Single-cell atlas
Build a single-cell RNA-seq atlas from raw or processed data — QC, clustering, scalable sketch analysis, batch integration, cell type annotation, marker identification, pseudobulk differential expression, trajectory, and pathway enrichment with reproducible history.
Research question
What cell populations exist in this dataset, what genes define each cluster, how do cell states change across trajectory or condition, and how does expression differ between experimental groups with biological replicates?
Who this is for
- Wet-lab biologists running their first single-cell analysis without local Seurat/Scanpy setup
- Computational biologists who want transparent parameters and checkpoint provenance
- Core facilities delivering standard QC → clustering → marker → enrichment packages to client labs
Data requirements
| Data | Required | Purpose |
|---|---|---|
.h5ad or convertible 10x/CSV | Yes | AnnData input for all pipeline steps |
Gene symbols in var | Recommended | GO enrichment compatibility |
| Metadata with condition labels | No (required for contrast DE) | Group comparison on Data page |
| Saved contrasts | No (required for contrast DE) | Treated vs. control comparisons |
Workflow
Data → QC → Normalization → Clustering → Integration / Annotation / Trajectory → DE or Pseudobulk DE → Enrichment → History snapshot
Step 1 — Upload and prepare
Create a single-cell study and upload an .h5ad file (or convert 10x/CSV with Convert on the Data page). Save metadata on Data → Metadata so columns merge into obs before analysis runs.
Define contrasts on the Data page when condition-level DE is needed.
Step 2 — Find structure
Open Analyze → Find Structure and run in order:
- QC — filter by genes/cells detected, counts, and mitochondrial fraction
- Normalization — log-normalize and select highly variable genes
- Clustering — Leiden clustering and UMAP embedding
For large datasets, run Integrate / Scale → Sketch & Scale to create a representative sketch and projected full-dataset labels before deep exploratory work.
Re-running an upstream step invalidates downstream checkpoints and marks dependent results stale.
Step 3 — Explore
Review outputs before formal comparison:
- Explore → Sample QC — retention histograms and per-sample metrics
- Explore → Embedding — interactive UMAP colored by cluster or metadata column
Step 4 — Compare groups
Under Analyze → Compare Groups:
| Mode | When to use |
|---|---|
| Cluster markers | Genes enriched in each cluster vs. all others |
| Contrast | Groups defined on the Data page (e.g. treated vs. control) |
| Pseudobulk DE | Replicate-aware DE after aggregating cells by sample plus cell type or cluster |
Run Pathway enrichment on DE results with ORA for thresholded gene lists, ranked GSEA for full DE tables, or ssGSEA/GSVA for sample- or cell-level gene-set scores.
Step 5 — Integrate, annotate, and order cells
Under Analyze → Integrate / Scale:
- Batch Integration corrects batch-driven PCA shifts and reports before/after diagnostics.
- Cell Type Annotation assigns auditable cluster labels from the built-in PBMC marker reference with scores and supporting genes.
- Trajectory & Cell Cycle scores S/G2M programs and computes graph-based pseudotime from a selected root cluster or cell.
- Sketch & Scale stores large-dataset Zarr/Parquet artifacts and downsampled browser payloads.
Step 6 — Interpret and preserve history
- Interpret → Enrichment — GO term tree and pathway context
- History → Snapshots — freeze parameter set and run IDs for reproducibility
Step 7 — AI interpretation (optional)
Use analytical interpretation on completed DE or enrichment runs for cluster annotations and pathway narratives. If Interpret isn't available, ask your administrator to enable AI interpretation.
Expected outputs
- Filtered AnnData checkpoint for the run
- UMAP embedding with Leiden cluster labels
- Integrated PCA/UMAP-ready checkpoint with batch diagnostics
- Cell type annotation table with scores, confidence, and supporting markers
- Cell-cycle phase and pseudotime summaries
- Ranked DE table with log fold change and adjusted p-values
- Pseudobulk DE table by sample and cell type or cluster when biological replicates are available
- GO enrichment results with term hierarchy
- Analysis snapshot with completed run IDs and parameters
Typical analyses
| Analysis | Comparison | Question |
|---|---|---|
| Cell type discovery | Cluster markers | What genes define each population? |
| Treatment response | Contrast DE | Which genes change after perturbation? |
| Replicate-aware response | Pseudobulk DE | Which cell types show sample-level treatment effects? |
| Multi-sample atlas | Batch Integration + Annotation | Are cell labels stable after batch correction? |
| Differentiation or activation | Trajectory & Cell Cycle | How are cell states ordered along pseudotime? |
| Immune infiltration | Condition on metadata | Are T cell clusters expanded in responders? |
| Core client delivery | Full pipeline + snapshot | Reproducible deliverable for requesting lab |