| """Rank genes by differential gamma across PT states.""" |
|
|
| from __future__ import annotations |
|
|
| import numpy as np |
| import pandas as pd |
| from anndata import AnnData |
|
|
| from .._constants import GAMMA, PT_STATE |
| from .._utils import get_layer, require_layers, require_obs, log_params |
|
|
|
|
| def rank_pt_genes( |
| adata: AnnData, |
| groupby: str = PT_STATE, |
| method: str = "t-test", |
| n_genes: int = 100, |
| ) -> pd.DataFrame: |
| """Rank genes by differential degradation rate across groups. |
| |
| Uses scanpy's ``rank_genes_groups`` on the gamma matrix. |
| |
| Parameters |
| ---------- |
| adata |
| Annotated data matrix with ``gamma`` layer and group labels. |
| groupby |
| Column in ``adata.obs`` defining groups. |
| method |
| Statistical test: ``'t-test'``, ``'wilcoxon'``, etc. |
| n_genes |
| Number of top genes to return per group. |
| |
| Returns |
| ------- |
| DataFrame with ranked genes per group. |
| """ |
| import scanpy as sc |
|
|
| require_layers(adata, GAMMA) |
| require_obs(adata, groupby) |
|
|
| gamma = get_layer(adata, GAMMA) |
|
|
| |
| gamma_adata = AnnData(X=gamma.copy()) |
| gamma_adata.obs_names = adata.obs_names.copy() |
| gamma_adata.var_names = adata.var_names.copy() |
| gamma_adata.obs[groupby] = adata.obs[groupby].values |
|
|
| sc.tl.rank_genes_groups( |
| gamma_adata, |
| groupby=groupby, |
| method=method, |
| n_genes=n_genes, |
| ) |
|
|
| |
| adata.uns["rank_pt_genes"] = gamma_adata.uns["rank_genes_groups"] |
|
|
| |
| result = sc.get.rank_genes_groups_df(gamma_adata, group=None) |
|
|
| log_params(adata, "rank_pt_genes", { |
| "groupby": groupby, |
| "method": method, |
| "n_genes": n_genes, |
| }) |
|
|
| return result |
|
|
