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Research Areas

Computational BiologySingle-cell, bulk RNA-seq, spatial, and biomarkers
CardiologyCardiovascular signals, cohorts, and interpretationImmunologyImmune cell states, signaling networks, and repertoire dynamicsNeurologyBrain connectivity, functional imaging, and electrophysiologyOncologyTumor microenvironment, immune response, mutations, and survivalPathologyWhole-slide imaging, tissue quantification, and spatial pathology

Application

Documentation

Documentation

Back to studies

Quick start

This walkthrough walks through a single-cell study from upload to snapshot.

1. Create a study

From the Studies dashboard, click New study. Choose Single-cell as the modality and give the study a name.

2. Upload a dataset

Go to Data → Datasets and upload an .h5ad file (or convert CSV / 10x data with Convert). For a small demo dataset, use a processed single-cell AnnData file with gene symbols in var.

3. Add metadata (optional)

On Data → Metadata, confirm sample and condition columns align with the dataset. Save metadata so it merges into the dataset before analysis runs.

4. Run the pipeline

Open Analyze → Find Structure and run steps in order:

  1. QC — filters low-quality cells
  2. Normalization — log-normalize and select highly variable genes
  3. Clustering — Leiden clustering and UMAP embedding

Then under Compare Groups, run Differential expression and Pathway enrichment.

5. Explore results

  • Explore → Embedding — interactive UMAP colored by cluster or metadata
  • Analyze → DE Results — volcano plot and gene table
  • Interpret → Enrichment — GO term tree

6. Save a snapshot

On Runs → Snapshots, create a snapshot to freeze the current parameter set and run IDs. You can restore from this snapshot later on the Analyze page.

Next steps