DPI
Load a sample-by-feature matrix to compute a PCA biplot.
Use Browse file or drop a CSV / TSV,
or click Demo Data to try example data.
Samples:—
Features:—
Filtered (zero var):—
PCs computed:—
PC1+PC2 var:—
Load a sample-by-feature matrix to compute a PCA biplot.
Use Browse file or drop a CSV / TSV,
or click Demo Data to try example data.
Input format
CSV or TSV. First column should be sample IDs. The second column may optionally be a group/annotation (any non-numeric column is auto-detected). Remaining columns are numeric features.
Orientation
Default: rows are samples, columns are features. If your matrix is flipped, tick Transpose: features in rows.
Scaling
Choose Standardize (z-score, default) for typical omics matrices where feature variances differ in scale. Center only keeps original variance weighting. None performs PCA on raw values.
Axes
X and Y default to PC1 and PC2. Pick any two PCs from PC1 through PC10 to explore secondary structure.
Ellipses
95% confidence ellipses per group are computed from the sample covariance using the chi-squared 2-df quantile. Enable only after selecting a group column with at least 3 samples per group.
Loadings
Loading arrows are scaled to fit the biplot. The largest arrow reaches roughly 80% of the score range. The scale factor is reported in the Figure Legend modal.
Export
PNG honors the DPI selector (300 DPI is publication standard, 600 for high-density journals). SVG is vector and opens in Illustrator and Inkscape. Copy puts PNG on the clipboard.
If this tool was useful in your work, please cite it as:
EuropaDX, Inc. (2026). PCA Biplot — EuropaXp OmicsCloud Bioinformatics Toolkit. Available at https://europadx.com/tools/pca/The PCA tool runs entirely client-side and may freeze or run out of memory on datasets above ~5,000 samples or ~50,000 features.
EuropaXp OmicsCloud handles datasets of any size with full pipeline support, persistent workspaces, and reproducible reports.