Free PCA Plot Maker
Create a PCA score plot, biplot, or scree plot from plain text
Describe your principal component analysis — the groups, the percentage of variance on PC1 and PC2, and any ellipses or loading vectors — and AI draws a clean, publication-style PCA plot ready for genomics, metabolomics, and machine-learning reports.
PCA plot examples
Click any example to load its prompt, or use it as a starting point for your own PCA plot.
What is a PCA plot?
A PCA plot visualizes the result of principal component analysis, a technique that reduces many correlated variables into a few uncorrelated principal components. The most common form is the score plot: a scatter of samples on PC1 versus PC2, where each axis is labeled with the percentage of total variance it captures, and points are colored by group to reveal clustering, separation, or batch effects. This PCA plot maker turns a plain-text description into a clean, labeled figure — you describe your groups, the variance each component explains, and any confidence ellipses or loading vectors, and the AI produces a properly proportioned PCA plot with titled axes and a legend, without R, Python, or spreadsheets.
Why use a PCA plot maker
- Get a clean PCA score plot in seconds without writing R or Python plotting code.
- Show clustering, group separation, and batch effects clearly with colored groups and ellipses.
- Switch between a score plot, biplot, scree plot, or loadings plot from the same description.
- Produce publication-style figures for genomics, metabolomics, and machine-learning reports.
- Communicate dimensionality-reduction results to readers who never see the raw data matrix.
How to make a PCA plot
Describe the figure in plain language: name your groups, give the percentage of variance for PC1 and PC2, and say whether you want a score plot, biplot, scree plot, or loadings plot. Mention extras such as 95% confidence ellipses, a legend, or labeled loading arrows. SciDraw AI then draws a clean PCA plot with %-variance axis labels and tidy group colors, and you can refine the prompt and regenerate until the layout looks exactly right.
What you can draw
- PC1 and PC2 axes, each labeled with the percentage of variance explained
- Sample points colored by group or condition, with a matching legend
- 95% confidence ellipses summarizing the spread of each group
- Loading vectors drawn as labeled arrows for a biplot
- A scree plot of the variance explained by each successive component
- The origin at (0, 0) where the components are centered
PCA plot FAQ
What is a PCA plot?
A PCA plot visualizes principal component analysis, which compresses many correlated variables into a few principal components. The usual form is a score plot: a scatter of samples on PC1 versus PC2 with each axis labeled by the percentage of variance it explains and points colored by group. It is used to reveal clustering, group separation, and batch effects in genomics, metabolomics, and machine learning.
What do PC1, PC2, and the % variance mean?
PC1 (principal component 1) is the direction in your data that captures the most variance; PC2 is the next direction, orthogonal to PC1, that captures the most of the remaining variance. The percentage on each axis is the share of total variance that component explains, so an axis labeled 'PC1 (42%)' means that component accounts for 42% of the spread in the data. Higher percentages on the first two components mean the 2D plot represents the original data more faithfully.
What is the difference between a score plot, a biplot, and a scree plot?
A score plot shows the samples positioned on PC1 versus PC2 and is used to see clustering and group separation. A biplot adds variable loading vectors as arrows on top of the score plot, showing which variables drive the separation. A scree plot is a separate chart of the percentage of variance explained by each successive component, used to decide how many components to keep. A loadings plot, in turn, shows each variable's contribution to PC1 and PC2.
How do I make a PCA plot online?
Type a short description of the figure you want, such as a PCA score plot on PC1 (42%) vs PC2 (18%) colored by three groups with 95% confidence ellipses, and name any loading arrows or a scree plot if you need them. SciDraw AI then draws a clean, labeled PCA plot. You can adjust the wording and regenerate until the score plot, biplot, or scree plot matches what you need.
Is the PCA plot maker free?
Yes, you can start for free. Create an account and you receive free credits to generate PCA plots right away, with paid plans available if you need to make many figures on a regular basis.
Is it suitable for research or publication?
The figures are publication-style and great for slides, posters, drafts, and teaching, but they are illustrative diagrams generated from your description, not computed from your data. For a results figure in a paper, run the actual principal component analysis on your dataset (in R, Python, or your stats package) so the point positions, variance percentages, and ellipses reflect real values, and use this tool to mock up or explain the layout.
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Try it freeDraw your PCA plot now
Describe your principal component analysis — groups, %-variance axes, ellipses, or loading vectors — and get a clean PCA plot in seconds. Start free with SciDraw AI.
