| """Variance decomposition into transcriptional and post-transcriptional fractions.""" |
|
|
| from __future__ import annotations |
|
|
| import numpy as np |
| from anndata import AnnData |
|
|
| from .._constants import ( |
| SMOOTHED_UNSPLICED, |
| GAMMA, |
| TF_SCORE, |
| PTF_SCORE, |
| ) |
| from .._utils import get_layer, require_layers, log_params |
|
|
|
|
| def variance_decomposition(adata: AnnData) -> None: |
| """Decompose gene expression variance into TF and PTF components. |
| |
| In log space: |
| - ``TF_g = Var(log(u_g)) / (Var(log(u_g)) + Var(log(gamma_g)))`` |
| - ``PTF_g = 1 - TF_g`` |
| |
| Results are stored in ``adata.var['tf_score']`` and ``adata.var['ptf_score']``. |
| |
| Parameters |
| ---------- |
| adata |
| Annotated data matrix with smoothed unspliced layer and gamma. |
| """ |
| require_layers(adata, SMOOTHED_UNSPLICED, GAMMA) |
|
|
| u = get_layer(adata, SMOOTHED_UNSPLICED) |
| gamma = get_layer(adata, GAMMA) |
|
|
| |
| log_u = np.log1p(u) |
| log_gamma = np.log1p(gamma) |
|
|
| var_u = np.var(log_u, axis=0) |
| var_gamma = np.var(log_gamma, axis=0) |
|
|
| total_var = var_u + var_gamma |
| |
| total_var = np.clip(total_var, 1e-10, None) |
|
|
| tf_score = var_u / total_var |
| ptf_score = var_gamma / total_var |
|
|
| adata.var[TF_SCORE] = tf_score.astype(np.float32) |
| adata.var[PTF_SCORE] = ptf_score.astype(np.float32) |
|
|
| log_params(adata, "variance_decomposition", {}) |
|
|
