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scrna-seq-pipeline

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Updated 10-15 17:23

About this skill

scRNA-seq Pipeline (Scanpy)

Overview

This skill produces a reproducible scRNA-seq analysis in Python using Scanpy and AnnData. It covers the standard workflow: load counts → QC filter → normalize/log → HVG → scale/PCA → neighbors/UMAP → Leiden → differential expression, plus batch correction with Harmony or scVI. Default parameters follow the Scanpy PBMC3k tutorial conventions and are safe starting points for most 10X Genomics datasets.

Output is a single .h5ad file with all intermediate embeddings, cluster labels, and DE results stored on the AnnData object.

Usage

Install dependencies:

pip install "scanpy>=1.10" anndata leidenalg python-igraph harmonypy scvi-tools

Run the pipeline end-to-end:

import scanpy as sc
import scanpy.external as sce

sc.settings.verbosity = 2
sc.settings.set_figure_params(dpi=100, facecolor="white")

# 1. Load
adata = sc.read_10x_h5("filtered_feature_bc_matrix.h5")
# Alternatives:
# adata = sc.read_10x_mtx("filtered_feature_bc_matrix/", var_names="gene_symbols", cache=True)
# adata = sc.read_h5ad("input.h5ad")
adata.var_names_make_unique()

# 2. QC
adata.var["mt"] = adata.var_names.str.startswith("MT-")
sc.pp.calculate_qc_metrics(adata, qc_vars=["mt"], percent_top=None, log1p=False, inplace=True)
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
adata = adata[adata.obs.n_genes_by_counts < 5000, :]
adata = adata[adata.obs.pct_counts_mt < 20, :].copy()

# 3. Normalize + HVG
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
adata.raw = adata  # freeze log-normalized full gene set BEFORE HVG subset
sc.pp.highly_variable_genes(adata, n_top_genes=2000, flavor="seurat")
adata = adata[:, adata.var.highly_variable].copy()

# 4. Scale + PCA + neighbors + UMAP + Leiden
sc.pp.scale(adata, max_value=10)
sc.tl.pca(adata, n_comps=50, svd_solver="arpack")
sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30)
sc.tl.umap(adata)
sc.tl.leiden(adata, resolution=0.5)

# 5. Differential expression per cluster
sc.tl.rank_genes_groups(adata, groupby="leiden", method="wilcoxon")

# 6. Save
adata.write("result.h5ad", compression="gzip")

Batch correction — Harmony

# Assumes adata.obs["batch"] exists and PCA is already computed.
sce.pp.harmony_integrate(adata, key="batch")  # writes adata.obsm["X_pca_harmony"]
sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30, use_rep="X_pca_harmony")
sc.tl.umap(adata)
sc.tl.leiden(adata, resolution=0.5)

Batch correction — scVI

scVI requires raw counts. Run it before normalization, or keep a raw-count layer.

import scvi

# adata_raw holds unnormalized integer counts; adata.obs["batch"] is the batch key.
scvi.model.SCVI.setup_anndata(adata_raw, batch_key="batch")
model = scvi.model.SCVI(adata_raw, n_latent=30)
model.train(max_epochs=400, early_stopping=True)
adata.obsm["X_scVI"] = model.get_latent_representation()

sc.pp.neighbors(adata, n_neighbors=15, use_rep="X_scVI")
sc.tl.umap(adata)
sc.tl.leiden(adata, resolution=0.5)

Response Format

  • Lead with the command or code the user needs — explain after
  • Structure as: confirm inputs → working code → key parameters explained → gotchas
  • One complete working example per task; do not show every alternative
  • Keep code comments minimal and functional (what, not why-it-exists)
  • Target: 50-100 lines of code with brief surrounding explanation

Core Concepts

  • AnnData layout. adata.X is the current expression matrix (cells × genes). adata.obs holds per-cell metadata (QC metrics, cluster labels, batch). adata.var holds per-gene metadata (HVG flags, mt). Embeddings live in adata.obsm (X_pca, X_umap, X_scVI). Neighbor graph and DE results live in adata.uns.
  • .raw snapshot. adata.raw = adata stores the full log-normalized matrix before HVG subsetting. Downstream plotting functions (sc.pl.umap(..., color="GENE"), sc.pl.rank_genes_groups) read from .raw so you can visualize any gene even after restricting adata to HVGs.
  • QC thresholds are dataset-dependent. n_genes ∈ [200, 5000] and pct_mt < 20 are reasonable defaults for human 10X PBMC-like data. Inspect distributions (sc.pl.violin) before committing. Use MT- for human and mt- for mouse.
  • HVG flavor matters. Use flavor="seurat" on log-normalized data (default). Use flavor="seurat_v3" on raw counts — it expects integer counts and will error or mislead on log data.
  • Neighbors parameters. n_neighbors controls local/global structure (10–50; larger = smoother UMAP). n_pcs should capture the elbow in sc.pl.pca_variance_ratio (typically 20–50). Leiden resolution controls cluster granularity (0.2–1.5; higher = more clusters).
  • Batch correction choice. Harmony is fast, operates on PCA, and works well for mild-to-moderate batch effects. scVI is a deep generative model, needs raw counts and GPU for speed, and handles stronger technical variation and multi-modal designs.

Quick Reference

Loading

Input Call
10X HDF5 sc.read_10x_h5("filtered_feature_bc_matrix.h5")
10X MTX dir sc.read_10x_mtx("path/", var_names="gene_symbols", cache=True)
Existing AnnData sc.read_h5ad("input.h5ad")
Always after load adata.var_names_make_unique()

Key parameter ranges

Parameter Default Typical range Notes
filter_cells(min_genes=) 200 100–500 Drop empty/low-quality droplets
filter_genes(min_cells=) 3 3–10 Drop rarely-expressed genes
n_genes_by_counts < 5000 2500–8000 Upper bound flags doublets
pct_counts_mt < 20 5–25 Human tissue dependent
normalize_total(target_sum=) 1e4 1e4 CP10k; standard
n_top_genes (HVG) 2000 1000–5000 More HVGs → more signal + noise
scale(max_value=) 10 10 Clip extreme z-scores
pca(n_comps=) 50 30–100 Pick elbow for downstream n_pcs
neighbors(n_neighbors=) 15 10–50 Larger → smoother manifold
neighbors(n_pcs=) 30 20–50 ≤ n_comps
leiden(resolution=) 0.5 0.2–1.5 Higher → more clusters

DE & inspection

sc.tl.rank_genes_groups(adata, groupby="leiden", method="wilcoxon")
sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False)

# Top-N markers per cluster as a DataFrame
import pandas as pd
result = adata.uns["rank_genes_groups"]
groups = result["names"].dtype.names
markers = pd.DataFrame({g: result["names"][g][:20] for g in groups})

Saving / loading

adata.write("result.h5ad", compression="gzip")
adata = sc.read_h5ad("result.h5ad")

Common Mistakes

  • Wrong: Subsetting to HVGs before saving adata.raw = adata Right: Always set .raw on the log-normalized full matrix before adata = adata[:, adata.var.highly_variable].copy() Why: Without .raw, you lose the ability to plot or score non-HVG genes in downstream analysis

  • Wrong: Using flavor="seurat_v3" on log-normalized data Right: Match HVG flavor to data state: seurat_v3 requires raw integer counts; seurat (default) requires log-normalized data Why: Wrong flavor produces nonsense HVG rankings or errors, corrupting all downstream steps

  • Wrong: Skipping adata.var_names_make_unique() after loading 10X data Right: Call adata.var_names_make_unique() immediately after loading Why: Duplicated gene symbols cause silent misbehavior in indexing, HVG selection, and DE analysis

  • Wrong: Running scVI on normalized/log-transformed data Right: Feed scVI the raw-count AnnData and set batch_key via setup_anndata Why: scVI models counts directly; normalized input violates its distributional assumptions and produces incorrect latent spaces

  • Wrong: Using MT- prefix for mouse mitochondrial genes (or mt- for human) Right: Use MT- for human genes and mt- for mouse genes Why: Wrong prefix makes pct_counts_mt always zero, rendering the QC filter a no-op

  • Wrong: Boolean-slicing AnnData without calling .copy() Right: Always use .copy() after filtering: adata = adata[mask, :].copy() Why: Without .copy(), the result is a view; subsequent in-place operations will warn or fail

References