feat(preprocessing): add optimal shifted CLR (PFlog) normalization

docs/api/preprocessing.md

@@ -29,6 +29,7 @@ For visual quality control, see {func}`~scanpy.pl.highest_expr_genes` and
    pp.log1p
    pp.pca
    pp.normalize_total
+   pp.normalize_clr
    pp.regress_out
    pp.scale
    pp.sample

docs/references.bib

@@ -111,6 +111,16 @@ @article{Blondel2008
   pages     = {P10008},
 }
 
+@article{Booeshaghi2022,
+  author    = {Booeshaghi, A. Sina and Hallgrímsdóttir, Ingileif B. and Gálvez-Merchán, Ángel and Pachter, Lior},
+  title     = {Normalization for sampled count data},
+  year      = {2026},
+  url       = {https://doi.org/10.1101/2022.05.06.490859},
+  doi       = {10.1101/2022.05.06.490859},
+  publisher = {Cold Spring Harbor Laboratory},
+  journal   = {bioRxiv},
+}
+
 @article{Burczynski2006,
   author    = {Burczynski, Michael E. and Peterson, Ron L. and Twine, Natalie C. and Zuberek, Krystyna A. and Brodeur, Brendan J. and Casciotti, Lori and Maganti, Vasu and Reddy, Padma S. and Strahs, Andrew and Immermann, Fred and Spinelli, Walter and Schwertschlag, Ulrich and Slager, Anna M. and Cotreau, Monette M. and Dorner, Andrew J.},
   title     = {Molecular Classification of Crohn’s Disease and Ulcerative Colitis Patients Using Transcriptional Profiles in Peripheral Blood Mononuclear Cells},

docs/release-notes/4160.feat.md

@@ -0,0 +1 @@
+Add {func}`scanpy.pp.normalize_clr` for PFlog shifted centered log-ratio normalization, a variance-stabilizing, depth-invariant and rank-preserving count transform {cite:p}`Booeshaghi2022`. By default, it estimates negative-binomial overdispersion and uses the Anscombe pseudocount. The complete centered result is stored in `X` or the selected layer. Because CLR centering generally maps zeros to non-zero values, the output is dense. {smaller}`R Baber`

src/scanpy/preprocessing/__init__.py

@@ -9,7 +9,7 @@
 from ._harmony import harmony_integrate
 from ._hashsolo import hashsolo
 from ._highly_variable_genes import highly_variable_genes
-from ._normalization import normalize_total
+from ._normalization import normalize_clr, normalize_total
 from ._pca import pca
 from ._qc import calculate_qc_metrics
 from ._recipes import recipe_seurat, recipe_weinreb17, recipe_zheng17
@@ -37,6 +37,7 @@
     "highly_variable_genes",
     "log1p",
     "neighbors",
+    "normalize_clr",
     "normalize_total",
     "pca",
     "recipe_seurat",

src/scanpy/preprocessing/_normalization.py

@@ -7,6 +7,7 @@
 import numpy as np
 from fast_array_utils import stats
 from fast_array_utils.numba import njit
+from fast_array_utils.stats import mean_var
 
 from .. import logging as logg
 from .._compat import CSBase, CSCBase, CSRBase, DaskArray, warn
@@ -304,3 +305,187 @@ def normalize_total(  # noqa: PLR0912
     elif not inplace:
         return dat
     return None
+
+
+def _estimate_overdispersion(x: np.ndarray | CSBase | DaskArray) -> float:
+    r"""Estimate the negative-binomial overdispersion :math:`α` from raw counts.
+
+    Fits :math:`\mathrm{Var}_g = μ_g + α \cdot μ_g^2` across genes, where
+    :math:`μ_g` and :math:`\mathrm{Var}_g` are the per-gene mean and (population)
+    variance over cells. The model is linear in :math:`α`, so the ordinary
+    least-squares solution is closed form
+
+    .. math::
+        α = \frac{\sum_g (\mathrm{Var}_g - μ_g) \, μ_g^2}{\sum_g μ_g^4},
+
+    which is exactly the minimizer a non-linear `curve_fit` would converge to,
+    but without the dependency. :func:`~fast_array_utils.stats.mean_var` is
+    dispatched for dense, sparse and dask input alike, so the only dask-specific
+    step is computing the two final scalar sums.
+    """
+    mu, var = mean_var(x, axis=0, correction=0)
+    mu2 = mu**2
+    numerator = np.sum((var - mu) * mu2)
+    denominator = np.sum(mu2 * mu2)
+    if isinstance(x, DaskArray):
+        import dask
+
+        numerator, denominator = dask.compute(numerator, denominator)
+    if denominator == 0.0:
+        msg = (
+            "Cannot estimate overdispersion: every gene has zero mean. "
+            "Pass a positive `alpha` explicitly."
+        )
+        raise ValueError(msg)
+    alpha = float(numerator / denominator)
+    if not alpha > 0:
+        msg = (
+            f"Estimated overdispersion is non-positive (alpha = {alpha}); "
+            "pass a positive `alpha` explicitly."
+        )
+        raise ValueError(msg)
+    return alpha
+
+
+def _log1p_sparse_block(x: np.ndarray | CSBase) -> np.ndarray | CSBase:
+    """Apply log1p to a dense or sparse block while preserving sparse zeros."""
+    if isinstance(x, CSBase):
+        x = x.copy()
+        x.data = np.log1p(x.data)
+        return x
+    return np.log1p(x)
+
+
+def _normalize_clr_helper(
+    x: np.ndarray | CSBase | DaskArray,
+    *,
+    alpha: float | None,
+) -> tuple[np.ndarray | DaskArray, np.ndarray | DaskArray]:
+    """Compute the dense PFlog / shifted-CLR matrix and cell depths."""
+    # Keep the depths lazy for dask; `.ravel()` would otherwise materialize them.
+    cell_depths = stats.sum(x, axis=1)
+    if not isinstance(x, DaskArray):
+        cell_depths = np.asarray(cell_depths).ravel()
+
+    if alpha is None:
+        alpha = _estimate_overdispersion(x)
+    elif not alpha > 0:
+        msg = (
+            f"`alpha` must be positive to compute PFlog, got {alpha}. "
+            "The data may be underdispersed."
+        )
+        raise ValueError(msg)
+
+    x = x * (4.0 * float(alpha))
+
+    if isinstance(x, DaskArray):
+        log_values = x.map_blocks(
+            _log1p_sparse_block, dtype=np.float64, meta=x._meta.astype(np.float64)
+        )
+    else:
+        log_values = _log1p_sparse_block(x)
+
+    row_center = stats.sum(log_values, axis=1) / x.shape[1]
+    if not isinstance(row_center, DaskArray):
+        row_center = np.asarray(row_center).ravel()
+    if isinstance(log_values, CSBase):
+        log_values = log_values.toarray()
+    return log_values - row_center[:, None], cell_depths
+
+
+def normalize_clr(
+    adata: AnnData,
+    *,
+    alpha: float | None = None,
+    layer: str | None = None,
+    inplace: bool = True,
+    copy: bool = False,
+) -> AnnData | dict[str, np.ndarray | DaskArray] | None:
+    r"""Normalize counts with the shifted centered log-ratio (PFlog) transform.
+
+    If `alpha` is not provided, it is estimated from the input matrix. PFlog is
+    then computed as
+
+    .. math::
+        T(x)_i = \log(1 + 4 α x_i)
+            - \frac{1}{D} \sum_{j=1}^D \log(1 + 4 α x_j),
+
+    which is equivalent to centering
+    :math:`\log(x_i + 1 / (4 α))` because the constant :math:`\log(4 α)`
+    cancels during CLR centering.
+
+    .. note::
+        CLR centering generally maps zeros to non-zero values, so the resulting
+        matrix is dense even when the input is sparse.
+
+    Parameters
+    ----------
+    adata
+        The annotated data matrix of shape `n_obs` × `n_vars`.
+        Rows correspond to cells and columns to genes.
+    alpha
+        Negative-binomial overdispersion of the dataset (``var = μ + α·μ²``).
+        If `None`, it is estimated from the input matrix. A positive numeric
+        value uses that value directly. PFlog applies
+        ``log1p(4 * alpha * x)`` before CLR centering.
+    layer
+        Layer to normalize instead of `X`.
+    inplace
+        Whether to update `adata` or return a dictionary with the normalized
+        matrix.
+    copy
+        Whether to modify a copied input object. Not compatible with
+        `inplace=False`.
+
+    Returns
+    -------
+    Returns a dictionary with the normalized matrix or updates `adata`, depending
+    on `inplace`.
+
+    Example
+    -------
+    >>> import numpy as np
+    >>> from anndata import AnnData
+    >>> import scanpy as sc
+    >>> adata = AnnData(np.array([[1, 2, 30], [4, 50, 6]], dtype="float32"))
+    >>> sc.pp.normalize_clr(adata, alpha=0.5)
+    >>> np.allclose(adata.X.sum(axis=1), 0, atol=1e-5)
+    True
+    """
+    if copy:
+        if not inplace:
+            msg = "`copy=True` cannot be used with `inplace=False`."
+            raise ValueError(msg)
+        adata = adata.copy()
+
+    view_to_actual(adata)
+
+    x = _get_arr(adata, layer=layer)
+    if isinstance(x, CSCBase):
+        x = x.tocsr()
+    if not inplace:
+        x = x.copy()
+    if issubclass(x.dtype.type, int | np.integer):
+        x = x.astype(np.float64)
+
+    start = logg.info("normalizing counts per cell via PFlog")
+
+    x, cell_depths = _normalize_clr_helper(x, alpha=alpha)
+
+    if not isinstance(cell_depths, DaskArray) and not np.all(cell_depths > 0):
+        warn("Some cells have zero counts", UserWarning)
+
+    dat = dict(X=x)
+    if inplace:
+        _set_obs_rep(adata, x, layer=layer)
+
+    logg.info(
+        "    finished ({time_passed})",
+        time=start,
+    )
+
+    if copy:
+        return adata
+    elif not inplace:
+        return dat
+    return None