Desktop GUI (Python/tkinter) for Principal Component Analysis on tabular data — e.g. compound/ metabolite concentration profiles across sample groups (HPLC data, chemotaxonomy, quality control), or any samples × variables dataset — producing publication-ready plots: scree plot, loadings bar chart and biplot.
- Load data from Excel, in either orientation — samples as rows or samples as columns (with
automatic transposition, see
trasponi.py) - Choose which variables to include and which samples to exclude, with per-group colors and distinct markers (up to 6 groups)
- Several scaling methods: z-score, mean-centering, min-max, robust, or none
- Automatic handling of missing values (mean imputation, with a warning)
- Three linked plots in one figure: scree plot (explained/cumulative variance), loadings bar chart, and biplot (scores + loading vectors + group confidence ellipses)
- Editable spreadsheet view of the loaded/transformed data
- Export: full figure or individual panels (PDF/PNG/SVG), scores/loadings/variance to Excel, or a ready-to-plot Excel workbook (separate sheets for scores, loading arrows, group ellipses) for building the same chart natively in Excel
Edit Figure...reopens any one panel (scree / loadings / biplot) in PlotStyleKit's standalone figure editor for titles, per-element styling and publication-size export;Salva figura (pickle)...saves a panel as a live, re-editableFigureobject instead of a raster image — see Requirements below
pip install -r requirements.txt
numpy, pandas, scikit-learn, matplotlib, openpyxl
adjustText # optional — avoids overlapping labels in the biplot
PlotStyleKit is optional: it provides the shared Origin-like plot
style and the standalone figure editor behind Edit Figure... / Salva figura (pickle).... The program looks
for it next to itself (../PlotStyleKit), in ~/progetti/PlotStyleKit, in the folder named by the environment
variable PLOTSTYLEKIT and in the folder you chose the last time; if it finds none it says so at start and asks
for the folder, with Cancel for going without it (it will not ask again; the PlotStyleKit... button changes
that). Without it the app uses default matplotlib styling, Salva figura (pickle)... writes the panel as drawn
and Edit Figure... offers to locate PlotStyleKit.
See installa_pca_gui.txt for a step-by-step Windows/PowerShell setup
guide (Italian).
pythonw pca_gui.pyw
Double-clicking the .pyw file on Windows works too (no console window).
Standard layout — first row is the header, one sample per row:
Sample | Group | Variable1 | Variable2 | …
F1 | F | 38.5 | 466.4 | …
F2 | F | 0.03 | 370.7 | …
Transposed layout — samples arranged in columns, detected and converted automatically:
Samples | F1 | F2 | … | SD6
Groups | F | F | … | SD
Compound1| 38.5 | 0.03 | … | 0.07
Compound2| 466 | 370 | … | 240
trasponi.py converts a transposed file to the standard layout on its own (useful for sharing
data with colleagues):
python trasponi.py <transposed_file.xlsx> [output_file.xlsx]
python -m unittest discover -s tests -v
The tests open a hidden Tk window (they are skipped when tkinter or a display is not available), run the PCA on the built-in example data and check the results against scikit-learn: explained variance, orthonormal loadings, group separation, excluded samples, missing-value imputation and the scaling options. They also check that the standard and transposed layouts give the same table and that the app runs when the PlotStyleKit sibling repo is missing.
Salva figura (pickle)... writes Python pickle files, and Edit Figure... in PlotStyleKit opens
them. A pickle can run arbitrary code when it is loaded, so open only figure files you created
yourself or received from someone you trust.
pca_gui.pyw— main application (a singlePCAAppclass)trasponi.py— standalone converter, transposed layout → standard layoutinstalla_pca_gui.txt— Windows setup guide (Italian)tests/test_pca_core.py— unit tests (see Tests)requirements.txt— Python dependencies