destriper 0.1.1

Estimate and correct row/column multiplicative stripe artifacts in high-resolution spatial transcriptomics count data.

estriper

Destriper corrects striping artefacts in VisiumHD spatial transcriptomics count data.

Under the hood, the method fits a negative binomial model to each nucleic bin $ij$ total counts $k_{ij}$: $k_{ij} \sim NB(\text{mean} = c_p h_i w_j, \text{dispersion} = \theta)$, where $h_i$, $w_j$ are row/column stripe factors, and $c_p$ a per-nucleus concentration [counts/bin]. The fitted stripe factors are then applied to all bins to produce destriped counts.

• The underlying mean-variance relationship reads $\text{var} = \mu + \theta\mu^2$.
• Fitted stripe factors $h_i$ and $w_j$ equal 1 on average.

Cite

To learn more about the method, or to cite, use:

Paola Malsot, Malte Londschien, Valentina Boeva, Gunnar Rätsch, Striping artifact removal in VisiumHD data through nuclear counts modeling, Bioinformatics, Volume 42, Issue Supplement_1, July 2026, btag306, https://doi.org/10.1093/bioinformatics/btag306

Install

pip install destriper            # low-level interface
pip install "destriper[anndata]" # + AnnData interface

Tutorial

See the tutorial notebook for an example on Visium HD mouse brain data.

Usage

Low-level interface

import destriper as ds

# nucleus bins only; counts are per-bin TOTAL counts
result = ds.fit(
    counts,          # 1-D int array
    row_indices,     # 1-D int array (array-grid row)
    column_indices,  # 1-D int array (array-grid col)
    nucl_labels,     # 1-D array of nucleus ids (non-null)
    cv="spatial",    # "spatial" | "default" | int (KFold) | per-bin group array
    max_iter_theta=5,
    max_iter=100_000,
)

result.row_factors        # pandas Series h_i
result.col_factors        # pandas Series w_j
result.nucl_concentration # pandas Series c_p
result.dispersion         # fitted NB theta

# apply to ALL bins (nucleus bins -> quantile matching, others -> division)
corrected_totals = ds.destripe_tot_counts(
    tot_counts, row_indices, column_indices, nucl_labels, result
)

# rescale the sparse count matrix to the corrected totals
corrected_matrix, achieved_totals = ds.rescale(count_matrix, corrected_totals)

achieved_totals can differ from corrected_totals on originally-empty bins: a zero row cannot be rescaled to a positive target, so it stays zero. Both are returned so the discrepancy is explicit.

Anndata interface

import destriper as ds

result = ds.fit_adata(
    adata,
    nucl_key="nucleus_id",
    count_key="total_counts",   # None -> compute from adata.X
    row_key="array_row",
    col_key="array_col",
)

ds.destripe_adata(adata, result, source_layer=None, target_layer="destriped")
# writes adata.layers["destriped"], adata.obs["ds_destripe_factor"], adata.obs["ds_corrected_counts"]
# and adata.uns["destriper"]["result"]

Fitting uses only bins with a non-null nucl_key; unlabelled (cytoplasm) bins are ignored during fitting and corrected by division at destriping time.

Notes

• glum==3.1.2 and tabmat==4.2.1 are pinned exactly — the coordinate-descent solver relies on private internals of those versions.
• This package is derived from the destriping-GLM repo.