multiscoresplot 2.1.0

Multi-dimensional gene set scoring and visualization for single-cell transcriptomics

multiscoresplot

Multi-dimensional gene set scoring and visualization for single-cell transcriptomics.

Color dimensionality reduction plots (UMAP, PCA, etc.) using a multi-dimensional color space derived from gene set scores — so you can visualize the activity of multiple gene programs simultaneously in a single plot.

Installation

pip install multiscoresplot

For interactive Plotly plots:

pip install 'multiscoresplot[interactive]'

Quick Start

import multiscoresplot as msp

# Define gene sets of interest
gene_sets = {
    "qNSCs": ["Id3", "Aldoc", "Slc1a3", ...],
    "aNSCs": ["Egfr", "Ascl1", "Mki67", ...],
    "TAP":   ["Dll1", "Dcx", "Neurod1", ...],
    "NB":    ["Dcx", "Sox11", "Tubb3", ...],
}

# 1. Score gene sets per cell
scores = msp.score_gene_sets(adata, gene_sets, inplace=True)

# 2. Map scores to RGB colors (returns RGBResult with metadata)
rgb = msp.reduce_to_rgb(scores, method="pca")

# 3. Plot — method & gene_set_names auto-detected from RGBResult
msp.plot_embedding(adata, rgb, basis="X_umap")

Pipeline

multiscoresplot follows a 3-step pipeline:

Step 1 — Score gene sets

Calculate per-cell gene set scores using pyUCell. Scores are stored in adata.obs as score-<name> columns with values in [0, 1].

scores = msp.score_gene_sets(adata, gene_sets, inplace=True)

# Options
scores = msp.score_gene_sets(
    adata, gene_sets,
    max_rank=1500,    # rank cap (tune to median genes per cell)
    chunk_size=1000,  # cells per batch
    n_jobs=-1,        # parallelism (-1 = all cores)
    inplace=False,    # don't store in adata.obs
)

Step 2 — Map scores to RGB

Convert gene set scores into per-cell RGB colors. Both functions return an RGBResult that carries the RGB array plus metadata (method, gene set names, colors) used automatically by the plotting functions.

Blend (2–3 gene sets) — multiplicative blending from white, where each gene set darkens toward its base color proportional to the score.

# Default colors (2 sets: blue/red, 3 sets: R/G/B)
rgb = msp.blend_to_rgb(scores)

# Custom colors
rgb = msp.blend_to_rgb(scores, colors=[(1, 0, 0), (0, 0.5, 1)])

Reduce (2+ gene sets) — dimensionality reduction maps scores to 3 RGB channels. Built-in methods: PCA, NMF, and ICA.

rgb = msp.reduce_to_rgb(scores, method="pca")  # default
rgb = msp.reduce_to_rgb(scores, method="nmf")
rgb = msp.reduce_to_rgb(scores, method="ica")

Or use a callable directly for one-off reductions:

rgb = msp.reduce_to_rgb(scores, method=my_custom_fn, component_prefix="MY")

Note: In reduction mode, RGB channels are learned statistical axes. Similar colors indicate similar projected profiles, not necessarily identical biology — see the Pipeline Guide for interpretation caveats.

Step 3 — Plot embedding

Scatter plot of embedding coordinates colored by RGB values, with an integrated color-space legend.

# Static matplotlib plot — method & gene_set_names auto-detected from RGBResult
msp.plot_embedding(adata, rgb, basis="X_umap")

# Options
ax = msp.plot_embedding(
    adata, rgb,
    basis="X_umap",
    legend=True,              # show color legend (default)
    legend_style="inset",     # "inset" or "side"
    legend_loc="lower right", # legend position
    legend_size=0.30,         # legend size (fraction of plot)
    legend_resolution=128,    # legend image resolution
    point_size=3,
    alpha=0.8,
    figsize=(6, 6),
    dpi=100,                  # figure resolution
    title="SVZ lineage",
    show=False,               # return axes instead of displaying
)

Interactive plot (requires plotly) — WebGL-accelerated scatter plot with hover info showing gene set scores, RGB channel values, and custom metadata.

msp.plot_embedding_interactive(
    adata, rgb,
    basis="X_umap",
    scores=scores,
    hover_columns=["n_counts", "cell_type", "Dcx"],  # obs columns or gene names
    legend=True,
    legend_loc="lower right",
    point_size=2,
    figsize=(6.5, 6.0),      # figure size in inches
    dpi=100,                  # pixels = figsize * dpi
)

Optional — Standalone legend

The plotting functions above include an integrated legend by default. If you need to render the legend separately (e.g., for a custom figure layout):

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

# Direct mode (2-set square or 3-set triangle)
msp.render_legend(ax, "direct", gene_set_names=["A", "B", "C"])

# Reduction mode (RGB triangle with component labels)
msp.render_legend(ax, "pca")
msp.render_legend(ax, "nmf", component_labels=["NMF1", "NMF2", "NMF3"])

One-Step Convenience Function

For quick exploration, plot_scores wraps the entire pipeline in a single call:

scores, rgb, ax = msp.plot_scores(adata, gene_sets, basis="X_umap")

It auto-selects blend_to_rgb for ≤ 3 gene sets and reduce_to_rgb(method="pca") for more. All scoring, color-mapping, and plotting parameters are accepted as keyword arguments.

Extensibility — Custom reducers

Register your own dimensionality reduction method:

def my_umap_reducer(X, n_components, **kwargs):
    """X: (n_cells, n_gene_sets), returns (n_cells, 3) in [0, 1]."""
    import umap
    embedding = umap.UMAP(n_components=n_components, **kwargs).fit_transform(X)
    # min-max normalize each column to [0, 1]
    for j in range(embedding.shape[1]):
        col = embedding[:, j]
        lo, hi = col.min(), col.max()
        if hi > lo:
            embedding[:, j] = (col - lo) / (hi - lo)
    return embedding

msp.register_reducer("umap", my_umap_reducer, component_prefix="UMAP")

# Now use it like any built-in method
rgb = msp.reduce_to_rgb(scores, method="umap")

Documentation

Full documentation is available at AndreMacedo88.github.io/multiscoresplot, including:

• Getting Started — installation and quick start
• Pipeline Guide — detailed pipeline tutorial
• API Reference — full function signatures and parameters
• Examples — custom reducers, plot customization, and more

API Reference

Function / Class	Description
plot_scores(adata, gene_sets)	One-step convenience: score → RGB → plot
score_gene_sets(adata, gene_sets)	Score gene sets per cell via pyUCell
blend_to_rgb(scores)	Multiplicative blend to RGB (2–3 sets) → RGBResult
reduce_to_rgb(scores, method="pca")	Dimensionality reduction to RGB (2+ sets) → RGBResult
RGBResult	RGB array + metadata (method, gene set names, colors)
plot_embedding(adata, rgb, basis=...)	Static matplotlib scatter plot
plot_embedding_interactive(adata, rgb, basis=...)	Interactive Plotly scatter plot
render_legend(ax, method)	Draw color-space legend on axes
register_reducer(name, fn)	Register a custom reduction method
get_component_labels(method)	Get axis labels for a reduction method

Development

git clone https://github.com/AndreMacedo88/multiscoresplot.git
cd multiscoresplot

# Install in editable mode with dev dependencies
pip install -e ".[interactive]"
pip install ruff pre-commit pytest pytest-cov mypy

# Run tests
pytest

# Lint & format
ruff check src/ tests/
ruff format src/ tests/

# Type check
mypy src/

# Set up pre-commit hooks
pre-commit install

License

MIT