Metabolite–Pathway Enrichment Sankey Generator
Use Metabolite–Pathway Enrichment Sankey Generator
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Upload a fixed CSV template to generate a two-column metabolite → pathway enrichment Sankey, with a matching enrichment bubble panel on the right (x-axis = -Log(Pvalue), bubble area maps Count, red shade maps Hit Ratio). Ribbons use one flat light-gray tone at ~0.6 opacity so overlaps darken naturally; every node label sits to the LEFT of its bar, right-aligned and vertically centered with no pill; ~50 pastel left metabolites feed 11 gem-toned pathway nodes. Light / dark canvas with PNG / SVG export.
About this tool
Metabolomics often hides which pathway the hits actually enrich. This chart chains metabolite → pathway into one traceable picture: ~50 pastel metabolites flow through flat light-gray ribbons (~0.6 opacity, overlaps darken) into 11 gem-toned pathways; every label sits left of its bar, right-aligned and centered, with no pill; the right bubble panel aligns row-by-row — x = -Log(Pvalue) for significance, area = Count, red shade = Hit Ratio. Built for metabolomics, biomarker studies and academic enrichment overviews.
- Export crisp PNG / SVG you can keep editing
- Multiple built-in themes, one-click switch
- Light and dark canvas support
- Free, no watermark, no limits
Compared with general charting workflows
| Aspect | This tool | Typical alternative |
|---|---|---|
| Visual direction | Optimized for a specific chart grammar | More general, but more setup |
| Export | Fast PNG/SVG export first | More formats, but heavier workflow |
| Best fit | Slides, landing pages, and social visuals | Building bespoke analytical charts from scratch |
Docs
What is a metabolite–pathway enrichment Sankey?
A Metabolite–Pathway Enrichment Sankey is the go-to chart in metabolomics pathway enrichment analysis that links differentially abundant metabolites to the biological pathways they are significantly enriched in. The figure has exactly two columns: the left holds individual metabolites (amino acids, sugars, fatty acids, nucleotides), and the right holds the pathways they map to (glycolysis, TCA cycle, purine metabolism, fatty acid oxidation). Each ribbon means “one metabolite maps to one pathway”; the wider the ribbon, the stronger the mapping. This makes it easy to see which metabolites jointly drive a few core pathways.
The visual language mirrors academic KEGG enrichment figures: pastel left-end metabolites, and most notably flat light-gray ribbons drawn at ~0.6 opacity, so overlapping flows darken naturally without distracting from the pathway structure. Every node label sits to the left of its bar, right-aligned and vertically centered, with no pill background, so labels and ribbons never fight for space.
Features
- Two-column enrichment + bubble panel: the left Sankey maps metabolites to pathways, while a right panel aligns row-by-row with the pathway column — bubble x maps -Log(Pvalue) (significance), bubble area maps Count (mapped metabolite number), and red shade maps Hit Ratio (hit rate), so one figure shows significance and scale together
- Template driven, auto-layered: download the CSV template, fill five columns, upload, and the tool automatically treats source as left metabolites and target as right pathways
- Uniform light-gray ribbons: one flat gray tone at ~0.6 opacity; overlaps deepen naturally for a restrained, academic look
- Left-aligned labels: every node label sits to the left of its bar, right-aligned and vertically centered, no background
- Two-zone coloring: ~50 pastel left metabolites feed ~11 gem-toned pathway nodes, separating structure from content
- Color + size encoding: Hit Ratio with a light-pink-to-red gradient, Count with bubble area, plus two vertical legends
- Light / dark canvas and PNG / SVG export: toggle canvas style, then export high-res PNG (2x) or editable SVG
- Privacy-first: all data is parsed and rendered locally in the browser, never uploaded
Use cases
- Metabolomics enrichment: map significant metabolites to KEGG and other pathway libraries, highlighting core enriched pathways and significance order
- Biomarker and disease research: serum, tissue or cell metabolite shifts linked to energy or lipid metabolism route clues
- Gene / protein / drug association: any molecule-to-pathway mapping structure works, with p-value and scale shown on the right
- Papers and omics reports: a clean enrichment overview for metabolomics manuscripts or array reports
- Pathway-level data stories: show both which individuals enter a set and that set’s significance and scale
Steps
- Click “Download CSV template” to get the sample (~50 mappings across 11 pathways with enrichment metrics)
- Fill your data as
source,target,value,logp,hitratio - Click the upload zone or drag the CSV file onto the page
- The two-column Sankey and the bubble panel render automatically; toggle light / dark
- Click “Download PNG” or “Download SVG” to save
Data format
| Field | Required | Meaning |
|---|---|---|
| source | yes | Left metabolite name (out only) |
| target | yes | Right pathway name (in only) |
| value | yes | Mapping strength / Count (positive) — ribbon width and bubble area |
| logp | yes | -Log(Pvalue) (significance) — bubble x position |
| hitratio | yes | Hit rate 0–1 — bubble red shade |
Limits: up to 200 rows; at most 100 left metabolites and 30 right pathways. Data must be a one-way “metabolite → pathway” structure; circular flows trigger a warning.
Tip: the same name is always one node. If you must distinguish same-named metabolites, add a prefix.
How to read it
Read the vertical order of the right bubble panel first: a bubble farther right means a larger -Log(Pvalue) (more significant), a bigger bubble means more mapped metabolites (higher Count), and a deeper red means a higher Hit Ratio. Then trace the left Sankey ribbons: a highly significant pathway points straight to which metabolites drive it, and a metabolite that maps to several pathways is a shared hub. Hover any bubble or ribbon for exact values.
About Gotokit
Gotokit builds simple, efficient and beautiful online tools. This enrichment Sankey generator runs on the ECharts engine — no sign-up, open-and-use, fully local data handling for your privacy.
FAQ
How many columns does the CSV need? Where does the bubble panel data come from?
Five fixed columns: source,target,value,logp,hitratio. value sets the ribbon width and is also that pathway's Count (bubble area); logp and hitratio set the bubble's x and red shade for that pathway — fill them on any one row per pathway and keep them consistent across its rows. Up to 200 rows.
Why are the ribbons gray and translucent?
That is the restrained academic look of KEGG enrichment figures. One flat gray ribbon draws no attention away from the pathway structure, and at ~0.6 opacity overlapping flows darken naturally into layers — clearer than flat single color.
What does each bubble in the panel mean?
Each bubble is one right-column pathway: horizontal position = -Log(Pvalue) (farther right = more significant; read the 0.5/1.0/1.5/2.0 axis), area = that pathway's Count, red shade = Hit Ratio (light pink to solid red ≈ 0.2-0.5). Hover to see the exact logp, Count and Hit Ratio.
Is this chart tool better for slides, reports, or social visuals?
All three can work, but each needs a different emphasis. Slides care about legibility, reports care about structure and annotation, and social visuals care about density and style. Tune colors, labels, and export size for the destination.
When should I export PNG instead of SVG?
PNG is usually better for slides, chats, or social sharing, while SVG is better if you need to keep editing, scale for print, or embed the graphic in a design system. If the target platform does not support SVG, export a higher-resolution PNG.
How is this different from a general charting tool?
These tools are optimized for a specific visual grammar, so you can get to a strong result faster. General charting tools are more flexible across many chart families, but usually take more setup and styling work.
Can I use this tool on mobile phones or tablets?
Most tools work in modern mobile browsers, but complex charts, larger files, and precision drag-and-drop tasks usually feel better on desktop. If speed and control matter, desktop is the safer choice.
Is this tool better for one-off tasks or recurring workflows?
It is strongest for fast one-off tasks, quick verification, and temporary exports. If you need long-term automation, team collaboration, or deeper integration, you will usually pair it with desktop software, scripts, or a larger system.
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