Single-cell Embedding

Single-cell Embedding is an AI Tool that creates numeric vectors for cells in an AnnData .h5ad dataset.

The first supported backend is UCE 4-layer.

What it does

The tool reads a single-cell dataset and asks UCE to compute one embedding vector per cell. Similar cells should usually have more similar vectors, so the output can be useful for exploration, visualization, clustering, and downstream analysis.

This is different from annotation. Annotation gives cells labels. Embedding creates a representation that other tools can inspect.

Inputs

Input	Meaning
AI Model	The installed UCE 4-layer AI Model
AnnData file	.h5ad file from Data
Species	Organism matching the dataset
Batch size	Number of cells processed together
CSV preview rows	Number of cells exported to the lightweight CSV preview

The AnnData .X matrix should contain scRNA-seq counts, and var_names should contain gene symbols.

Outputs

Output	Meaning
Embedded AnnData	Main .h5ad artifact with obsm["X_uce"]
Embedding preview CSV	Small table for quick inspection
Summary JSON	Counts, dimensions, settings, warnings, and provenance
Intermediate files	UCE processing artifacts, marked as intermediate

Use the embedded AnnData file for downstream scientific work. The CSV preview is only meant for quick inspection.

In pipelines

Use this tool when you want:

• h5ad -> single-cell embedding -> viewer/report;
• h5ad -> embedding -> downstream script/plugin;
• a reusable cell-embedding step before visualization.

In the pipeline editor, the AI Model dropdown only shows compatible installed models.

Reading the result

Check:

• cell count and gene count;
• embedding dimensions;
• warnings about input format;
• provenance;
• whether the species matches the dataset.

Embeddings are not a final biological conclusion. They are a representation to inspect, compare, and validate.