| # scPTR |
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| **Single-Cell Post-Transcriptional Regulatory Decomposition** |
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| scPTR estimates per-cell, per-gene mRNA degradation rates from scRNA-seq spliced/unspliced counts and uses them as a primary analytical axis — complementary to RNA velocity. |
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| ## What scPTR does |
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| - **Degradation rate estimation**: Per-cell, per-gene gamma from kinetic steady-state relationships with kNN Gaussian-kernel smoothing |
| - **Expression-invisible states**: Discovers cell subpopulations with distinct post-transcriptional programs undetectable by standard expression analysis |
| - **Post-transcriptional velocity**: Neighbor-averaged gamma gradient that captures degradation dynamics orthogonal to RNA velocity |
| - **RBP-target networks**: Library-size-corrected inference of RNA-binding protein regulatory networks with elastic net |
| - **DeepPTR**: Structured VAE with a kinetic decoder that disentangles transcriptional and post-transcriptional latent spaces |
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| ## Installation |
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| ```bash |
| pip install . |
| ``` |
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| Optional dependencies: |
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| ```bash |
| pip install ".[deep]" # PyTorch for DeepPTR |
| pip install ".[datasets]" # Pooch for dataset downloads |
| pip install ".[dev]" # pytest for testing |
| ``` |
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| ## Quick start |
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| ```python |
| import scptr |
| |
| # Load data with spliced/unspliced layers |
| adata = scptr.read_h5ad("your_data.h5ad") |
| |
| # Preprocessing |
| scptr.pp.filter_genes(adata) |
| scptr.pp.normalize_layers(adata) |
| scptr.pp.neighbors(adata) |
| scptr.pp.smooth_layers(adata) |
| |
| # Estimate rates |
| scptr.tl.estimate_beta(adata) |
| scptr.tl.estimate_gamma(adata) |
| |
| # Downstream analysis |
| scptr.tl.variance_decomposition(adata) |
| scptr.tl.pt_states(adata) |
| scptr.tl.pt_velocity(adata) |
| scptr.tl.infer_network(adata) |
| ``` |
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| ## Pipeline overview |
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| ``` |
| Raw scRNA-seq (spliced + unspliced) |
| -> Gene/cell filtering |
| -> Library-size normalization (per layer) |
| -> kNN graph + Gaussian smoothing |
| -> Beta estimation (quantile regression on u/s phase portraits) |
| -> Gamma estimation (gamma = beta * u / s, per cell per gene) |
| -> Variance decomposition (transcriptional vs post-transcriptional) |
| -> PT states (PCA + Leiden clustering in gamma-space) |
| -> PT velocity (neighbor-averaged gamma gradient) |
| -> RBP-target network inference (elastic net, library-size corrected) |
| ``` |
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| ## Validation |
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| scPTR gamma estimates have been validated against: |
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| | Validation | Result | |
| |------------|--------| |
| | Published mRNA half-lives | ρ = −0.81 (sci-fate), −0.33 to −0.40 (10x developmental) | |
| | Method comparison | Outperforms scVelo steady-state (−0.37) and velVI (−0.28) | |
| | miRNA target enrichment | 59% of 215 families enriched (p = 4.7×10⁻⁶⁵) | |
| | 3′ UTR sequence features | UTR length ρ = 0.34 (p < 10⁻²⁰⁰), AU content ρ = 0.30 | |
| | DepMap CRISPR essentiality | Hub RBPs more essential (p = 6.4×10⁻⁵) | |
| | Subsampling robustness | r > 0.97 at 20% subsampling | |
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| ## Key findings |
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| - **Expression-invisible states**: 3/8 pancreatic and 6/11 hippocampal cell types harbor post-transcriptional subpopulations undetectable by expression analysis (confirmed by zero-permutation control, ARI ≈ 0), enriched for ER stress/autophagy and synaptic plasticity pathways |
| - **Temporal precedence**: degradation-rate changes precede expression changes for 54% of transition genes in pancreas (p < 10⁻⁵⁷) and 78% in dentate gyrus (p = 9.9×10⁻¹³) |
| - **RBP networks**: library-size-corrected inference identifies essential hub regulators (HNRNPA1, YBX1, ELAVL1/HuR); neuroblastoma shows 66% stabilizing edges vs. destabilizing bias in developmental tissues |
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| ## Datasets |
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| Built-in dataset loaders (downloaded via Pooch): |
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| ```python |
| adata = scptr.datasets.pancreas() # Mouse endocrinogenesis (3,696 cells) |
| adata = scptr.datasets.dentate_gyrus() # Mouse hippocampal neurogenesis (2,930 cells) |
| adata = scptr.datasets.sci_fate() # Human A549 dexamethasone response (7,404 cells) |
| ``` |
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| ## Requirements |
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| - Python >= 3.9 |
| - anndata >= 0.8, scanpy >= 1.9, numpy >= 1.21, scipy >= 1.7, numba >= 0.55 |
| - Optional: torch >= 2.0 (DeepPTR), pooch >= 1.6 (datasets) |
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| ## Citation |
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| If you use scPTR, please cite: |
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| > scPTR: Decomposing Post-Transcriptional Regulation at Single-Cell Resolution (2026) |
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