---
title: Scanpy quickstart
description: Run a GPU-accelerated single-cell analysis on the platform in a few steps.
---

You have single-cell RNA-seq counts in an AnnData file and you want clustered cells, a UMAP, and per-cluster marker genes. [Scanpy](/docs/scanpy) on the platform runs the standard pipeline — QC → normalize → highly variable genes → PCA → neighbors → UMAP → Leiden → differential expression — on a GPU via rapids-singlecell, so even large datasets finish in minutes. This walk-through takes you from an `.h5ad` file to exported results.

## Before you start

The platform's Scanpy job reads an **AnnData `.h5ad`** file from storage — either an S3 URI or an HTTPS URL ending in `.h5ad`. The full GPU pipeline runs on a T4 GPU; the batch-correction, annotation, and scoring extensions run on CPU. Runs are asynchronous with a 10-minute timeout.

## 1. Prepare your data

Upload an AnnData (`.h5ad`) file, or reuse one produced by an upstream job. If you have 10X Cell Ranger output or a count matrix, convert it to `.h5ad` first — that's the format the pipeline expects.

## 2. Choose a workflow

Pick what this run should do:

- **`full`** (default) — the complete pipeline through clustering and differential expression.
- **`qc`** — quality control and filtering only.
- **`clustering`** — QC through Leiden clustering and UMAP, without marker genes.
- **`de`** — differential expression / marker genes.

## 3. Set quality-control thresholds

The defaults are a good starting point:

- **Minimum genes per cell** (`min_genes`, default 200) — removes empty droplets.
- **Maximum mitochondrial percent** (`max_mito_percent`, default 5) — removes dying cells.
- **Highly variable genes** (`n_top_genes`, default 2000) — focuses downstream steps on informative genes.
- **Clustering resolution** (`resolution`, default 1.0) — lower values give fewer, larger clusters.

## 4. Run the analysis

Submit the job. The pipeline runs on GPU and reports progress through its phases. You can keep working; you'll be notified when it completes.

## 5. Review and export

When the job finishes, a result card shows the UMAP with clusters plus cell, gene, and cluster counts. Download the processed AnnData (with all embeddings and cluster labels), the marker-gene tables, and the publication-ready figures.

> Start with the `full` workflow to see the whole picture, then re-run `de` or `clustering` at a different `resolution` if the cluster granularity doesn't match your biology — over-clustering is easier to merge than under-clustering is to split.

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