pha-plots

Transcriptome visualisation utilities for PhatnaniLab.

Installation

pip install pha-plots

Install with optional development dependencies:

pip install "pha-plots[dev]"

Usage

Cortical slices

draw_frontal_cortex and draw_motor_cortex each accept a list of per-layer numeric values (outermost layer first), a colormap, and optional normalization bounds. Frontal cortex uses 7 layers (I–VI and white matter); motor cortex uses 6 (no layer IV).

import matplotlib.pyplot as plt
from pha_plots import draw_frontal_cortex, draw_motor_cortex

fig, (ax_f, ax_m) = plt.subplots(1, 2, figsize=(4, 3))

draw_frontal_cortex(
    ax_f,
    values=[0.1, 0.4, 0.8, 0.3, 0.6, 0.9, 0.5],  # 7 layers
    cmap="viridis",
    vmin=0, vmax=1,
)

draw_motor_cortex(
    ax_m,
    values=[0.2, 0.5, 0.7, 0.4, 0.8, 0.6],  # 6 layers
    cmap="viridis",
    vmin=0, vmax=1,
)

for ax in (ax_f, ax_m):
    ax.set_aspect("equal")
    ax.autoscale()
    ax.axis("off")

Pass is_sig (a list of booleans, one per layer) to mark significant layers with an annotation character:

draw_frontal_cortex(
    ax, values, "viridis", vmin=0, vmax=1,
    is_sig=[False, False, True, False, True, False, False],
    sig_annotation_char="*",
)

Spinal cord cross-section

draw_spinal_cord accepts a dictionary mapping anatomical region IDs to values. Any region absent from the dictionary is filled with nan_color (default 'lightgray').

Recognized region IDs:

from pha_plots import draw_spinal_cord

fig, ax = plt.subplots(figsize=(3, 3))

draw_spinal_cord(
    ax,
    values={
        "Dors_Horn":      0.9,
        "Vent_Horn":      0.7,
        "Med_Grey":       0.4,
        "Dors_Med_White": 0.2,
        "Vent_Med_White": 0.3,
    },
    cmap="plasma",
    vmin=0, vmax=1,
)

ax.set_aspect("equal")
ax.autoscale()
ax.axis("off")

Significance annotations use the same is_sig / sig_annotation_char pattern, but is_sig is a dict[str, bool] keyed by region ID:

draw_spinal_cord(
    ax, values, "plasma", vmin=0, vmax=1,
    is_sig={"Dors_Horn": True, "Vent_Horn": False},
)

Colorbar

draw_colorbar creates a colorbar inset directly inside an existing axis at a bounding box given in data coordinates (x0, y0, width, height), so it moves and scales with the plot.

from pha_plots.utils import draw_colorbar

cbar, cax = draw_colorbar(
    ax,
    xycoords=(x_max + 0.02, y_min, 0.04, y_max - y_min),
    cmap="viridis",
    vmin=0, vmax=1,
    orientation="vertical",
    label="Expression (normalised)",
)

cbar is the matplotlib.colorbar.Colorbar instance; cax is the inset Axes on which it was drawn. Both are returned so tick positions, labels, and other properties can be adjusted after the call:

cbar.set_ticks([0, 0.5, 1])
cax.yaxis.set_tick_params(labelsize=6)

Combining all four

import matplotlib.pyplot as plt
from pha_plots import draw_frontal_cortex, draw_motor_cortex, draw_spinal_cord
from pha_plots.utils import draw_colorbar

CMAP, VMIN, VMAX = "viridis", 0.0, 1.0

fig, axes = plt.subplots(1, 3, figsize=(8, 3))

draw_frontal_cortex(
    axes[0],
    values=[0.1, 0.4, 0.8, 0.3, 0.6, 0.9, 0.5],
    cmap=CMAP, vmin=VMIN, vmax=VMAX,
)
draw_motor_cortex(
    axes[1],
    values=[0.2, 0.5, 0.7, 0.4, 0.8, 0.6],
    cmap=CMAP, vmin=VMIN, vmax=VMAX,
)
draw_spinal_cord(
    axes[2],
    values={"Dors_Horn": 0.9, "Vent_Horn": 0.7, "Med_Grey": 0.4},
    cmap=CMAP, vmin=VMIN, vmax=VMAX,
)

for ax in axes:
    ax.set_aspect("equal")
    ax.autoscale()
    ax.axis("off")

# Add a shared colorbar just outside the spinal cord panel.
x0, x1 = axes[2].get_xlim()
y0, y1 = axes[2].get_ylim()
draw_colorbar(
    axes[2],
    xycoords=(x1 + 0.02 * (x1 - x0), y0, 0.04 * (x1 - x0), y1 - y0),
    cmap=CMAP, vmin=VMIN, vmax=VMAX,
    label="Expression",
)

plt.tight_layout()
plt.savefig("expression_figure.pdf", bbox_inches="tight")

Development setup

git clone https://github.com/PhatnaniLab/phatnani_transcriptome_plots.git
cd phatnani_transcriptome_plots
pip install -e ".[dev]"
pytest

Releasing

1. Create and push a git tag matching v* (e.g. v0.1.0).
2. Draft a GitHub Release from that tag.
3. The publish workflow builds the distribution, pushes to TestPyPI, then promotes to PyPI automatically.

Trusted publishing — configure an OIDC publisher for pha-plots on PyPI/TestPyPI (no API tokens needed). See the PyPA guide.

License

MIT — see LICENSE.