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Scanpy: Single-Cell Analysis

Overview

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis. Targets Scanpy 1.12.4 (released 2026-08-27), reviewed 2026-10-01. Native synthetic tests establish data/API contracts, not biological validity.

Installation

Requires Python 3.12+ (scanpy 1.12 dropped Python ≤3.11) and anndata ≥0.10.

uv pip install "scanpy[leiden]"

The [leiden] extra installs igraph and leidenalg; the scripts explicitly select flavor="igraph". For reproducible environments, pin a version: uv pip install "scanpy[leiden]==1.12.4".

For large or out-of-core datasets, many functions support [Dask](https://docs.dask.org/) arrays (experimental):

uv pip install "scanpy[leiden]" dask

See the [Using dask with Scanpy](https://scanpy.scverse.org/en/stable/tutorials/experimental/dask.html) tutorial. For GPU-accelerated scanpy-like operations, use [rapids-singlecell](https://rapids-singlecell.readthedocs.io/) as a separate package.

If the input is an R-native single-cell object (.rds, .RData, Seurat, or SingleCellExperiment), first convert it to .h5ad with R tooling, then load it with Scanpy. Read references/r_interop.md for agent-run installation and conversion instructions across macOS, Linux, and Windows.

For AnnData structure and I/O details, use the anndata skill. For probabilistic models and batch correction, use scvi-tools.

Representation and inference contracts

• Input to QC/preprocessing must be identified raw counts. The scripts reject negative,

fractional, nonfinite or zero-total cells. Use --counts-layer counts when X is already

normalized; never infer provenance from integer-looking values alone.

• layers["counts"] remains unnormalized; .raw is an independent full-gene

log-normalized snapshot. Gene subsetting also subsets every layer, but does not subset

.raw genes. Retain a full-gene object for pseudobulk; the full pipeline does so.

• seurat/cell_ranger HVGs use log-normalized values; seurat_v3 and

seurat_v3_paper use counts and require scanpy[skmisc]. Scaling/regression are optional,

may densify sparse data, and can remove biology along with covariates.

• Marker ranking defaults to .raw when present; --no-use-raw selects X and --layer

selects an explicit log-normalized layer. --groups/--reference control contrasts.

Wilcoxon/t-test adjusted p-values use BH within each comparison; logreg returns ranking

scores without p-values. Clusters chosen from the same data yield exploratory markers.

• Condition DE requires raw-count sums per sample and cell type, independent biological

replicates, aligned condition/donor metadata and a full-rank design. Analyze each cell

type separately; repeated donor samples require a suitable paired design. Aggregation

cannot fix confounding, missing replication, doublets or incorrect annotations.

• Harmony (harmonypy) changes PCA coordinates; BBKNN (bbknn) changes the graph;

ComBat changes X. Keep original expression/counts for DE, and assess biological

conservation and batch mixing together. Requested integration/doublet failures stop.

• The toolkit loads files into memory. For backed data, explicitly call .to_memory()

on a chosen subset and close the file; backed mode is not a general out-of-core

pipeline. Copy views before mutation. Dask support varies by function/array layout.

• .rds needs an explicit R conversion stage. Cluster IDs do not determine cell types:

all mappings/signature assets are illustrative human marker examples, requiring review.

Harmony 2 returns cells x PCs; the toolkit calls its native API because the

Scanpy 1.12.4 wrapper still transposes that output and fails with current Harmony.

Optional packages: scikit-image for Scrublet automatic thresholding, loompy for Loom,

scikit-misc for v3 HVGs, harmonypy==2.0.2/bbknn==1.6.0 for their integration branches, and louvain

for the deprecated Louvain option (separate environment: louvain 0.8.2 requires

igraph <0.12, conflicting with the tested igraph 1.0). Install only needed branches. R and optional integration

execution boundaries are recorded in [references/upstream-review.md](references/upstream-review.md).

When to Use This Skill

This skill should be used when:

• Analyzing single-cell RNA-seq data (.h5ad, 10X, CSV formats)
• Working with R-friendly single-cell datasets (.rds, .RData, Seurat, SingleCellExperiment) that need conversion to .h5ad
• Performing quality control on scRNA-seq datasets
• Creating UMAP, t-SNE, or PCA visualizations
• Identifying cell clusters and finding marker genes
• Annotating cell types based on gene expression
• Conducting trajectory inference or pseudotime analysis
• Generating publication-quality single-cell plots

Script Toolkit (prefer these over writing code from scratch)

This skill bundles ready-to-run CLI scripts in scripts/ for every common step. Run these instead of hand-writing scanpy code — they handle file loading by extension, figure setup, sensible defaults, raw-count preservation, and progress logging. Each reads and writes .h5ad, so they chain together, and each has its own --help. Only drop down to writing scanpy code when a task isn't covered by a script or needs unusual customization.

All scripts use a shared scripts/_common.py helper (loading, saving, figure config) — keep it alongside the others. Run from the skill directory or pass full paths; figures default to ./figures/.

| Script | Purpose | Typical call |

|--------|---------|--------------|

| run_pipeline.py | Full workflow in one command: load → QC → normalize → HVG → PCA → (batch) → UMAP → Leiden → markers | python scripts/run_pipeline.py raw.h5ad -o processed.h5ad |

| inspect_data.py | Summarize an unknown dataset (shape, obs/var, layers, what's already computed, raw vs normalized) | python scripts/inspect_data.py data.h5ad |

| convert.py | Load any format (10x dir/.h5, csv, loom, mtx) and write .h5ad | python scripts/convert.py 10x_dir/ -o data.h5ad |

| qc_analysis.py | QC metrics, before/after plots, filtering, optional Scrublet doublets | python scripts/qc_analysis.py raw.h5ad -o qc.h5ad --scrublet |

| preprocess.py | Normalize, log1p, HVG, optional scale/regress (keeps counts layer + raw) | python scripts/preprocess.py qc.h5ad -o norm.h5ad |

| reduce_dimensions.py | PCA + variance plot, neighbors, UMAP, optional t-SNE | python scripts/reduce_dimensions.py norm.h5ad -o red.h5ad |

| batch_correct.py | Integration: harmony / bbknn / combat | python scripts/batch_correct.py red.h5ad -o int.h5ad --method harmony --batch-key sample |

| cluster.py | Leiden (or louvain) at one or many resolutions | python scripts/cluster.py red.h5ad -o clu.h5ad --resolution 0.3 0.6 1.0 |

| find_markers.py | rank_genes_groups + per-group CSVs + marker plots | python scripts/find_markers.py clu.h5ad --groupby leiden -o clu.h5ad |

| annotate.py | Map clusters → cell types from JSON/CSV; optional marker reference dotplot | python scripts/annotate.py clu.h5ad -o ann.h5ad --mapping map.json |

| score_genes.py | Score gene signatures (JSON) and/or cell-cycle phase | python scripts/score_genes.py ann.h5ad -o scored.h5ad --gene-sets sigs.json |

| pseudobulk.py | Aggregate counts by sample × cell type → matrix for pydeseq2 | python scripts/pseudobulk.py ann.h5ad --by sample cell_type --metadata condition donor --out-prefix pb |

| subset.py | Subset by obs values or gene list (optionally clear stale embeddings) | python scripts/subset.py ann.h5ad -o tcells.h5ad --obs cell_type --keep "T cells" |

| plot.py | Generate umap/tsne/pca/violin/dotplot/heatmap/etc. from a processed object | python scripts/plot.py ann.h5ad --kind dotplot --genes CD3D CD14 --groupby cell_type |

One-shot end-to-end run

# Counts → clustered object and exploratory marker ranks + figures + marker CSVs
python scripts/run_pipeline.py raw.h5ad -o processed.h5ad \
    --resolution 0.5 --n-top-genes 2000 --scrublet
# With multi-sample integration:
python scripts/run_pipeline.py raw.h5ad -o processed.h5ad --batch-key sample --batch-method harmony
# Reproducible parameters via JSON (keys mirror flag names with underscores):
python scripts/run_pipeline.py raw.h5ad -o processed.h5ad --config params.json

Step-by-step chain (when you need to inspect/iterate between stages)

python scripts/qc_analysis.py        raw.h5ad  -o qc.h5ad   --scrublet
python scripts/preprocess.py         qc.h5ad   -o norm.h5ad --n-top-genes 2000
python scripts/reduce_dimensions.py  norm.h5ad -o red.h5ad  --n-pcs 40
python scripts/cluster.py            red.h5ad  -o clu.h5ad  --resolution 0.3

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