How do I make a UMAP plot from coordinates?

Paste or upload a table with cell, UMAP1, and UMAP2 (and cell type if you have it). The scatter renders immediately. Label a few cells if needed and download the SVG.

Does this run UMAP for me?

No. It plots embeddings you already computed in Scanpy, Seurat, or another pipeline. It will not build a neighbor graph or choose min_dist.

How is this different from a PCA plot?

PCA plots linear sample scores with origin lines. UMAP plots a nonlinear embedding, usually of cells, without treating (0,0) as special. Use the PCA plot generator for PC1 vs PC2.

Can I color points by cell type or cluster?

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

Is this a t-SNE plot?

No. t-SNE is a different embedding. This page only reads UMAP1/UMAP2 (or umap_1 / umap_2) columns.

Is the UMAP 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

UMAP Plot Generator for cell embeddings

Free UMAP plot generator. Paste UMAP1 and UMAP2 coordinates to draw an exact embedding scatter and download SVG.

Paste cell, UMAP1, UMAP2Color by cell type or clusterLabel a few cellsDownload a watermark-free SVG — free

Paste cell, UMAP1, UMAP2 coordinates — renders an exact embedding scatter as SVG, free

UMAP coordinate table

cell, UMAP1, UMAP2, and optional group. This plots embeddings you already computed — it does not run UMAP.

cell,UMAP1,UMAP2,celltype
c1,1.574,7.979,T cell
c2,1.896,7.297,T cell
c3,1.678,8.736,T cell
c4,1.852,8.633,T cell
c5,2.033,7.913,T cell
c6,2.598,7.950,T cell
c7,2.605,8.439,T cell
c8,1.604,9.179,T cell
c9,2.622,8.872,T cell
c10,1.957,7.918,T cell
c11,2.398,8.522,T cell
c12,2.951,9.015,T cell
c13,1.559,7.591,T cell
c14,2.567,8.850,T cell
c15,1.736,8.320,T cell
c16,1.791,8.272,T cell
c17,2.243,7.670,T cell
c18,2.638,9.103,T cell
c19,1.823,7.706,T cell
c20,2.909,8.207,T cell
c21,2.840,7.927,T cell
c22,1.359,9.529,T cell
c23,1.486,9.297,T cell
c24,1.794,8.859,T cell
c25,1.773,8.641,T cell
c26,1.757,7.874,T cell
c27,1.508,8.863,T cell
c28,2.501,7.799,T cell
c29,2.127,8.260,T cell
c30,1.969,8.138,T cell
c31,1.703,8.475,T cell
c32,2.022,7.370,T cell
c33,2.521,8.692,T cell
c34,2.616,8.782,T cell
c35,2.021,8.882,T cell
c36,8.450,3.336,B cell
c37,10.212,2.973,B cell
c38,9.334,3.205,B cell
c39,9.759,2.598,B cell
c40,8.388,4.133,B cell
c41,9.012,2.901,B cell
c42,8.716,2.642,B cell
c43,9.064,3.019,B cell
c44,8.904,2.667,B cell
c45,10.385,3.415,B cell
c46,9.548,2.388,B cell
c47,9.076,3.261,B cell
c48,9.055,3.664,B cell
c49,9.760,2.840,B cell
c50,8.707,2.900,B cell
c51,9.166,3.244,B cell
c52,8.328,1.577,B cell
c53,9.196,3.407,B cell
c54,7.800,2.994,B cell
c55,9.166,2.746,B cell
c56,9.314,2.332,B cell
c57,9.677,3.757,B cell
c58,10.218,2.507,B cell
c59,9.962,3.582,B cell
c60,9.919,2.530,B cell
c61,8.343,3.804,B cell
c62,8.542,2.966,B cell
c63,8.538,2.178,B cell
c64,9.041,2.491,B cell
c65,9.191,3.474,B cell
c66,9.034,3.221,B cell
c67,9.246,2.602,B cell
c68,8.749,3.329,B cell
c69,8.898,3.755,B cell
c70,8.841,3.941,B cell
c71,6.558,11.027,Myeloid
c72,5.990,11.541,Myeloid
c73,6.511,11.035,Myeloid
c74,6.839,11.492,Myeloid
c75,6.429,11.817,Myeloid
c76,6.706,11.730,Myeloid
c77,7.875,10.712,Myeloid
c78,6.018,11.213,Myeloid
c79,6.939,11.394,Myeloid
c80,5.929,11.839,Myeloid
c81,5.268,10.898,Myeloid
c82,6.628,11.117,Myeloid
c83,7.096,11.002,Myeloid
c84,5.331,11.395,Myeloid
c85,7.602,11.785,Myeloid
c86,7.002,10.784,Myeloid
c87,6.947,10.532,Myeloid
c88,6.303,11.664,Myeloid
c89,6.673,11.953,Myeloid
c90,6.965,11.857,Myeloid
c91,7.064,11.579,Myeloid
c92,7.173,11.483,Myeloid
c93,7.074,12.427,Myeloid
c94,5.633,12.166,Myeloid
c95,6.407,11.751,Myeloid
c96,7.438,11.233,Myeloid
c97,6.087,10.928,Myeloid
c98,5.789,10.962,Myeloid
c99,6.182,10.687,Myeloid
c100,7.106,10.692,Myeloid
c101,5.543,10.505,Myeloid
c102,6.732,11.331,Myeloid
c103,6.268,11.375,Myeloid
c104,7.603,10.200,Myeloid
c105,6.626,12.009,Myeloid
c106,11.100,9.076,NK cell
c107,12.972,10.258,NK cell
c108,11.142,9.511,NK cell
c109,11.357,9.837,NK cell
c110,10.880,9.180,NK cell
c111,12.259,9.469,NK cell
c112,11.232,9.566,NK cell
c113,12.736,10.524,NK cell
c114,10.979,10.236,NK cell
c115,11.701,9.116,NK cell
c116,12.366,9.568,NK cell
c117,11.853,9.455,NK cell
c118,12.637,9.490,NK cell
c119,13.054,10.079,NK cell
c120,11.667,9.060,NK cell
c121,12.683,9.423,NK cell
c122,11.263,8.736,NK cell
c123,12.143,9.301,NK cell
c124,12.509,9.003,NK cell
c125,11.116,9.056,NK cell
c126,11.554,9.599,NK cell
c127,11.058,9.356,NK cell
c128,12.633,9.873,NK cell
c129,11.738,8.908,NK cell
c130,11.174,9.812,NK cell
c131,11.905,9.584,NK cell
c132,10.330,10.358,NK cell
c133,11.680,9.503,NK cell
c134,13.395,10.394,NK cell
c135,13.341,9.545,NK cell
c136,11.385,9.476,NK cell
c137,10.777,9.326,NK cell
c138,11.650,10.173,NK cell
c139,12.281,9.754,NK cell
c140,11.480,8.382,NK cell

140 samples parsed

X axisY axisLabel top NTitle

Extra samples to label

T cell 35 · B cell 35 · Myeloid 35 · NK cell 35

Example UMAP plots

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

View:

PBMC cell types

Exact engine render — pasted UMAP1/UMAP2, colored by cell type.

umapsingle-cellcelltype

Tumor vs stroma

Exact engine render — two islands on UMAP1 vs UMAP2.

tumortwo-clusters

Batch split

Exact engine render — QC view of leftover batch structure.

qcbatch

Labeled cells

Exact engine render — farthest cells labeled.

labelsoutliers

AI illustration — PBMCs

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

aiillustration

AI illustration — tumor

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

aiillustrationtumor

What is a UMAP plot?

A UMAP plot here is an embedding scatter: each cell or sample is a point on UMAP1 versus UMAP2. Nearby points were neighbors in the original high-dimensional space. This page draws that figure from a table of already-computed coordinates. It does not run UMAP, estimate neighbors, or choose n_neighbors / min_dist. It is not a PCA score plot, not a t-SNE plot, and not a “what is UMAP” explainer.

UMAP versus PCA

PCA is a linear score scatter of samples. UMAP is a nonlinear embedding, often used for single-cell islands. Paste PC scores into the PCA plot generator and UMAP coordinates here.
t-SNE is a different embedding. This page only plots UMAP1/UMAP2 columns you already have.
This tool does not recompute the embedding. Changing colors or labels does not change the layout.

What table to paste

A header plus cell (or sample), UMAP1, and UMAP2 is enough. A cell type, cluster, or group column colors the points. Comma, tab, or semicolon separated text all work. Files are truncated to 8,000 rows.

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 coordinates yet — it will not match your cells.