Heatmap Generator
Paste an expression matrix and get a clustered, z-scored heatmap with hierarchical clustering of rows and columns. Read it on screen and download a publication-ready SVG.
Drag & drop a file or
CSV, TSV, Excel (.xlsx/.xls) - max 120 rows
Turning a Matrix of Numbers Into a Picture of Structure
A gene expression heatmap is the workhorse figure of transcriptomics because it makes a matrix of genes by samples legible at a glance. Color encodes value, and once the rows and columns are ordered sensibly, co-regulated genes and related samples snap into visible blocks. The technique became standard after Eisen et al. (1998) paired hierarchical clustering with a colored grid to display genome-wide expression.
Two processing choices determine whether the figure is honest. The first is scaling. Genes span orders of magnitude in absolute expression, so without standardization a handful of highly expressed genes wash out everything else. Z-scoring each row, so color reflects deviation from that gene's own mean, is the convention for expression heatmaps and is why the scale here is diverging: red above the row mean, blue below.
The second is clustering. Hierarchical agglomerative clustering measures the distance between every pair of rows, then repeatedly merges the closest clusters, producing an ordering that places similar genes adjacent. Doing the same on columns groups similar samples. This tool clusters both with average linkage on Euclidean distance, the same defaults used by common heatmap packages, and lets you turn column clustering off when the sample order is meaningful, such as a time course.
A heatmap displays structure but does not test it: apparent blocks should be confirmed with a formal analysis, and the genes shown are usually a pre-selected significant set rather than the whole transcriptome. Select that set with the volcano plot generator and the false discovery rate calculator, normalize inputs with the RNA-seq normalization calculator, and for the complete analysis with a proper statistical model, the bioinformatics analysis service delivers the figure and the statistics behind it.
A Worked Example: What Row Z-scoring Actually Does
Two genes are measured across two control and two treatment samples. TP53 sits near an expression level of 2 in the controls and 9 in the treatments; MYC does the opposite, starting near 8 and dropping to 2. Their raw magnitudes are very different, so on an unscaled color scale TP53's low control values would look uniformly dark. Z-scoring each row, subtracting the gene's own mean and dividing by its standard deviation, puts both on one scale so their patterns become directly comparable.
| Gene | Ctrl1 | Ctrl2 | Treat1 | Treat2 | Row mean |
|---|---|---|---|---|---|
| TP53 | 2.1 | 2.4 | 8.9 | 9.2 | — |
| −1.04 | −0.95 | +0.95 | +1.04 | ||
| MYC | 8.2 | 7.9 | 1.8 | 2.1 | — |
| +1.05 | +0.95 | −1.05 | −0.95 |
The italicized second row under each gene is its z-score. TP53 reads blue in the controls and red in the treatments; MYC reads the exact mirror. Both now range across roughly −1 to +1 despite their opposite raw levels, so a reader sees at a glance that TP53 is induced by treatment while MYC is repressed, an inverse pair. Clustering would then place these two genes apart and group each with others sharing its direction. Without row scaling, MYC's high control values would simply look bright everywhere and the pattern would be muddied.
Common Mistakes When Building a Heatmap
- Not scaling rows. Without per-gene standardization, a few highly expressed genes dominate the color scale and drown out the patterns in everything else. Z-score by row so the map shows relative change, unless you specifically need absolute magnitudes.
- Clustering an ordered axis. Reordering columns by similarity destroys a meaningful order such as a time course or a dose series. Turn column clustering off when the sequence of samples carries information.
- Heatmapping raw counts. Color built from un-normalized counts reflects library size as much as expression. Normalize first, for example to TPM with the RNA-seq normalization calculator, then log-transform before scaling.
- Plotting thousands of rows unfiltered. A heatmap of every gene is an uninterpretable wash. Select the differentially expressed or most variable genes first, so the blocks that carry the story are visible.
- Reading the color scale backwards. After row z-scoring, red and blue mean above and below each gene's own mean, not high and low absolute expression. State the scaling in the figure legend so the colors are not misread.
How to Use This Generator
Paste your matrix
A header row of sample names, then one row per gene with a label and its values.
Choose scaling
Z-score by row to compare genes on the same scale, the usual choice for expression data.
Cluster
Turn on row and column clustering to reorder similar genes and samples into visible blocks.
Export
Download the labelled heatmap as a publication-ready SVG.
Next step
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Frequently Asked Questions
What is a heatmap in gene expression analysis?
A heatmap is a grid where each cell's color encodes a numeric value, used in gene expression analysis to show a matrix of genes by samples at a glance. Rows are usually genes and columns are samples, with color representing expression level, so blocks of similar color reveal groups of genes that behave alike across conditions. It is the standard figure for presenting the results of a clustering or differential-expression analysis.
What does a heatmap indicate?
A heatmap indicates the pattern of values across a whole matrix through color, so at a glance you see which genes are high or low in which samples and where blocks of similar behavior lie. In gene expression it indicates co-regulated gene modules and groups of related samples: a red block marks genes elevated together in one set of conditions, and a mirrored blue block marks where they are suppressed. After row standardization, the color indicates relative level, above or below each gene's own average, rather than raw magnitude.
How do I interpret a heatmap?
Read color as value, then look for rectangular blocks of consistent color: a block of red where a set of genes is high in a group of samples, with the mirrored blue block elsewhere, marks a co-regulated gene module tied to a condition. When the rows and columns are clustered, their order reflects similarity, so neighbors are alike and the dendrogram shows how tightly. With row z-scoring, red means above the gene's own mean and blue below, so you are reading each gene's pattern across samples, not comparing one gene's brightness to another's.
How do you visualize gene expression?
The choice depends on the question. To show a whole matrix of genes by samples, a clustered heatmap is the standard figure. To show which genes changed between two conditions, a volcano plot places effect size against significance. To show how samples relate overall, a PCA plot projects them into two dimensions. For a single gene across groups, a boxplot is clearer than any of these. Whatever the display, normalize the counts first so the picture reflects biology rather than sequencing depth.
How do I make my own heat map?
Arrange your data as a matrix with genes in rows and samples in columns, standardize each gene so its values are comparable, then color each cell and optionally reorder rows and columns by similarity through clustering. This generator does all of that: paste or import the matrix with a header row of sample names, and it z-scores each gene, clusters the rows and columns, and renders a colored, labelled heatmap you can download.
Why do you z-score a heatmap by row?
Genes are expressed at very different baseline levels, so without standardization a few highly expressed genes dominate the color scale and subtle patterns disappear. Z-scoring each row, subtracting the gene's mean and dividing by its standard deviation, puts every gene on the same scale so the heatmap shows relative change across samples rather than absolute magnitude. Red then means above the gene's own average and blue means below.
What does clustering do in a heatmap?
Hierarchical clustering reorders the rows and columns so that similar genes and similar samples are placed next to each other, turning a scrambled grid into visible blocks. Genes that rise and fall together end up adjacent, and samples from the same condition group together, which is often the main insight of the figure. This tool clusters rows and columns independently using average-linkage agglomerative clustering on Euclidean distance.
Should I cluster rows, columns, or both?
It depends on the question. Clustering rows groups co-expressed genes and is almost always useful. Clustering columns groups similar samples, which is valuable for discovering or confirming sample structure but should be turned off when the column order is meaningful, for example an ordered time course. This tool lets you toggle row and column clustering independently.
Related Analysis Tools
To select the significant genes to display, use the volcano plot generator and the false discovery rate calculator. To see relationships between samples as a projection, the PCA plot generator reduces dimensions, and the correlation matrix generator shows pairwise similarity. For the full analysis behind the figure, the bioinformatics analysis service delivers publication-ready results.
Reviewed by
Dr. Sarah Mitchell
Dr. Sarah Mitchell holds a PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health and has over 15 years of experience in systematic review methodology and meta-analysis. She has authored or co-authored 40+ peer-reviewed publications in journals including the Journal of Clinical Epidemiology, BMC Medical Research Methodology, and Research Synthesis Methods. A former Cochrane Review Group statistician and current editorial board member of Systematic Reviews, Dr. Mitchell has supervised 200+ evidence synthesis projects across clinical medicine, public health, and social sciences. She reviews all Research Gold tools to ensure statistical accuracy and compliance with Cochrane Handbook and PRISMA 2020 standards.
The Tool Is Free. The Full Analysis of Your Biological Data? We Handle That.
Our PhD statisticians run the complete pipeline: differential expression with multiple-testing correction, survival modelling, dimensionality reduction, and publication-ready figures with a reproducible methods section. Constant pricing, most projects delivered in under two weeks.
Our promise: Free pipeline re-run and figure revisions if reviewers push back.