snapatac2

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Single-cell ATAC-seq analysis with SnapATAC2 (scverse). Covers the full pipeline — fragment import, TSS enrichment QC, tile-matrix construction, doublet filtering, spectral embedding, UMAP/leiden, MACS3 peak calling, gene activity matrices, differentially accessible regions, multi-sample integration via Harmony / MNN-correct, and cell-type annotation via scRNA-seq reference (SCANVI label transfer) or marker-based gene activity. Built on AnnData; interoperates with scanpy.

swaruplab By swaruplab schedule Updated 6/5/2026
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name: snapatac2 description: Single-cell ATAC-seq analysis with SnapATAC2 (scverse). Covers the full pipeline — fragment import, TSS enrichment QC, tile-matrix construction, doublet filtering, spectral embedding, UMAP/leiden, MACS3 peak calling, gene activity matrices, differentially accessible regions, multi-sample integration via Harmony / MNN-correct, and cell-type annotation via scRNA-seq reference (SCANVI label transfer) or marker-based gene activity. Built on AnnData; interoperates with scanpy. license: BSD-3-Clause metadata:

SnapATAC2: Single-Cell ATAC-seq Analysis

Overview

SnapATAC2 is the scverse-native Python rewrite of the original R SnapATAC package, designed for scalable scATAC-seq and snATAC-seq analysis. The compute-heavy paths are written in Rust, so it handles atlas-scale datasets (millions of cells) on modest hardware. It operates on AnnData objects and interoperates with scanpy, anndata, and the rest of the scverse stack.

The core pipeline: fragments → cells → tile matrix → spectral → clusters → peaks → biology. Five distinct downstream branches all share the same backbone:

  1. Standard analysis (single sample) — QC, clustering, gene activity, marker peaks
  2. Multi-sample integration — Harmony / MNN-correct across donors / batches
  3. Multi-modality — joint analysis of paired ATAC + RNA (multiome)
  4. Atlas-scale — millions of cells with chunked processing
  5. Annotation — label transfer from a scRNA-seq reference via SCANVI, or manual annotation via gene activity scores

When to Use This Skill

  • Analyzing scATAC-seq or snATAC-seq fragment files (.tsv.gz from cellranger-atac, .bed.gz, etc.)
  • Multi-sample integration of ATAC datasets (treatment vs control, multiple donors)
  • Calling peaks at the cluster level with MACS3
  • Identifying differentially accessible regions (DARs) between cell populations
  • Annotating ATAC clusters using a matched scRNA-seq reference (label transfer)
  • Going from a fragment file all the way to a final h5ad with cell-type labels + peaks + gene activity

Not for: bulk ATAC-seq (use vanilla MACS2/MACS3), CUT&RUN/CUT&Tag (specialised tools exist), differential motif analysis (use chromVAR or pycisTopic), trajectory analysis (use scVelo / PAGA on the spectral embedding).

Prerequisites

  • Python 3.9+ (3.10 / 3.11 recommended)
  • Linux or macOS x86_64 (precompiled binaries available); Apple Silicon and Windows require source build
  • 8 GB+ RAM for typical 10X experiments; 32 GB+ for multi-sample / atlas runs
  • Fragment file(s) — sorted/bgzipped or unsorted (sorted_by_barcode=False)

Installation

pip install snapatac2                    # core
pip install 'snapatac2[recommend]'       # + harmonypy, scanorama, xgboost (recommended)

If precompiled wheels aren't available for your platform (Apple Silicon, exotic Linux), install build deps first:

# Rust + cmake
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
brew install cmake hdf5                  # macOS — also: apt install cmake libhdf5-dev on Linux
pip install snapatac2

For SCANVI-based label transfer (annotation tutorial), additionally:

pip install 'scvi-tools[scanpy]'

Pipeline 1 — Standard Single-Sample Analysis

This is the foundation. Every other pipeline starts here.

1. Import fragments and compute basic QC

import snapatac2 as snap

data = snap.pp.import_fragments(
    "fragments.tsv.gz",
    chrom_sizes=snap.genome.hg38,        # or .mm10, .hg19, .GRCh38 etc.
    file="sample.h5ad",                   # backed mode — written to disk
    sorted_by_barcode=False,
    min_num_fragments=200,                # drop barcodes below this floor
)

# TSS enrichment — the standard scATAC quality metric.
# Higher = more cell-like signal (open promoters); lower = empty/dead droplets.
snap.metrics.tsse(data, snap.genome.hg38)

# Visualize the TSS-enrichment × fragments knee plot
snap.pl.tsse(data, interactive=False, out_file="figures/tsse_knee.pdf")

After this, data.obs has: n_fragment, frac_dup, frac_mito, tsse.

2. Filter cells

snap.pp.filter_cells(
    data,
    min_counts=1000,
    min_tsse=7,                # canonical cut; raise to 10 for stringent data
    max_counts=100000,         # cap top end — likely doublets
)

The knee on tsse from the previous step tells you where to cut.

3. Tile matrix — cells × genome bins

ATAC data starts unstructured. SnapATAC2's tile matrix bins the genome into fixed windows (default 500 bp, often bumped to 5 kb for memory):

snap.pp.add_tile_matrix(data, bin_size=5000)

After this, the AnnData X is a cell × bin sparse matrix. data.var has the bin coordinates.

4. Select informative features

snap.pp.select_features(data, n_features=50000)

Picks the most variable / informative bins. The output is stored in data.var['selected'].

5. Doublet detection

snap.pp.scrublet(data)
snap.pp.filter_doublets(data)

Scrublet is run on the binarized accessibility — same principle as scRNA but operates on bins. Adds data.obs['doublet_score'] and data.obs['is_doublet'].

6. Spectral embedding

SnapATAC2's headline algorithm: a scalable spectral (Laplacian eigenmap) decomposition that scales to millions of cells via random projections.

snap.tl.spectral(data, n_comps=50)

Result: data.obsm['X_spectral'] is the cell × component matrix. This is the analog of PCA on RNA — every downstream step uses it.

7. KNN graph + UMAP + leiden

snap.pp.knn(data, use_rep="X_spectral", n_neighbors=50)
snap.tl.umap(data, use_rep="X_spectral")
snap.tl.leiden(data, resolution=1.0)

Standard. Pass use_rep="X_spectral_harmony" after running Harmony (next pipeline).

8. Peak calling per cluster — MACS3

snap.tl.macs3(
    data,
    groupby='leiden',                    # one peak set per cluster
    replicate=None,                       # set to the sample column for multi-sample
)

# Result lives in data.uns['macs3'] as a dict: {cluster_id: peak_dataframe}

9. Merge peaks across clusters

merged_peaks = snap.tl.merge_peaks(data.uns['macs3'], chrom_sizes=snap.genome.hg38)
# merged_peaks is a polars DataFrame with the union of cluster-specific peaks

10. Cell × peak matrix

The tile matrix (step 3) is too coarse for biological interpretation. The cell × peak matrix uses the merged peak set:

peak_mat = snap.pp.make_peak_matrix(data, use_rep=merged_peaks)
# peak_mat is a separate AnnData — cells × peaks, sparse

11. Differentially accessible regions (DARs)

# Find peaks specific to one cluster vs the rest
dars = snap.tl.diff_test(
    peak_mat,
    cell_group1=peak_mat.obs.leiden == '5',
    cell_group2=peak_mat.obs.leiden != '5',
    direction='positive',                # 'positive', 'negative', or 'both'
)
# Returns a polars DataFrame: peak, log2(fc), p-value, FDR

12. Gene activity matrix — bridge to RNA-seq

gene_mat = snap.pp.make_gene_matrix(data, gene_anno=snap.genome.hg38)
# gene_mat is a new AnnData — cells × genes
# X is the per-gene aggregated accessibility (TSS + gene body + extensions)

Once you have the gene matrix, you can use scanpy for marker analysis:

import scanpy as sc
sc.pp.normalize_total(gene_mat); sc.pp.log1p(gene_mat)
sc.tl.rank_genes_groups(gene_mat, groupby='leiden', method='wilcoxon')
sc.pl.rank_genes_groups(gene_mat, n_genes=10)

Convenience: python scripts/snapatac_standard.py --fragments fragments.tsv.gz --genome hg38 --out sample.h5ad. See references/multi_sample.md for multi-sample, references/annotation.md for label transfer.

Source: Standard pipeline tutorial and Identify DARs tutorial.

Pipeline 2 — Multi-Sample Integration

When you have multiple donors / conditions / batches and want a unified embedding. Two batch-correction methods supported: Harmony (more common) and MNN-correct (more robust on extreme batch shifts).

1. Import all fragments in one shot

import snapatac2 as snap

files = snap.datasets.colon()             # demo: 5 colon snATAC samples

adatas = snap.pp.import_fragments(
    [fragment_path for _, fragment_path in files],
    file=[f"{name}.h5ad" for name, _ in files],   # one file per sample
    chrom_sizes=snap.genome.hg38,
    min_num_fragments=1000,
)

2. Run QC + features per sample (one call, vectorized)

snap.metrics.tsse(adatas, snap.genome.hg38)
snap.pp.filter_cells(adatas, min_tsse=7)
snap.pp.add_tile_matrix(adatas, bin_size=5000)
snap.pp.select_features(adatas, n_features=50000)
snap.pp.scrublet(adatas)
snap.pp.filter_doublets(adatas)

Each call iterates internally across all samples; no Python loop needed.

3. Combine into an AnnDataSet

This is the SnapATAC2-specific container — a "view" over per-sample h5ad files that exposes them as one AnnData. Cells from different samples get prefixed barcodes so collisions don't matter.

data = snap.AnnDataSet(
    adatas=[(name, ad) for (name, _), ad in zip(files, adatas)],
    filename="colon.h5ads",
)
# Result: AnnDataSet object with n_obs x n_vars = 41785 x 606219
#         (5 samples, 41785 cells total)

4. Spectral + UMAP (raw, pre-correction)

snap.pp.select_features(data, n_features=50000)
snap.tl.spectral(data)
snap.tl.umap(data)

Inspect the UMAP — donor/batch usually dominates. That's what the next step fixes.

5. Batch correction — pick one

# Option A: MNN-correct (mutual nearest neighbors — slower, more robust)
snap.pp.mnc_correct(data, batch="sample")
# Sets data.obsm['X_spectral_mnn']

# Option B: Harmony (faster, more common; needs harmonypy installed)
snap.pp.harmony(data, batch="sample", max_iter_harmony=20)
# Sets data.obsm['X_spectral_harmony']

You can run both and compare.

6. UMAP + clustering on the corrected embedding

snap.tl.umap(data, use_rep="X_spectral_harmony")     # or X_spectral_mnn
snap.pp.knn(data, use_rep="X_spectral_harmony")
snap.tl.leiden(data, resolution=1.0)

7. Per-cluster peak calling, per-sample replicates

snap.tl.macs3(data, groupby='leiden', replicate='sample')
merged_peaks = snap.tl.merge_peaks(data.uns['macs3'], chrom_sizes=snap.genome.hg38)

Specifying replicate='sample' tells MACS3 to treat each sample as a biological replicate within each cluster — yields more conservative, reproducible peaks.

8. Persist + reopen

# The AnnDataSet persists automatically to colon.h5ads
# In a later session:
data = snap.read_dataset("colon.h5ads")

Convenience: python scripts/snapatac_multi_sample.py --samples sample_paths.txt --genome hg38 --batch-correct harmony --out colon.h5ads.

Source: Integration tutorial.

Pipeline 3 — Cell-Type Annotation

Two complementary approaches:

3a. Manual — gene activity + canonical markers

# Build gene activity matrix from the standard pipeline output
gene_mat = snap.pp.make_gene_matrix(data, gene_anno=snap.genome.hg38)

import scanpy as sc
sc.pp.normalize_total(gene_mat); sc.pp.log1p(gene_mat)

# Score canonical markers (T cells, B cells, monocytes, etc.)
sc.tl.score_genes(gene_mat, gene_list=['CD3D', 'CD3E', 'CD3G', 'TRAC'], score_name='T_score')
sc.tl.score_genes(gene_mat, gene_list=['MS4A1', 'CD79A', 'CD79B'],      score_name='B_score')
sc.tl.score_genes(gene_mat, gene_list=['CD14', 'LYZ', 'S100A8'],        score_name='Mono_score')

# Per-cluster mean of each score
gene_mat.obs.groupby('leiden')[['T_score', 'B_score', 'Mono_score']].mean()

# Assign cluster → cell type from the scores, then write back to the ATAC object
data.obs['cell_type'] = data.obs['leiden'].map({
    '0': 'T_CD4', '1': 'T_CD8', '2': 'B', '3': 'Mono', ...
})

3b. Automated — SCANVI label transfer from scRNA-seq

When you have a matched scRNA-seq reference (with cell-type labels), train a joint embedding and predict labels for the ATAC cells.

import scanpy as sc
import anndata as ad
import scvi

# Build ATAC gene-activity matrix
query = snap.pp.make_gene_matrix(atac, gene_anno=snap.genome.hg38)
query.obs['batch']   = 'ATAC'
query.obs['celltype_scanvi'] = 'Unknown'        # placeholder for unlabelled

# Load and tag the RNA reference
reference = sc.read_h5ad("rna_reference.h5ad")
reference.obs['batch'] = 'RNA'
# reference.obs['celltype_scanvi'] already has labels

# Merge on shared genes
data = ad.concat([reference, query], join='inner', label='batch_origin')

# Standard HVG / log-norm prep
sc.pp.normalize_total(data); sc.pp.log1p(data)
sc.pp.highly_variable_genes(data, n_top_genes=4000, batch_key='batch')

# Train scVI then SCANVI for label transfer
scvi.model.SCVI.setup_anndata(data, batch_key="batch")
vae  = scvi.model.SCVI(data, n_layers=2, n_latent=30)
vae.train()

lvae = scvi.model.SCANVI.from_scvi_model(
    vae, adata=data,
    labels_key="celltype_scanvi",
    unlabeled_category="Unknown",
)
lvae.train()
data.obs["C_scANVI"] = lvae.predict(data)

# Map predicted labels back to the ATAC AnnData
atac.obs['celltype_predicted'] = data[data.obs['batch'] == 'ATAC'].obs['C_scANVI'].values

See references/annotation.md for confidence scoring, conflict resolution between SCANVI predictions and leiden clusters, and how to handle ATAC cell types absent from the RNA reference (e.g. very rare populations).

Source: Annotation tutorial.

Pipeline 4 — Differentially Accessible Regions (DARs)

After clustering and per-cluster peak calling, find peaks specific to one cell type:

# Build the peak matrix (cells × peaks, sparse)
peak_mat = snap.pp.make_peak_matrix(data, use_rep=merged_peaks)

# DARs of cluster 5 (e.g. CD8 T cells) vs all other cells
dars_pos = snap.tl.diff_test(
    peak_mat,
    cell_group1=peak_mat.obs.leiden == '5',
    cell_group2=peak_mat.obs.leiden != '5',
    direction='positive',
)

# Filter by significance
sig_peaks = dars_pos.filter(
    (dars_pos['adjusted p-value'] < 0.01) & (dars_pos['log2(fc)'] > 1)
)

# Save to BED for downstream motif analysis (HOMER, MEME, etc.)
sig_peaks.to_pandas()[['chrom', 'start', 'end']].to_csv(
    'CD8_dars.bed', sep='\t', header=False, index=False
)

direction='positive' finds peaks ENRICHED in group1; 'negative' finds peaks DEPLETED in group1; 'both' returns all changing peaks.

For across-condition DARs (e.g. tumor vs adjacent normal within the same cell type), filter the peak matrix to one cluster first then run diff_test between conditions.

Source: DAR tutorial (3rd tutorial in the list).

Visualization Cookbook

# QC plots
snap.pl.tsse(data, interactive=False)
snap.pl.frag_size_distr(data, interactive=False)

# UMAP — leiden clusters
snap.pl.umap(data, color='leiden')

# UMAP — sample (batch sanity check)
snap.pl.umap(data, color='sample')

# UMAP — gene activity of a marker
gene_mat = snap.pp.make_gene_matrix(data, gene_anno=snap.genome.hg38)
sc.pl.umap(gene_mat, color=['CD3D', 'MS4A1', 'CD14'])

# Peak coverage at a locus — like a Genome Browser snapshot
snap.pl.regions(data, groupby='leiden', regions=['chr1:1000000-1010000'])

Key Parameters to Adjust

Tile size — add_tile_matrix(bin_size=...)

  • 500 bp (default): finest resolution, biggest matrix
  • 5,000 bp (5 kb): standard for first-pass analysis; 10× faster
  • 10,000 bp: very large datasets only

Spectral components — tl.spectral(n_comps=...)

  • 50 (default): plenty for most datasets
  • 30 for very small datasets (< 5k cells)
  • 100 for atlas-scale (millions of cells)

KNN neighbors — pp.knn(n_neighbors=...)

  • 50 (default for ATAC): higher than RNA's typical 15, because ATAC noise is higher

Leiden resolution — tl.leiden(resolution=...)

  • 1.0 (default): coarse-grained
  • 0.5: fewer clusters (often what you want for cell-type assignment)
  • 2.0+: very fine sub-clusters (cell states / niches)

MACS3 — tl.macs3(replicate=...)

  • None for single-sample
  • sample column for multi-sample — treats each sample as a replicate. Recommended for any multi-sample work.

Best Practices

  • Use the backed h5ad mode. Pass file="sample.h5ad" to import_fragments — keeps the working memory low and lets you resume from disk.
  • Inspect the TSS-enrichment knee. It's the canonical scATAC QC. Cells with tsse < 7 are usually dead/empty. Higher data quality → higher cutoff (10-15 is reasonable for fresh PBMC).
  • 5 kb bins by default; 500 bp only when you need it. The default 500 bp matrix is huge and rarely changes downstream conclusions for clustering. Bump down to 500 bp only after you've decided which regions / cell types matter.
  • For peak calling, always provide replicate=sample in multi-sample. Otherwise MACS3 treats all cells in a cluster as one pool, inflating power and getting non-reproducible peaks.
  • Don't bin-feature-select after batch correction. Run select_features first, then spectral, then Harmony / MNN. Reversing this throws away the batch-correction benefit on the bin layer.
  • Gene activity is not gene expression. It's a proxy. Trust it for high-level cell-type marker calls; don't trust it for fine-grained DEG-like analyses.

End-to-End Template

assets/snapatac_template.py — single parameterized script. Set fragment file(s), genome, batch correction method, then run all of Pipelines 1+2+3.

Convenience Scripts

  • scripts/snapatac_standard.py — Pipeline 1 (single-sample)
  • scripts/snapatac_multi_sample.py — Pipeline 2 (multi-sample integration with Harmony or MNN)

References

Install via CLI
npx skills add https://github.com/swaruplab/operon --skill snapatac2
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