MultiNeuronChat

MultiNeuronChat is a Python library for inferring condition‑related changes in synaptic cell‑cell communication from single‑cell / single‑nucleus RNA‑seq (sc/snRNA‑seq) datasets in case vs control study designs.
It builds on the mathematical model of Zhao et al. 2023 method NeuronChat and extends it to multi‑condition comparisons with subject‑level statistics.

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Installation

From PyPI:

pip install multineuronchat

Python ≥ 3.10 is required.

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Quick start (minimal end-to-end)

MultiNeuronChat expects a cell-wise log-normalized gene expression matrix in Loom format. You can either normalize your matrix manually or use the provided implementation:

from multineuronchat.normalize import cell_wise_log_normalization

cell_wise_log_normalization(
    # Path to the input Loom file 
    path_to_loom="path/to/data.loom",
    # Path to the cell-wise log-normalized output Loom file
    path_to_normalized_loom='path/to/cellwise_normalized_data.loom',
    # Whether to print computational progress
    verbose=True
)

This log-normalized loom file can then be used to run MultiNeuronChat:

from multineuronchat.MultiNeuronChatObject import MultiNeuronChatObject

# 1) Configure your analysis
mnc = MultiNeuronChatObject(
    condition_label_column="condition",           # column attribute in Loom
    condition_names=("control", "case"),          # order matters
    subject_label_column="subject",               # column attribute in Loom
    cell_type_label_column="cell_types",          # column attribute in Loom
    db="human_extended"                           # "human", "mouse", "human_extended", or path to custom DB
)

# 2) Compute communication scores
mnc.compute_communication_scores(
    # Path to the cell-wise log-normalized Loom file
    path_to_data_loom="path/to/cellwise_normalized_data.loom",
    # The row name of the gene attribute in the Loom file
    gene_label_row='gene',
    # The number of processes to use for the parallel computations of communication scores
    n_processes=4,
    # The minimum number of cells of a specific cell type within a specific subject to include in the analysis
    min_n_cells_threshold=20,
    # Whether to print computational progress
    verbose=True
)

# 3) (Optional) Focus hypotheses with masks (Wasserstein / EMD)
from multineuronchat.masks import compute_wasserstein_mask
mask = compute_wasserstein_mask(mnc, top_percentile=99.0)

# 4) Significance testing and multiple testing correction
pvals = mnc.compute_significance(
    # The statistical test to use when comparing the communication score distributions between conditions
    statistical_test='KS',
    # Optional mask to focus the significance testing on specific interactions. If None, all interactions are tested.
    mask=mask,
    # The number of resamples to use for permutation testing when appropriate
    n_resamples=10_000,
    # The random state for reproducibility
    random_state=42
)

# 5) Control FDR (Benjamini–Yekutieli by default; use "bh" for Benjamini–Hochberg)
pvals_adj = mnc.correct_p_values(statistical_test="KS", method="by")

Input data requirements

For MultiNeuronChat to function, your input Loom file must meet the following criteria:

• Row attributes:
  • genes that were measured (gene)
• Column attributes:
  • labels assigning a subject to each cell (subject in the example above but can be configured)
  • condition labels (condition in the example above but can be configured)
  • cell type labels (cell_types in the example above but can be configured)

Gene symbols should match the selected interaction database (human, mouse, or your custom DB).