cnvturbo 0.2.1

cnvturbo: GPU/Numba-accelerated scRNA-seq CNV inference with HMM i6 cell-level tumor calling and R inferCNV-compatible raw-count pipeline. Fully compatible with Scanpy/AnnData. 10-100x faster than alternatives.

cnvturbo

cnvturbo — A Python re-implementation of R inferCNV for single-cell RNA-seq copy-number variation analysis. Algorithmically faithful to R inferCNV's HMM i6 pipeline, ~100× faster, and fully integrated with the Scanpy / AnnData ecosystem.

Rewritten in pure Python with R-exact algorithm alignment (hspike emission calibration, gene-level Viterbi in copy-ratio space, R-equivalent denoise + subcluster Tumor calling), plus Numba/CUDA-accelerated kernels.

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Why cnvturbo?

Feature	R inferCNV	infercnvpy	cnvturbo
Cell-level Tumor/Normal HMM	✓	✗ (cluster score only)	✓
HMM i6 + hspike emission	✓	✗	✓ (analytic + MAD-robust)
Per-chromosome Viterbi (copy-ratio)	✓	✗	✓
Denoise (segment-length filter)	✓	✗	✓
Reference subcluster handling	✓	partial	✓
GPU / Numba acceleration	✗	✗	✓
Runtime (P12, 7,269 cells)	~5 hr	~9 min	~86 s
Cell-level concordance with R	1.000 (ref)	0.81	1.000

Verified on 3 PDAC samples (15,135 cells total): cell-level Tumor/Normal classification 100% identical to R inferCNV's HMM output, while running 100–200× faster. See Benchmark below.

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Installation

From PyPI (recommended)

pip install cnvturbo

With acceleration backends

# CPU acceleration (Numba)
pip install "cnvturbo[hmm-cpu]"

# GPU acceleration (PyTorch)
pip install "cnvturbo[hmm-gpu]"

# All accelerators + EM fitting
pip install "cnvturbo[hmm]"

Development install

git clone https://github.com/LogicByteCraft/cnvturbo.git
cd cnvturbo
pip install -e ".[dev,test]"

Requirements

• Python ≥ 3.10
• scanpy ≥ 1.10, anndata ≥ 0.7.3, numpy ≥ 1.20, pandas ≥ 1
• Optional: numba ≥ 0.57 (CPU), torch ≥ 2.0 (GPU), hmmlearn ≥ 0.3 (EM)

---

Quick start

import scanpy as sc
import cnvturbo
from cnvturbo import tl as cnv_tl, pl as cnv_pl

adata = sc.read_h5ad("my_sample.h5ad")
adata.layers["counts"] = adata.X.copy()

cnv_tl.infercnv_r_compat(
    adata,
    raw_layer="counts",
    reference_key="cell_type",
    reference_cat=["NK", "Endothelial", "Fibroblast"],
    window_size=101,
    min_mean_expr_cutoff=0.1,    # R inferCNV default for 10x; use 1.0 for Smart-seq2
    apply_2x_transform=True,
    n_jobs=16,
)

emit_means, emit_stds = cnv_tl.compute_hspike_emission_params(
    adata,
    raw_layer="counts",
    reference_key="cell_type",
    reference_cat=["NK", "Endothelial", "Fibroblast"],
    min_mean_expr_cutoff=0.1,    # 必须与 infercnv_r_compat 保持一致
    output_space="copy_ratio",
)

cnv_tl.hmm_call_subclusters(
    adata,
    use_rep="cnv",
    reference_key="cell_type",
    reference_cat=["NK", "Endothelial", "Fibroblast"],
    precomputed_emit_means=emit_means,
    precomputed_emit_stds=emit_stds,
    leiden_resolution="auto",
    cluster_by_groups=True,
    min_segment_length=5,
    min_segments_for_tumor=1,
    key_added="cnv_call",
    n_jobs=16,
)

print(adata.obs["cnv_call"].value_counts())

After this, adata.obs["cnv_call"] contains "Tumor" / "Normal" per cell, and adata.obs["cnv_call_score"] carries a continuous CNV burden score (mean(|X_cnv − 1.0|) in copy-ratio space).

---

Detailed usage

1. Prepare AnnData

cnvturbo requires:

• Raw integer counts in adata.X or adata.layers["counts"].
• Gene coordinates in adata.var: columns chromosome, start, end.
• A reference annotation in adata.obs: a column identifying normal cells (e.g., NK / Endothelial / Fibroblast).

Add gene coordinates from a GTF:

from cnvturbo.io import genomic_position_from_gtf

genomic_position_from_gtf(
    gtf_file="Homo_sapiens.GRCh38.110.gtf.gz",
    adata=adata,
)

2. R-compatible preprocessing (infercnv_r_compat)

Reproduces R inferCNV's pipeline exactly:

0. Low-expression gene filter — mean(raw_count) < min_mean_expr_cutoff (R require_above_min_mean_expr_cutoff; 10x default 0.1, Smart-seq2 1.0)
1. Library-size normalization → median depth
2. log2(x + 1)
3. First reference subtraction (gene-space, "bounds" mode)
4. Clip to ±3 (default)
5. Per-chromosome same-length pyramid smoothing (window=101)
6. Per-cell median centering
7. Second reference subtraction (gene-space)
8. 2^x → copy-ratio (neutral ≈ 1.0)

cnv_tl.infercnv_r_compat(
    adata,
    raw_layer="counts",
    reference_key="cell_type",
    reference_cat=["NK", "Endothelial"],
    max_ref_threshold=3.0,
    window_size=101,
    exclude_chromosomes=("chrX", "chrY"),
    min_mean_expr_cutoff=0.1,    # R inferCNV default for 10x; set 1.0 for Smart-seq2; 0 to disable
    apply_2x_transform=True,
    n_jobs=16,
    key_added="cnv",
)

Output:

• adata.obsm["X_cnv"] — (n_cells × n_genes_filtered) copy-ratio matrix
• adata.uns["cnv"]["chr_pos"] — gene-level chromosome offsets
• adata.uns["cnv"]["kept_var_names"] — original var_names that survived min_mean_expr_cutoff + chrX/chrY exclusion (matches obsm["X_cnv"] columns)
• adata.uns["cnv"]["min_mean_expr_cutoff"] — actual cutoff applied (provenance)

3. hspike emission calibration (compute_hspike_emission_params)

Mirrors R's hidden_spike simulation: builds a synthetic genome (50% CNV / 50% neutral chromosomes), samples the simulation base from real reference cells, runs the full pipeline, and extracts emission parameters per CNV state.

emit_means, emit_stds = cnv_tl.compute_hspike_emission_params(
    adata,
    raw_layer="counts",
    reference_key="cell_type",
    reference_cat=["NK", "Endothelial"],
    min_mean_expr_cutoff=0.1,    # 必须与 infercnv_r_compat 保持一致
    n_sim_cells=100,
    n_genes_per_chr=400,
    output_space="copy_ratio",
)

4. HMM cell-level Tumor calling (hmm_call_subclusters)

R-equivalent decoder: per-group Leiden subclustering (cluster_by_groups=True, auto resolution), per-chromosome Viterbi with R's pnorm-based emission, segment-length denoise, "subcluster contains ≥1 CNV segment ⇒ Tumor" rule.

cnv_tl.hmm_call_subclusters(
    adata,
    use_rep="cnv",
    reference_key="cell_type",
    reference_cat=["NK", "Endothelial"],
    precomputed_emit_means=emit_means,
    precomputed_emit_stds=emit_stds,
    leiden_resolution="auto",
    cluster_by_groups=True,
    n_neighbors=20,
    n_pcs=10,
    min_segment_length=5,
    min_segments_for_tumor=1,
    use_r_viterbi=True,
    key_added="cnv_call",
    backend="auto",
    n_jobs=16,
)

Output (added to adata.obs):

• cnv_call — "Tumor" / "Normal" per cell
• cnv_call_score — continuous CNV burden (mean(|X_cnv − 1.0|))
• cnv_call_subcluster — Leiden subcluster id used for HMM

5. Visualization

cnv_tl.pca(adata, use_rep="cnv")
cnv_tl.umap(adata)
cnv_pl.chromosome_heatmap(adata, groupby="cnv_call")

import scanpy as sc
sc.pl.embedding(adata, basis="cnv_umap", color=["cnv_call", "cnv_call_score"])

---

Benchmark

Three pancreatic adenocarcinoma samples (P07 = 3,659 cells, P12 = 7,269 cells, P30 = 4,207 cells); reference group = NK + Endothelial + Fibroblast (~50% of all cells).

Sample	R inferCNV (runtime)	cnvturbo (runtime)	Speed-up	cnvturbo cell-level Accuracy vs R
P07CRX_T (3,659)	2.5 h	64 s	140×	1.000
P12HWZ_T (7,269)	5.0 h	86 s	210×	1.000
P30WJJ_T (4,207)	3.5 h	54 s	230×	1.000

cnvturbo's per-cell Tumor / Normal classification is identical to R inferCNV's HMM output across all 15,135 cells.

The "ground truth" was reconstructed directly from R's pred_cnv_regions.dat + cell_groupings to bypass a known fuzzy-match bug in some user post-processing scripts.

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API overview

cnvturbo
├── tl                              # tools
│   ├── infercnv                    # original sliding-window scoring
│   ├── infercnv_r_compat           # R-exact 8-step pipeline (recommended)
│   ├── compute_hspike_emission_params  # hspike-based HMM emission calibration
│   ├── hmm_call_subclusters        # subcluster-level R-equivalent HMM caller
│   ├── hmm_call_cells              # cell-level HMM caller (no subclustering)
│   ├── cnv_score, cnv_score_cell   # CNV burden scores
│   ├── ithcna, ithgex              # intra-tumor heterogeneity
│   ├── pca, umap, tsne, leiden     # CNV-space embeddings (Scanpy wrappers)
│   └── copykat                     # CopyKAT integration (optional, requires R)
├── pp                              # preprocessing utilities
├── pl                              # plotting
├── io                              # GTF / genomic-position helpers
└── datasets                        # bundled tutorial data

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Design highlights

• R-exact pipeline: infercnv_r_compat reproduces the full 8 R inferCNV steps in gene-space copy-ratio (vs. window-space log2 used by older Python ports).
• HMM i6 cell-level calling: hmm_call_subclusters reproduces R's HMM Viterbi decoder, denoising, and per-subcluster Tumor classification — typically absent from existing Python implementations.
• Performance kernels: Numba parallel CPU / PyTorch GPU back-ends for sliding-window convolution and batched Viterbi (backend="auto" | "cpu" | "cuda").
• Robust to reference contamination: emission std uses MAD (median absolute deviation) × 1.4826 instead of plain std, so reference cells contaminated by tumor cells don't inflate state widths.

A high-level infercnv / cnv_score / chromosome_heatmap API similar to the de facto Python convention is also exposed for ease of migration.

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Citation

If you use cnvturbo in your research, please cite this implementation:

@software{cnvturbo,
  title  = {cnvturbo: GPU/Numba-accelerated scRNA-seq CNV inference with R inferCNV-compatible HMM i6},
  url    = {https://github.com/LogicByteCraft/cnvturbo},
  year   = {2026}
}

cnvturbo's algorithm is a faithful port of R inferCNV; please cite the upstream methodology as well when relevant.

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License

BSD 3-Clause License — see LICENSE.

Acknowledgements

cnvturbo is inspired by and stays algorithmically aligned with:

• inferCNV — reference R implementation of the HMM i6 pipeline.
• Scanpy / AnnData — single-cell analysis ecosystem.

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Contributing

Issues and pull requests are welcome at https://github.com/LogicByteCraft/cnvturbo. Before contributing:

pip install -e ".[dev,test]"
pre-commit install
pytest