scPyviewer — Python-native interactive viewer for single-cell data

scPyviewer is a lightweight, browser-based explorer for analyzed single-cell datasets. It ingests an AnnData (.h5ad) object directly — no Seurat conversion, no notebook — and lets a non-programmer explore embeddings, gene expression, metadata, and marker/DE tables, then share the result with a single command.

Every actively maintained tool in this space (ShinyCell, ScRDAVis, sCIRCLE/scViewer) is built on R Shiny and requires a Seurat object. scPyviewer fills the Python/scanpy gap: it stays entirely inside the Python stack that most single-cell analysis already runs in.

---

Highlights

	scPyviewer	ShinyCell	ScRDAVis	sCIRCLE/scViewer
Embedding plot (UMAP/PCA/t-SNE)	✓	✓	✓	✓
Single/multi-gene expression overlay	✓	✓	✓	Partial
Violin/box plot grouped by metadata	✓	✓	✓	Partial
Marker gene / DE table browsing	✓	Partial	✓	✗
Cross-dataset / cross-species comparison	✓	✗	✗	✗
No-code shareable deployment	✓	✓	✓	✓
Native Python/AnnData input (no Seurat conversion)	✓	✗	✗	✗

Performance on the chicken-heart atlas (22,315 cells × 10,031 genes):

• All six core views render in < 0.25 s (best-of-three), peak memory 661 MB
• 2.1× faster total render than the R/Seurat substrate (1.4 s vs. 3.0 s)
• 0.6× peak memory vs. Seurat (661 MB vs. 1,076 MB)
• 5.6× faster load from .h5ad than from Seurat .rds (0.6 s vs. 3.5 s)
• Scales to 313K cells / 6 GB on disk via automatic backed mode (≤ 6 GB RAM)

---

Install

# PyPI (recommended)
pip install scPyviewer

# with optional extras (Streamlit viewer + leiden clustering + xlsx export)
pip install 'scPyviewer[all]'

# conda
conda env create -f environment.yml
conda activate scPyviewer

# from source (editable)
pip install -e '.[all]'

This registers two console scripts — scpyviewer (launch the viewer) and scpyviewer-prepare (raw .h5ad → viewer-ready object) — and makes import scPyviewer available.

Optional dependency groups: app (Streamlit), prepare (leiden clustering), excel (.xlsx export), dev (pytest), all (everything).

---

Quick start

./run.sh install     # pip install -e .[all]
./run.sh app         # launch the viewer at http://localhost:8501
                     # (data/toy_example.prepared.h5ad is included for immediate use)

A ready-to-use toy dataset (data/toy_example.prepared.h5ad, 500 cells × 200 genes, 5 immune cell types) is included so you can explore the viewer without downloading any data.

To prepare your own raw .h5ad:

./run.sh prepare data/your_dataset.h5ad

Everything is driven through run.sh, the single reproduction interface:

Command	What it does
./run.sh setup	pip install -r requirements.txt
./run.sh install	pip install -e .[all] — package + console scripts + API
./run.sh prepare [RAW.h5ad]	preprocess raw counts → *.prepared.h5ad (lognorm, HVG, PCA, UMAP, t-SNE, per-group DE)
./run.sh app	launch the Streamlit viewer
./run.sh benchmark	feature-parity + performance harness → results/benchmark_results.json + figures
./run.sh bench-r	R/Seurat cross-language benchmark (needs R + Seurat) → comparison figures
./run.sh api-demo	exercise the programmatic API → results/api_demo/
./run.sh figures	regenerate all demonstration + paper figures
./run.sh test	run the pytest test suite
./run.sh all	prepare → benchmark → figures
./run.sh help	usage

Environment overrides

Variable	Default	Meaning
PY	python	Python interpreter
RSCRIPT	Rscript	R interpreter for bench-r
DATA_DIR	data	directory scanned for .h5ad files
RAW	$DATA_DIR/chicken_heart.h5ad	raw input for prepare/all
PORT	8501	Streamlit port

RAW=data/my_dataset.h5ad ./run.sh prepare
PORT=9000 ./run.sh app

---

The viewer

The Streamlit app (scPyviewer/app.py) has five tabs:

• Embedding — any 2-D embedding colored by metadata or gene expression
• Expression — single/multi-gene overlays, violin, and dot plots grouped by any metadata column
• Markers / DE — interactive browsing of the per-group differential-expression table
• Compare — cross-dataset / cross-species side-by-side comparison (not available in any R Shiny incumbent)
• Export — download the current view and filtered cell tables

A sidebar picks the dataset (any *.prepared.h5ad in DATA_DIR) and applies metadata filters shared across all tabs.

---

Programmatic API

The same data and plotting layers that back the app are exposed as a public Python API. Every plot_* function returns a Matplotlib Figure; every *_table function returns a pandas DataFrame.

import scPyviewer as sv

# load a prepared dataset
ds = sv.load_dataset("data/toy_example.prepared.h5ad")
print(ds.n_obs, ds.n_vars, ds.group_key)   # 500  200  cell_type

# --- figures → matplotlib.figure.Figure ---
fig = sv.plot_embedding(ds, color=ds.group_key)          # color by metadata
fig = sv.plot_embedding(ds, gene="CD3D")                 # color by gene
fig = sv.plot_multigene(ds, genes=["CD3D", "CD19", "CD14"])
fig = sv.plot_violin(ds, gene="CD3D", group=ds.group_key)
fig = sv.plot_dotplot(ds, genes=["CD3D", "CD19", "CD14"], group=ds.group_key)
fig = sv.plot_composition(ds, group=ds.group_key, split="sample")

# --- tables → pandas.DataFrame ---
mk   = sv.markers_table(ds, top_n=25)
comp = sv.composition_table(ds, group=ds.group_key, split="sample")
meta = sv.metadata_table(ds)

# --- batch export ---
sv.export_figures(ds, "out/figs",   formats=["png", "pdf", "svg"])
sv.export_tables(ds,  "out/tables", formats=["csv", "tsv", "xlsx"])

API parameter reference (v0.2.0)

All plotting functions accept additional style parameters beyond the defaults:

Function	Key parameters
plot_embedding	color, gene, embedding, point_size, figsize, label_groups, cmap, alpha, title, dpi, show_legend
plot_multigene	genes, embedding, ncol, point_size, cmap, alpha, max_genes
plot_violin	gene, group, figsize, kind ("violin" / "box"), palette, rotation, show_points
plot_dotplot	genes, group, cmap, size_scale, standard_scale (None / "var" / "group")
plot_composition	group, split, normalize, figsize, palette, bar_width, sort_groups
markers_table	group, top_n, sort_by, ascending
export_figures	outdir, formats, genes, dpi
export_tables	outdir, formats, top_n

Run ./run.sh api-demo for a worked end-to-end example → results/api_demo/.

---

What prepare does

scPyviewer/prepare.py is dataset-agnostic and idempotent — it guards every step and only fills in what is missing:

1. Log-normalize X (skipped if already log-normalized)
2. Highly-variable gene selection → PCA (skipped if X_pca or an alternative embedding exists)
3. UMAP + optional t-SNE (pre-existing embeddings are preserved)
4. Per-group differential expression (Wilcoxon) over the auto-selected grouping column → uns['rank_genes_groups'] + tidy uns['scPyviewer_markers']
5. CSR → CSC conversion for fast backed column access
6. uns['scPyviewer'] provenance block + sidecar *.manifest.json

Flag	Effect
--no-tsne	skip t-SNE (recommended for > 50 K cells)
--no-csc	skip CSC conversion (saves RAM; column access slower)
--backed-only	force disk-streaming mode for very large files
-g COLUMN	force the DE grouping column

---

Large-dataset support (> 5 GB files)

Viewer — backed mode (automatic)

Files larger than 500 MB are opened with backed='r' so the expression matrix X stays on disk and is read column-by-column on demand. Only embeddings, metadata, and graphs enter RAM. Typical viewer peak memory on a 6 GB dataset is < 500 MB.

SCPYVIEWER_BACKED_BYTES=1000000000 ./run.sh app   # back files > 1 GB

Prepare — backed-only mode (auto or explicit)

If the file is larger than 0.66× available RAM, prepare.py automatically switches to backed-only mode:

• Opens the file with backed='r' — X never enters RAM
• Computes UMAP from any pre-existing embedding (X_uce, X_scvi, …)
• Streams X directly from the source file on write
• Peak RAM: ≈ 2–6 GB regardless of file size

In backed-only mode, DE markers are skipped (they require X in RAM).

---

Running the tests

./run.sh test
# or directly:
python -m pytest tests/ -v

The test suite (tests/test_api.py) contains 85 tests covering every public API function, all new style parameters, error paths, and export helpers. Tests run against an in-memory toy AnnData fixture (no disk download required).

---

Full reproduction from scratch

./run.sh install                     # 1. install package + deps
./run.sh all                         # 2. prepare + benchmark + figures
./run.sh bench-r                     # 3. R/Seurat cross-language benchmark (needs R + Seurat)
./run.sh app                         # 4. explore interactively

run.sh all regenerates results/benchmark_results.json and all figures under results/figures/. run.sh bench-r additionally produces results/benchmark_comparison.csv and the comparison figures.

---

Project layout

run.sh                        single reproduction interface
pyproject.toml                package metadata + console scripts + deps
environment.yml               conda environment
requirements.txt              pinned dependencies (Python 3.11)
build_seurat.R                AnnData export -> native Seurat .rds (timed)
bench_r.R                     R/Seurat cross-language render benchmark
scPyviewer/
  __init__.py                 re-exports the public API (import scPyviewer as sv)
  api.py                      public API: plot_*/*_table/export_* (matplotlib)
  api_demo.py                 worked API example (run.sh api-demo)
  _launch.py                  `scpyviewer` console-script launcher
  prepare.py                  raw .h5ad -> viewer-ready .prepared.h5ad
  app.py                      Streamlit viewer (5 tabs)
  io_utils.py                 AnnData loading / metadata / gene access
  plots.py                    pure Plotly plotting functions (app)
  benchmark.py                feature-parity + performance + time-to-share
  merge_bench.py              merge Python + R benchmarks, render comparison figs
  make_figures.py             demonstration + paper figures
tests/
  conftest.py                 shared fixtures (in-memory toy AnnData)
  test_api.py                 85 pytest tests covering the full public API
data/                         .h5ad inputs, *.prepared.h5ad
results/
  benchmark_results.json        Python benchmark output
  benchmark_r.json              R/Seurat benchmark output
  benchmark_comparison.csv      per-operation Python-vs-R table
  benchmark_multi_dataset.csv   multi-dataset scalability table
  api_demo/                     example API figures + tables
  figures/                      paper + demonstration figures
  figures_green_monkey/         benchmark figures — green monkey (78K cells)
  figures_human_lung/           benchmark figures — human lung disease (313K cells, 6 GB)
paper/                        manuscript

---

Reproducibility notes

• Dependencies are pinned in requirements.txt to the versions used for the paper's benchmarks (scanpy 1.11.5, anndata 0.12.19, streamlit 1.59.2, plotly 6.9.0, Python 3.11).
• Benchmark timings are machine-dependent. Re-run ./run.sh benchmark to obtain numbers for your own hardware.
• The cross-language comparison (./run.sh bench-r) needs R with Seurat installed (measured with R 4.5.3, Seurat 5.5.1).
• The tool is built to be dataset- and species-agnostic. Prepare your own .h5ad with ./run.sh prepare data/your_dataset.h5ad.

---

Citation

If you use scPyviewer in your research, please cite:

@article{Xuan2026.08.26.747418,
  author    = {Xuan, Hao and Huang, Yu and Bian, Jiang and Liu, Xiangtao},
  title     = {scPyviewer: a Python-native interactive viewer from AnnData single-cell data},
  year      = {2026},
  doi       = {10.64898/2026.08.26.747418},
  publisher = {Cold Spring Harbor Laboratory},
  journal   = {bioRxiv},
  URL       = {https://www.biorxiv.org/content/early/2026/08/31/2026.08.26.747418}
}

---

License

MIT License — see LICENSE for details.