palette

Analysis Tip

Upload your gene expression matrix or analysis results in CSV/TSV format. The tool will automatically generate a publication-quality 2d pca figure ready for your dormancy omics manuscript.

Data Input

Upload Data

Drop TSV file here

Format: id | Type | feature1 | feature2 ...

Or paste data

Required Files

data.tsv — Sample x gene matrix

Format

Row = samples, Column = genes/features
1st column: Sample ID
2nd column: Group label (for coloring)
Remaining columns: Gene expression values

PCA Data

ID	Group	Gene1	Gene2	Gene3	Gene4	Gene5
S1	Active	5.2	3.1	8.2	1.2	4.5
S2	Active	6.1	2.8	7.5	2.1	5.2
S3	Torpor	7.2	3.9	9.1	2.8	5.8

Click to load example data and generate plot instantly

Parameters

Clustering Method

Distance Metric

Scale

Color Scheme

2D PCA Visualization

Upload data and click "Generate 2D PCA" to visualize