| """Per-layer library-size normalization.""" |
|
|
| from __future__ import annotations |
|
|
| import numpy as np |
| from anndata import AnnData |
| from scipy.sparse import issparse |
|
|
| from .._constants import UNSPLICED, SPLICED |
| from .._utils import log_params |
|
|
|
|
| def normalize_layers( |
| adata: AnnData, |
| target_sum: float | None = None, |
| layers: tuple[str, ...] = (SPLICED, UNSPLICED), |
| ) -> None: |
| """Library-size normalize spliced and unspliced layers independently. |
| |
| Each cell's counts in each layer are divided by the cell's total |
| counts in that layer and multiplied by *target_sum* (defaults to |
| the median library size across cells for that layer). |
| |
| Modifies *adata* in place — layers are converted to dense float32. |
| |
| Parameters |
| ---------- |
| adata |
| Annotated data matrix. |
| target_sum |
| Target total counts per cell. If ``None``, use the median. |
| layers |
| Which layers to normalize. |
| """ |
| for layer in layers: |
| mat = adata.layers[layer] |
| if issparse(mat): |
| mat = np.asarray(mat.todense(), dtype=np.float32) |
| else: |
| mat = np.asarray(mat, dtype=np.float32) |
|
|
| lib_sizes = mat.sum(axis=1, keepdims=True) |
| lib_sizes = np.clip(lib_sizes, 1e-10, None) |
|
|
| if target_sum is None: |
| ts = np.median(lib_sizes) |
| else: |
| ts = target_sum |
|
|
| mat = mat / lib_sizes * ts |
| adata.layers[layer] = mat |
|
|
| log_params(adata, "normalize_layers", { |
| "target_sum": target_sum, |
| "layers": list(layers), |
| }) |
|
|
