vsparse

vsparse provides VCSCArray/VCSRArray: Value-Compressed Sparse Column/Row (VCSC/VCSR) array types, implemented in NumPy and accelerated with Numba. The array types are standalone -- they can be built from and converted to SciPy sparse arrays and used entirely without AnnData. vsparse also ships an optional AnnData integration (from_anndata, to_layer, and the VCSCAnnData subclass) for projects that want compressed arrays backed directly into an AnnData object.

VCSC/VCSR are compressed-sparse layouts inspired by IVSparse's VCSC. In addition to the usual compressed-sparse pointer/index arrays, nonzero values within each major-axis slice (columns for VCSC, rows for VCSR) are deduplicated: each unique value is stored once, alongside the list of minor-axis positions that share it. This is a strict memory win whenever values repeat heavily within a slice — as is typical for integer count matrices, e.g. single-cell RNA-seq counts.

Install

Install from source or via package manager:

pip install git+https://github.com/meyer-lab/vsparse.git
# or with uv
uv add git+https://github.com/meyer-lab/vsparse.git

For local development:

git clone https://github.com/meyer-lab/vsparse.git
cd vsparse
uv sync --all-extras --dev

Quickstart

Working with VCSC / VCSR arrays

VCSCArray/VCSRArray are standalone array types -- they only need a SciPy sparse array (or an AnnData object, if you have one) to be built, and none of the operations below require AnnData at all:

import vsparse
import scipy.sparse as sp

# Build directly from a SciPy sparse array -- no AnnData involved
csc = sp.random(1000, 500, density=0.1, format="csc")
v = vsparse.VCSCArray.from_scipy(csc)

# Or build from an AnnData object or SciPy sparse array
adata = ...  # an AnnData object
v = vsparse.from_anndata(adata)                   # VCSCArray (column-compressed) from adata.X
vr = vsparse.from_anndata(adata, format="csr")    # VCSRArray (row-compressed)

# Transposition is zero-copy (swaps major/minor axes and shares buffers)
vr = v.T                                       # VCSRArray

# Scalar arithmetic & math
v2 = v * 2.0                                   # Scalar multiplication
v_div = v / 2.0                                # Scalar division
v_neg = -v                                     # Negation
v_log = v.log1p()                              # Elementwise log1p

# Matrix & vector products (Numba-parallelized)
y = v @ x                                      # Matrix-vector: (n_rows, n_cols) @ (n_cols,) -> (n_rows,)
y_left = x @ v                                 # Vector-matrix: (n_rows,) @ (n_rows, n_cols) -> (n_cols,)
Y = v @ B                                      # Matrix-matrix: (n_rows, n_cols) @ (n_cols, k) -> (n_rows, k)
Y_left = B @ v                                 # Matrix-matrix: (k, n_rows) @ (n_rows, n_cols) -> (k, n_cols)

# Slicing
col_slice = v[:, [1, 3, 5]]                    # Fast major-axis slicing (returns VCSCArray)
sub = v[0:10, 0:10]                            # 2D slicing (falls back to scipy)

# Conversion & layers
sp_csc = v.to_scipy()                          # -> scipy.sparse.csc_array (or to_csr())
dense = v.toarray()                            # -> numpy.ndarray
vsparse.to_layer(adata, v, key="counts_vcsc")  # Attach to AnnData layer

VCSCAnnData: AnnData with direct VCSC/VCSR backing

For projects that want the compressed array wired directly into AnnData, vsparse.VCSCAnnData is an AnnData subclass whose X (and optionally raw_X) is backed directly by a VCSCArray or VCSRArray:

import vsparse

va = vsparse.VCSCAnnData.from_anndata(adata)   # Compresses X and raw.X
va.X                                           # VCSCArray
va.raw_X                                       # VCSCArray (separate from anndata's .raw)

# Persist to HDF5 (.h5ad) or Zarr with default Blosc2+LZ4 compression
va.write_h5ad("compressed.h5ad")               # Read back with VCSCAnnData.read_h5ad
va2 = vsparse.VCSCAnnData.read_h5ad("compressed.h5ad")

va.write_zarr("compressed.zarr")               # Read back with VCSCAnnData.read_zarr
va3 = vsparse.VCSCAnnData.read_zarr("compressed.zarr")

# Escape hatch back to standard AnnData
plain = va.to_anndata()

Byte-Packed On-Disk Format (IVCSC / IVCSR)

For smaller files, pass format="ivcsc" (or "ivcsr") on write. This byte-packs the minor-axis indices with delta + varint encoding (inspired by IVSparse's IVCSC).

It is purely an archival storage format: read_h5ad / read_zarr decompress the indices on load and return a standard VCSCArray/VCSRArray.

# Write delta+varint byte-packed indices
va.write_h5ad("archived.h5ad", format="ivcsc")

# Reads back directly as an ordinary VCSCAnnData
va_loaded = vsparse.VCSCAnnData.read_h5ad("archived.h5ad")

Fast Filtering & Depth Normalization (load_and_normalize)

For IVCSR-backed datasets, vsparse.load_and_normalize bypasses full array decompression for rapid preprocessing, reproducing the filtering and depth normalization from parafac2.normalize.prepare_dataset:

• Cell filtering without decoding: Computes cell totals in $O(n_{\text{unique}})$ time directly from group sizes without unpacking varint indices.
• Fused filter & normalization: Performs gene filtering, cell/gene depth-scaling, and $\log_{10}(1000x + 1)$ transform in parallel passes over the compact CSR representation.

adata_norm = vsparse.load_and_normalize(
    "archived.h5ad",
    min_cell_counts=10.0,      # Filter cells with counts <= 10
    gene_threshold=0.05,       # Filter genes with counts <= 0.05 * n_cells
)

See docs/ for full usage guides and API documentation.

Development

uv sync --all-extras --dev
uv run pytest
uv run ruff check .
uv run ty check
uv run sphinx-build -b html docs docs/_build/html