How do I make a PCA plot from a score table?

Paste or upload a table with sample, PC1, and PC2 (and group if you have it). The scatter renders immediately. Set axis titles if you want “PC1 (42%)”, label a few outliers, and download the SVG.

Does this run PCA for me?

No. It plots scores you already computed. It will not decompose a gene-by-sample matrix, scale variables, or report eigenvalues.

Is this a PCA biplot?

No. A biplot adds loading arrows for variables. This page is the sample score scatter. Keep loadings in your analysis notebook if you need them.

Can I color points by condition or cluster?

Yes. Include a group, cluster, condition, or cell type column. Each distinct value gets a color and a legend count.

How is this different from a scatter plot maker?

The scatter plot maker is a general x–y chart. This page is for PCA scores: PC1/PC2 column detection, group colors, origin lines, and copy that does not pretend to explain principal components.

Is the PCA plot generator free?

The precise, table-driven plot is free to use and free to download as SVG, with no sign-up. Sign in only if you want AI illustrations or other ConceptViz generation tools.

Research Tool

PCA Plot Generator for sample scores

Free PCA plot generator. Paste sample scores for PC1 and PC2 to draw an exact scatter and download SVG.

Paste sample, PC1, PC2Color by group or clusterOrigin lines, label outliersDownload a watermark-free SVG — free

Paste sample, PC1, PC2 scores — renders an exact PCA scatter as SVG, free

PCA score table

sample, PC1, PC2, and optional group. This plots scores you already computed — it does not run PCA.

sample,PC1,PC2,group
s1,-5.300,-1.387,Control
s2,-6.318,-2.298,Control
s3,-4.809,0.190,Control
s4,-4.509,-2.992,Control
s5,-4.966,-4.543,Control
s6,-3.550,-0.046,Control
s7,-2.470,-0.697,Control
s8,-2.924,-0.018,Control
s9,-3.701,-2.608,Control
s10,-1.405,-2.033,Control
s11,-5.375,-1.714,Control
s12,-3.635,-0.753,Control
s13,-4.687,-2.383,Control
s14,-4.553,-0.754,Control
s15,-4.555,-0.865,Control
s16,-1.800,-2.590,Control
s17,-3.280,-1.551,Control
s18,-4.275,-0.726,Control
s19,-3.626,1.059,Control
s20,-3.134,-2.295,Control
s21,-5.595,-1.304,Control
s22,-5.908,-1.308,Control
s23,-4.103,-2.406,Control
s24,-2.754,-1.446,Control
s25,-3.540,-3.358,Control
s26,-3.707,-0.993,Control
s27,-4.496,-2.653,Control
s28,-5.434,-0.686,Control
s29,-3.361,-0.686,Control
s30,-3.134,-1.488,Control
s31,-4.477,-0.389,Control
s32,-4.071,-2.152,Control
s33,-2.178,-1.327,Control
s34,-4.488,0.424,Control
s35,-2.203,0.006,Control
s36,-3.156,-1.398,Control
s37,-5.637,-1.641,Control
s38,-5.004,-1.330,Control
s39,-5.246,-2.077,Control
s40,-5.092,-1.800,Control
s41,3.987,-0.071,Treated
s42,5.038,2.142,Treated
s43,4.117,-0.325,Treated
s44,4.609,-0.030,Treated
s45,5.298,1.240,Treated
s46,3.283,-0.911,Treated
s47,3.994,-0.783,Treated
s48,3.597,0.919,Treated
s49,4.636,1.778,Treated
s50,3.204,1.375,Treated
s51,3.303,0.978,Treated
s52,3.110,1.562,Treated
s53,3.211,0.335,Treated
s54,4.760,-0.756,Treated
s55,6.564,0.455,Treated
s56,4.051,0.258,Treated
s57,3.473,2.593,Treated
s58,2.116,0.366,Treated
s59,3.970,-0.351,Treated
s60,2.563,-0.650,Treated
s61,3.059,1.567,Treated
s62,3.653,-0.529,Treated
s63,2.810,2.701,Treated
s64,5.906,2.268,Treated
s65,2.307,1.583,Treated
s66,1.906,0.073,Treated
s67,4.820,0.600,Treated
s68,4.101,0.989,Treated
s69,4.377,2.554,Treated
s70,3.581,0.141,Treated
s71,5.023,-1.710,Treated
s72,3.439,-0.018,Treated
s73,5.723,0.566,Treated
s74,4.640,0.447,Treated
s75,4.249,-1.002,Treated
s76,2.970,-0.257,Treated
s77,5.038,0.619,Treated
s78,2.606,1.222,Treated
s79,4.941,1.759,Treated
s80,2.984,1.211,Treated
s81,1.161,5.026,Knockout
s82,2.165,4.304,Knockout
s83,0.547,4.321,Knockout
s84,1.605,4.043,Knockout
s85,1.667,5.468,Knockout
s86,-0.497,3.499,Knockout
s87,-0.532,5.802,Knockout
s88,2.196,5.193,Knockout
s89,0.122,6.783,Knockout
s90,-0.175,5.551,Knockout
s91,1.336,4.212,Knockout
s92,-1.321,2.739,Knockout
s93,0.283,3.656,Knockout
s94,-0.754,4.941,Knockout
s95,1.265,6.530,Knockout
s96,-1.217,4.723,Knockout
s97,0.261,4.162,Knockout
s98,1.447,4.748,Knockout
s99,0.854,3.771,Knockout
s100,2.388,3.814,Knockout
s101,0.559,5.691,Knockout
s102,1.710,3.978,Knockout
s103,0.658,5.671,Knockout
s104,-0.437,4.815,Knockout
s105,-0.059,5.266,Knockout
s106,0.103,6.094,Knockout
s107,-0.008,4.495,Knockout
s108,-0.201,3.291,Knockout
s109,0.906,5.006,Knockout
s110,2.695,2.679,Knockout
s111,-0.577,3.921,Knockout
s112,2.589,4.134,Knockout
s113,-1.635,3.770,Knockout
s114,0.381,5.708,Knockout
s115,-0.011,4.487,Knockout
s116,1.702,5.370,Knockout
s117,0.443,4.854,Knockout
s118,-1.978,3.358,Knockout
s119,-0.914,3.327,Knockout
s120,-0.455,3.229,Knockout

120 samples parsed

X axisY axisLabel top NTitle

Extra samples to label

Control 40 · Treated 40 · Knockout 40

Example PCA plots

The first four are exact engine renders from score tables. The last two are AI illustrations of Illustration mode.

View:

Three groups on PC1 vs PC2

Exact engine render — pasted PC scores, colored by group.

pcascoresgroups

Tumor vs normal

Exact engine render — two-group separation on PC1.

rna-seqtwo-groups

Batch clouds

Exact engine render — QC view of batch structure.

qcbatch

Labeled outliers

Exact engine render — farthest samples labeled.

outlierslabels

AI illustration — groups

AI illustration of Illustration mode — visual mockup, not plotted from scores.

aiillustration

AI illustration — RNA-seq

AI illustration of Illustration mode — visual mockup, not plotted from a matrix.

aiillustrationrna-seq

What is a PCA plot?

A PCA plot here means a score scatter: each sample is a point on PC1 versus PC2 (or any two components you paste). Points that sit together share similar multivariate profiles. This page draws that figure from a table of already-computed scores. It does not run principal component analysis, estimate loadings, or draw a biplot with arrows. It is also not a generic scatter-plot maker and not a “what is PCA” explainer.

Score plot versus biplot

A score plot is samples in PC space. A PCA biplot overlays variable loadings as arrows on the same axes. This generator is the score plot — paste sample, PC1, PC2, and an optional group column.
If you still need to compute PCs, do that in R, Python, or your RNA-seq pipeline, then paste the score matrix here for a clean SVG.
For differential expression of the same experiment, pair this with the volcano plot generator or the MA plot generator.

What table to paste

A header plus sample, PC1, and PC2 is enough. A group, cluster, or condition column colors the points. Comma, tab, or semicolon separated text all work. Files are truncated to 8,000 rows. Axis titles default to PC1 and PC2; type variance percentages yourself if you want them on the figure.

Export

Download a scalable SVG with no watermark and no account required for the precise plot. The AI Illustration tab is only for slides when you do not have scores yet — it will not match your samples.

Frequently Asked Questions

Try it free

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What is a PCA plot?

Score plot versus biplot

A score plot is samples in PC space. A PCA biplot overlays variable loadings as arrows on the same axes. This generator is the score plot — paste sample, PC1, PC2, and an optional group column.

If you still need to compute PCs, do that in R, Python, or your RNA-seq pipeline, then paste the score matrix here for a clean SVG.

For differential expression of the same experiment, pair this with the volcano plot generator or the MA plot generator.