shanuz 0.1.1

Python single-cell genomics toolkit — a port of Seurat's core data structures and analysis pipeline

Shanuz — Python Single-Cell Genomics Toolkit

Shanuz is a Python port of the Seurat single-cell RNA-seq analysis framework, implementing Seurat's core data structures, preprocessing pipeline, dimensionality reduction, clustering, and marker detection — entirely in Python.

The package is spiritually and algorithmically faithful to Seurat v5 while providing a pure-Python, pip-installable alternative that integrates naturally with NumPy, SciPy, and AnnData ecosystems.

---

Features

• Shanuz object — mirrors the R Seurat S4 class with __slots__-based Python classes
• Assay5 — sparse-matrix-backed multi-layer assay (counts, data, scale.data)
• Preprocessing — normalize_data, find_variable_features (VST), scale_data, percentage_feature_set
• SCTransform — sctransform (regularized negative-binomial Pearson residuals)
• Signature scoring — add_module_score, cell_cycle_scoring (S/G2M + Phase)
• Dimensionality reduction — run_pca (via scikit-learn)
• Nearest-neighbour graph — find_neighbors (KNN + SNN)
• Clustering — find_clusters (Louvain via python-igraph, Leiden via leidenalg)
• UMAP — run_umap (via umap-learn)
• PC significance — jack_straw, score_jackstraw (JackStraw permutation test)
• Differential expression — find_markers, find_all_markers (wilcox tie-corrected, t, LR, negbinom, roc)
• Plotting — dim_plot, feature_plot, vln_plot, dot_plot, elbow_plot, do_heatmap, dim_heatmap, feature_scatter, variable_feature_plot, ridge_plot (matplotlib/seaborn)
• AnnData interoperability — as_anndata, from_anndata
• Spatial (Xenium / Visium / CosMx) — load_xenium/load_visium/load_cosmx, get_tissue_coordinates, nearest_neighbor_distance, local_neighborhood, build_niche_assay, composition_test, image_dim_plot, image_feature_plot
• PBMC 3k tutorial — end-to-end validated against the official Seurat tutorial
• PBMC 8k advanced tutorial — larger dataset + T/NK subclustering workflow
• CITE-seq multimodal tutorial — RNA + surface protein (ADT) with CLR normalization
• Xenium spatial tutorial — spatial neighbourhood/niche analysis, verified to 8 s.f. against R Seurat

---

Installation

Shanuz is not yet published to PyPI. Install directly from the GitHub repository.

With uv (recommended)

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh  # macOS/Linux
# or: powershell -c "irm https://astral.sh/uv/install.ps1 | iex"  # Windows

git clone https://github.com/GenomicAI/shanuz.git
cd shanuz
uv venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
uv pip install -e ".[analysis]"

With pip

git clone https://github.com/GenomicAI/shanuz.git
cd shanuz
pip install -e ".[analysis]"

Full development installation (includes tests and linting)

git clone https://github.com/GenomicAI/shanuz.git
cd shanuz
uv venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
uv pip install -e ".[all]"

---

Quick Start

import scipy.sparse as sp
import numpy as np
from shanuz import create_shanuz_object

# Create a Shanuz object from a counts matrix
counts = sp.random(2000, 500, density=0.2, format="csc")
sobj = create_shanuz_object(counts, project="my_project", min_cells=3, min_features=200)
print(sobj)
# Shanuz object — my_project
#   500 cells × 2000 features
#   Active assay: 'RNA'
#   Reductions: []
#   Version: 5.4.0

# Access metadata
print(sobj.meta_data.head())

---

Tutorials

Three end-to-end tutorials — from basic guided clustering to multimodal CITE-seq — each pairing R Seurat code side-by-side with the Python Shanuz equivalent. See tutorials/README.md for the full index.

#	Tutorial	Dataset	Complexity
1	PBMC 3k — Guided Clustering	3k PBMCs · 10x Genomics	Beginner
2	PBMC 8k — Advanced Subclustering	8k PBMCs · GRCh38	Intermediate
3	CBMC CITE-seq — Multimodal	8,600 CBMCs · RNA + 13 proteins	Advanced
4	PBMC 3k — SCTransform	3k PBMCs · 10x Genomics	Advanced
5	Xenium — Spatial (R vs Python)	36k cells · 10x Xenium mouse brain	Spatial

# Tutorial 1 — PBMC 3k
python tutorials/pbmc3k_tutorial.py && python tutorials/generate_plots.py

# Tutorial 2 — PBMC 8k subclustering
python tutorials/pbmc8k_subclustering_tutorial.py && python tutorials/generate_advanced_plots.py

# Tutorial 3 — CITE-seq multimodal
python tutorials/cbmc_citeseq_tutorial.py && python tutorials/generate_multimodal_plots.py

# Tutorial 4 — SCTransform
python tutorials/pbmc3k_sctransform_tutorial.py && python tutorials/generate_sctransform_plots.py

# Tutorial 5 — Xenium spatial (auto-downloads ~20 MB)
python tutorials/generate_spatial_plots.py

---

API Reference

Object creation

from shanuz import create_shanuz_object

pbmc = create_shanuz_object(
    counts,             # scipy.sparse CSC/CSR or numpy ndarray (genes × cells)
    project="pbmc3k",
    min_cells=3,        # filter genes present in fewer than N cells
    min_features=200,   # filter cells with fewer than N detected genes
)

Preprocessing

from shanuz.preprocessing import (
    normalize_data,
    find_variable_features,
    scale_data,
    percentage_feature_set,
)

percentage_feature_set(pbmc, pattern=r"^MT-", col_name="percent.mt")
normalize_data(pbmc, normalization_method="LogNormalize", scale_factor=10000)
find_variable_features(pbmc, selection_method="vst", nfeatures=2000)
scale_data(pbmc)

Dimensionality reduction & clustering

from shanuz.reduction import run_pca
from shanuz.neighbors import find_neighbors
from shanuz.clustering import find_clusters
from shanuz.umap import run_umap

run_pca(pbmc, n_pcs=50)
find_neighbors(pbmc, dims=range(10), k_param=20)
find_clusters(pbmc, resolution=0.5)
run_umap(pbmc, dims=range(10))

Differential expression

from shanuz.markers import find_markers, find_all_markers

markers = find_markers(pbmc, ident_1=1)
all_markers = find_all_markers(pbmc, only_pos=True, logfc_threshold=0.25)

Plotting

All plotting functions return a matplotlib.figure.Figure — save or display as needed.

from shanuz.plotting import (
    dim_plot,            # DimPlot   — cells on UMAP/PCA coloured by ident
    feature_plot,        # FeaturePlot — gene expression on embedding
    vln_plot,            # VlnPlot   — violin plots per cluster
    elbow_plot,          # ElbowPlot — stdev per PC
    feature_scatter,     # FeatureScatter — two features vs each other
    variable_feature_plot, # VariableFeaturePlot — mean-variance HVG plot
    dim_heatmap,         # DimHeatmap — top loading genes per PC
    do_heatmap,          # DoHeatmap  — expression heatmap sorted by cluster
    ridge_plot,          # RidgePlot  — ridgeline plots per cluster
)

# Quick examples
fig = dim_plot(pbmc, reduction="umap", label=True)
fig = feature_plot(pbmc, ["LYZ", "MS4A1", "NKG7"], reduction="umap", ncol=3)
fig = vln_plot(pbmc, ["LYZ", "CD3D", "PPBP"], group_by=None)
fig = elbow_plot(pbmc, ndims=20)
fig = do_heatmap(pbmc, top_marker_genes)
fig.savefig("output.png", dpi=150, bbox_inches="tight")

Shanuz function	R Seurat equivalent
dim_plot	DimPlot
feature_plot	FeaturePlot
vln_plot	VlnPlot
dot_plot	DotPlot
elbow_plot	ElbowPlot
feature_scatter	FeatureScatter
variable_feature_plot	VariableFeaturePlot
dim_heatmap	DimHeatmap
do_heatmap	DoHeatmap
ridge_plot	RidgePlot

---

Data Structures

Shanuz
├── assays: dict[str, Assay5]
│   └── "RNA"
│       ├── layers["counts"]    # raw integer counts (genes × cells)
│       ├── layers["data"]      # log-normalized (genes × cells)
│       └── layers["scale.data"] # z-scored (genes × cells)
├── meta_data: pd.DataFrame     # per-cell metadata
├── reductions: dict
│   ├── "pca": DimReduc         # PCA embeddings + loadings
│   └── "umap": DimReduc        # UMAP embeddings
├── graphs: dict
│   ├── "RNA_nn": Graph         # KNN graph
│   └── "RNA_snn": Graph        # SNN graph
└── commands: list[ShanuzCommand]  # audit log

---

Roadmap

See ROADMAP.md for the full development plan. Upcoming milestones:

Milestone	Focus
v0.2.0	Batch correction — Harmony, CCA/RPCA, IntegrateLayers
v0.3.0	Reference mapping — FindTransferAnchors, TransferData, MapQuery
v0.4.0	Multimodal WNN — FindMultiModalNeighbors, joint UMAP/clustering
v0.5.0	Additional reductions — t-SNE, ICA, SPCA, GLM-PCA
v0.6.0	Pseudobulk DE — AggregateExpression, DESeq2, MAST, FindConservedMarkers
v0.7.0	Spatial — Xenium/Visium/CosMx loaders, niche/neighbourhood analysis, image_* plots ✅ (largely delivered — see Tutorial 5); remaining: MERSCOPE loader, FindSpatiallyVariableFeatures (Moran's I), Visium tissue-image plots
v0.8.0	Scale — BPCells-style lazy matrices, SketchData, ProjectData
v0.9.0	Specialized — HTODemux, Mixscape (CRISPR screens)
v0.10.0	Infrastructure — PyPI, GitHub Actions CI, type annotations, MkDocs site

---

Running Tests

uv pip install -e ".[dev]"
pytest tests/ -v

All 144 unit tests pass.

---

Dependencies

Package	Purpose
numpy, scipy, pandas	Core numerics and data frames
statsmodels	LOESS smoothing for VST
scikit-learn	PCA
umap-learn	UMAP embedding
python-igraph	Louvain clustering
leidenalg	Leiden clustering
packaging	Version handling

---

Credits

Development assistance: This package was developed with the help of Claude (Anthropic's AI assistant, claude-sonnet-4-6), which assisted in porting the R Seurat codebase to Python, implementing the VST algorithm, degree-2 LOESS, Louvain clustering, and validating results against the official PBMC 3k tutorial.

Original R Seurat package:
The algorithms and data structures in Shanuz are direct Python translations of the R Seurat package by the Satija Lab. Please cite the original Seurat papers if you use Shanuz in published work:

Hao Y, Stuart T, Kowalski MH, et al. (2024). Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nature Biotechnology, 42, 293–304. https://doi.org/10.1038/s41587-023-01767-y

Hao Y, Hao S, Andersen-Nissen E, et al. (2021). Integrated analysis of multimodal single-cell data. Cell, 184(13), 3573–3587. https://doi.org/10.1016/j.cell.2021.04.048

Stuart T, Butler A, Hoffman P, et al. (2019). Comprehensive Integration of Single-Cell Data. Cell, 177(7), 1888–1902. https://doi.org/10.1016/j.cell.2019.05.031

Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36, 411–420. https://doi.org/10.1038/nbt.4096

PBMC 3k dataset:
10x Genomics. (2016). 3k PBMCs from a Healthy Donor. https://www.10xgenomics.com/resources/datasets/3-k-pb-mcs-from-a-healthy-donor-1-standard-1-1-0

---

License

MIT License — see LICENSE for details.

This software is an independent reimplementation for educational and research purposes. It is not affiliated with, endorsed by, or maintained by the Satija Lab or 10x Genomics.