flashdeconv 0.1.4

Fast Linear Algebra for Scalable Hybrid Deconvolution of Spatial Transcriptomics

FlashDeconv

Fast Linear Algebra for Scalable Hybrid Deconvolution

Unlocking atlas-scale spatial biology with randomized numerical linear algebra.

FlashDeconv is a high-performance spatial transcriptomics deconvolution method designed for atlas-scale and subcellular-resolution platforms (Visium HD, Stereo-seq, Xenium). It leverages structure-preserving randomized sketching to estimate cell type proportions with linear scalability—processing 1 million spots in ~3 minutes on commodity hardware.

Paper: Chen Yang, Jun Chen, Xianyang Zhang. FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching. bioRxiv, 2025. DOI: 10.64898/2025.12.22.696108

Reproducibility: To reproduce figures and benchmarks from the paper, visit the flashdeconv-reproducibility repository.

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Key Features

• Ultra-fast & Scalable: Deconvolve 1 million spots in ~3 minutes. Time and memory scale linearly O(N) with dataset size.
• Hardware Friendly: No GPU required. Runs efficiently on laptops (e.g., 32GB RAM handles 1M spots).
• Rare Cell Detection: Uses leverage-score sampling to preserve transcriptomically distinct but low-abundance cell types (e.g., Tuft cells, endothelial cells) that variance-based methods systematically miss.
• Spatially Aware: Sparse graph Laplacian regularization ensures spatial coherence without the O(N²) cost of dense kernel methods.
• Visium HD Ready: Specifically optimized for the extreme sparsity and scale of subcellular resolution technologies (2µm–16µm bin sizes).
• Statistically Rigorous: Log-CPM normalization with leverage-weighted gene selection preserves both common and rare cell populations.

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Installation

# From PyPI (recommended)
pip install flashdeconv

# With scanpy/anndata integration
pip install flashdeconv[io]

For development:

# From source
git clone https://github.com/cafferychen777/flashdeconv.git
cd flashdeconv
pip install -e ".[dev]"

Requirements: Python ≥ 3.9, numpy, scipy, numba. Optional: scanpy, anndata for AnnData workflow.

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Quick Start

With Scanpy/AnnData

import scanpy as sc
import flashdeconv as fd

adata_st = sc.read_h5ad("visium_hd.h5ad")
adata_ref = sc.read_h5ad("reference.h5ad")

fd.tl.deconvolve(adata_st, adata_ref, cell_type_key="cell_type")

adata_st.obsm["flashdeconv"]          # Cell type proportions
sc.pl.spatial(adata_st, color="flashdeconv_Hepatocyte")

With NumPy

from flashdeconv import FlashDeconv

model = FlashDeconv()
proportions = model.fit_transform(Y, X, coords)  # (n_spots, n_cell_types)

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Algorithm Under the Hood

FlashDeconv reformulates spatial deconvolution as Graph-Regularized Non-Negative Least Squares, solved in a compressed "sketch" space via randomized numerical linear algebra (RandNLA):

Figure 1. Overview of the FlashDeconv framework. (A) Input data preprocessing with Log-CPM normalization and gene selection. (B) Structure-preserving randomized sketching using leverage-score weighting to compress gene space while preserving rare cell signals. (C) Spatial graph construction and regularized optimization via Block Coordinate Descent. (D) Final cell type proportion estimates for each spatial location.

Three-Stage Framework

1. Preprocessing & Gene Selection

   • Log-CPM normalization: Stabilizes variance and prevents high-expression genes from dominating the sketch
   • Leverage-weighted gene selection: Combines highly variable genes (HVGs) with cell-type-specific markers, weighted by statistical leverage scores. Unlike variance (which conflates abundance with informativeness), leverage scores identify genes that define transcriptomically distinct directions, preserving rare cell type markers.

2. Structure-Preserving Sketching

   • Randomized projection: Compress gene space (~20,000 genes → 512 dimensions) using CountSketch with leverage-score importance sampling
   • Johnson-Lindenstrauss guarantee: Preserves Euclidean distances between cell type signatures with high probability
   • Key innovation: Leverage-weighted sampling amplifies rare cell type markers relative to housekeeping genes, preventing signal loss during hash collisions

3. Spatial Graph Regularization

   • Sparse graph Laplacian: Constructs k-NN spatial graph (O(N) memory vs. O(N²) for dense kernels like CARD)
   • Numba-accelerated Block Coordinate Descent (BCD): Fast closed-form updates with non-negativity constraints
   • Linear scalability: Spatial term complexity O(N·k) enables million-spot analysis

Why This Works

• Log-CPM bounds dynamic range while preserving sparsity (log1p(0) = 0)
• Leverage scores decouple biological identity from population abundance—markers of rare cell types (0.1% frequency) receive equal weight to abundant types (30% frequency)
• Sparse graph Laplacian encodes spatial autocorrelation as a Gaussian Markov Random Field (GMRF) without dense matrix operations

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Benchmarks

FlashDeconv exhibits linear O(N) scaling for both time and memory:

Dataset Size	Runtime	Memory	Hardware
10K spots	< 1 sec	< 1 GB	MacBook Pro M2 Max
100K spots	~4 sec	~2 GB	(32GB unified memory)
1M spots	~3 min	~21 GB	No GPU required

Accuracy on Synthetic Benchmarks (Spotless suite):

• Pearson correlation: 0.944 (mean across 56 datasets spanning 6 tissues)
• RMSE: 0.065 (median)
• Rare cell detection (AUPR): 0.960 ± 0.036 (standard deviation)

Real-world validation:

• Mouse liver (Visium): JSD = 0.056, ranking 3rd among 13 methods
• Melanoma tumor (Visium): JSD = 0.027, ranking 5th among 13 methods
• Reference stability: Ranked 1st for robustness to different scRNA-seq protocols

FlashDeconv matches top-tier Bayesian methods (Cell2Location, RCTD) on accuracy while accelerating inference by orders of magnitude.

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API Reference

fd.tl.deconvolve

fd.tl.deconvolve(
    adata_st,                        # Spatial AnnData
    adata_ref,                       # Reference AnnData
    cell_type_key="cell_type",       # Column in adata_ref.obs
    key_added="flashdeconv",         # Key for results in adata_st
    random_state=0,                  # Random seed for reproducibility
    copy=False,                      # If True, return copy instead of inplace
)

Results stored in adata_st:

• .obsm["flashdeconv"] — Cell type proportions (DataFrame)
• .obs["flashdeconv_dominant"] — Dominant cell type per spot
• .uns["flashdeconv_params"] — Parameters used

FlashDeconv Class

class FlashDeconv:
    def __init__(
        self,
        sketch_dim=512,              # Sketch space dimension
        lambda_spatial="auto",       # Spatial regularization (auto-tuned)
        rho_sparsity=0.01,           # L1 sparsity penalty
        n_hvg=2000,                  # Number of highly variable genes
        n_markers_per_type=50,       # Marker genes per cell type
        spatial_method="knn",        # "knn", "radius", or "grid"
        k_neighbors=6,               # k for k-NN graph
        max_iter=100,                # BCD max iterations
        tol=1e-4,                    # Convergence tolerance
        preprocess="log_cpm",        # "log_cpm", "pearson", or "raw"
        random_state=0,              # Random seed for reproducibility
        verbose=False,
    ): ...

    def fit(self, Y, X, coords, gene_names=None, cell_type_names=None) -> self
    def fit_transform(self, Y, X, coords, **kwargs) -> np.ndarray
    def get_cell_type_proportions(self) -> np.ndarray
    def get_abundances(self) -> np.ndarray
    def get_dominant_cell_type(self) -> np.ndarray
    def summary(self) -> dict

Parameters

Parameter	Type	Default	Description
sketch_dim	int	512	Dimension of sketch space (higher = more info, slower)
lambda_spatial	float or "auto"	"auto"	Spatial regularization strength (auto-tuned to data scale)
rho_sparsity	float	0.01	L1 sparsity penalty
n_hvg	int	2000	Number of highly variable genes to select
n_markers_per_type	int	50	Top markers per cell type
k_neighbors	int	6	Neighbors for spatial graph
max_iter	int	100	Maximum BCD iterations
tol	float	1e-4	Convergence tolerance
preprocess	str	"log_cpm"	Preprocessing: "log_cpm" (recommended), "pearson", or "raw"
random_state	int	0	Random seed for reproducibility (scanpy convention)

Attributes (After Fitting)

Attribute	Shape	Description
beta_	(n_spots, n_cell_types)	Raw cell type abundances
proportions_	(n_spots, n_cell_types)	Normalized proportions (sum to 1)
gene_idx_	(n_selected,)	Indices of genes used
lambda_used_	float	Actual λ value used
info_	dict	Optimization info (converged, n_iterations, final_objective)
cell_type_names_	array	Cell type names (if provided)

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Input Data Formats

FlashDeconv accepts multiple input formats:

Spatial Data (Y)

• NumPy array: Dense (n_spots, n_genes)
• SciPy sparse matrix: CSR/CSC format (recommended for Visium HD to reduce memory usage)
• AnnData: .X or specified layer (e.g., adata.layers["counts"])

Reference (X)

• NumPy array: Dense (n_cell_types, n_genes) signature matrix
• AnnData: Automatically aggregated from single-cell data via prepare_data() using mean expression per cell type

Coordinates

• NumPy array: (n_spots, 2) for 2D spatial coordinates, or (n_spots, 3) for 3D (e.g., z-stacked sections)
• From AnnData: Automatically extracted from .obsm["spatial"], .obsm["X_spatial"], or .obs[["x", "y"]]

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Reference Data Quality

The algorithm is only as good as the reference data it's given.

Deconvolution accuracy depends critically on reference quality. Before running FlashDeconv, ensure your reference data meets these criteria:

Requirement	Threshold	Why It Matters
Cells per type	≥500	Fewer cells → unstable signatures
Marker expression	≥80% cells	Low expression → identity not captured
Marker fold-change	≥5×	Low FC → cannot distinguish from others
Signature correlation	<0.95	High correlation → algorithm cannot separate

Common failure modes:

• Rare cell types with <200 cells produce noisy signatures
• Cell types annotated only by positive markers may include contaminants
• Similar subtypes (e.g., T cell subsets) often have correlation >0.98

📖 For detailed guidance: See Building High-Quality Reference Data — a comprehensive guide covering dual-marker annotation, QC pipelines, and troubleshooting.

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Citation

If you use FlashDeconv in your research, please cite:

Plain text:

Yang, C., Chen, J. & Zhang, X. FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching. bioRxiv (2025). https://doi.org/10.64898/2025.12.22.696108

BibTeX:

@article{yang2025flashdeconv,
  title={FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching},
  author={Yang, Chen and Chen, Jun and Zhang, Xianyang},
  journal={bioRxiv},
  year={2025},
  doi={10.64898/2025.12.22.696108},
  url={https://doi.org/10.64898/2025.12.22.696108}
}

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Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

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License

This project is licensed under the BSD-3-Clause License.

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Related Resources

• Paper Reproducibility: flashdeconv-reproducibility — Complete code to reproduce all figures and benchmarks
• Documentation: ReadTheDocs (coming soon)
• Issues & Support: GitHub Issues

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Acknowledgments

We thank the developers of Spotless, Cell2Location, and RCTD for their benchmarking frameworks and methodological contributions to the spatial transcriptomics field.