| """Benchmark visualization plots.""" |
|
|
| from __future__ import annotations |
|
|
| from typing import Optional |
|
|
| import numpy as np |
| import pandas as pd |
| import matplotlib.pyplot as plt |
| from anndata import AnnData |
|
|
| from .._constants import GAMMA |
| from .._utils import get_layer |
|
|
|
|
| def halflife_scatter( |
| adata: AnnData, |
| halflives_df: pd.DataFrame, |
| gene_col: str = "gene_symbol", |
| halflife_col: str = "half_life_hours", |
| ax: Optional[plt.Axes] = None, |
| **kwargs, |
| ) -> plt.Figure: |
| """Scatter plot of per-gene median gamma vs published half-lives. |
| |
| Parameters |
| ---------- |
| adata |
| Annotated data with ``gamma`` layer. |
| halflives_df |
| DataFrame with gene symbols and half-life measurements. |
| gene_col |
| Column name for gene symbols. |
| halflife_col |
| Column name for half-life values. |
| ax |
| Optional matplotlib Axes. |
| |
| Returns |
| ------- |
| matplotlib Figure. |
| """ |
| gamma = get_layer(adata, GAMMA) |
| median_gamma = pd.Series(np.median(gamma, axis=0), index=adata.var_names) |
| hl = halflives_df.set_index(gene_col)[halflife_col] |
| shared = median_gamma.index.intersection(hl.index) |
|
|
| if ax is None: |
| fig, ax = plt.subplots(figsize=(6, 5)) |
| else: |
| fig = ax.figure |
|
|
| g = median_gamma[shared].values |
| h = hl[shared].values |
|
|
| ax.scatter(h, g, alpha=0.6, s=20, **kwargs) |
| ax.set_xlabel("Half-life (hours)") |
| ax.set_ylabel("Median gamma") |
| ax.set_title(f"Gamma vs Half-life (n={len(shared)} genes)") |
|
|
| return fig |
|
|
|
|
| def enrichment_barplot( |
| results: list[dict], |
| ax: Optional[plt.Axes] = None, |
| **kwargs, |
| ) -> plt.Figure: |
| """Bar plot comparing gamma distributions for enrichment results. |
| |
| Parameters |
| ---------- |
| results |
| List of dicts from ``are_enrichment`` / ``nmd_enrichment``. |
| ax |
| Optional matplotlib Axes. |
| |
| Returns |
| ------- |
| matplotlib Figure. |
| """ |
| if ax is None: |
| fig, ax = plt.subplots(figsize=(6, 4)) |
| else: |
| fig = ax.figure |
|
|
| labels = [r["label"] for r in results] |
| in_set = [r.get("median_gamma_in_set", 0) for r in results] |
| background = [r.get("median_gamma_background", 0) for r in results] |
| p_values = [r.get("p_value", 1.0) for r in results] |
|
|
| x = np.arange(len(labels)) |
| width = 0.35 |
|
|
| ax.bar(x - width / 2, in_set, width, label="Gene set", **kwargs) |
| ax.bar(x + width / 2, background, width, label="Background", alpha=0.7) |
|
|
| |
| for i, p in enumerate(p_values): |
| if np.isfinite(p): |
| ax.text(x[i], max(in_set[i], background[i]) * 1.05, |
| f"p={p:.2e}", ha="center", fontsize=8) |
|
|
| ax.set_xticks(x) |
| ax.set_xticklabels(labels) |
| ax.set_ylabel("Median gamma") |
| ax.set_title("Enrichment Analysis") |
| ax.legend() |
|
|
| return fig |
|
|
