clria 1.0.9

Package to decipher LRI-mediated brain network communication

CLRIA

CLRIA: connectome-constrained ligand-receptor interaction analysis for understanding brain network communication

Installation

• Install from PyPI: pip install clria

• install from source:

  git clone [e-mail]:duzc-Repos/CLRIA.git
  cd CLRIA
  pip install .
  

• [Optional] install pytorch for GPU acceleration, see pytorch.org for detail.

Usage

Quick example

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import clria

## 0. simulation data
n_rank = 5
n_region = 100
n_lrpair = 20
eps = 1e-100
r, A, B, C, L, R, TL, TR, M, (M1, M2) = clria.otsim.simu_data(
            n_r=n_rank, n1=n_region, n_lr=n_lrpair,             ## r, A, B, C
            TL=10, TR=15,                                       ## L, R, TL, TR
            scale_factor=1e4, epsilon=1,                        ## M, 
            is_decomposition=True, n_d=10, solver="nmf",        ## (M1, M2)
            eps=eps)

## 1. solver
epsi, tau = 0, 1
r = 5
mmot_obj = clria.ottl.MMOTNTD(L, R, M, TL, TR, epsilon=epsi, tau1=tau, tau2=tau,)
mmot_obj.fit(r=r)
(Ae, Be, Ce) = mmot_obj.factors
tca_obj = clria.ottl.TCA(Ae, Be, Ce)

## 2. plotting
annot = pd.DataFrame({ i:[f"network_{i//20+1}"] for i in range(100) }).T ## random generate annotation
clria.otpl.plot_sankey(tca_obj, annot, figure_size=(600, 700))

Notebook

• See ./tutorial/tutorial.ipynb for an application of CLRIA to publicly available gene expression (AHBA) and diffusion MRI (HCP) data. The data used in this tutorial can be found at https://zenodo.org/records/15005583

• The code to replicate the analysis of the study are available at https://zenodo.org/records/13906808.

API

Here is a brief list of the functionalities provided by this repository. Additional information is provided in their docstring or by calling help(function_name).

preprocessing: otpp

• otpp.LRdatabse(lrdb, ...):
  • .extract_lr_expression(self, expr, ...): extract ligand and receptor expression from gene expression matrix.
  • .generate_coupling_matrix(self, ...): generate two matrix $T_L, T_R$ recoding the correspondence between LR pairs and ligand or receptor.
• otpp.BNCM():
  • .proximal_to_distance(self, SC): transfrom the fiber density measure to distance measures.
  • .calc_navigation(self, L, D): calculate navigation measure based on $L$ (strength-to-length remapping of the connection weight) and $D$ (distance matrix, e.g. Euclidean distance between nodes)

tools: ottl

• ottl.MMOTNTD(L, R, M, TL, TR, ...)
  • .fit(self, r): solve LR-mediated communication patterns $A, B, C$.
• ottl.gen_PermM(surf_files, info, ...): generate spatial-preserving permutation idx.
• ottl.TCA(A, B, C):
  • .calc_T_statistics_matrix(self): infer region-wise asymmetrix signaling across all LR pairs.
  • .analyze_trans_hierarchical_signaling_cortex(self, hierarchy, ...): infer trans-hierarchical asymmetrix signaling between cortical regions by KS test.
  • .analyze_trans_hierarchical_signaling_cortex_spin(self, hierarchy, ...): infer trans-hierarchical asymmetrix signaling between cortical regions by spin test.
  • .analyze_trans_hierarchical_signaling_cortex_and_subcortex(self, hierarchy, ...): infer trans-hierarchical asymmetrix signaling between cortical and subcortex regions by KS test.
  • .graph_spectrum_analysis(self, hamonics, SC): energy density analysis of asymmetrix signal of each LR pair.
  • .get_pathway_network(self, pathway_name, ...): reconstruct pathway-specific communication network from communication pattters.
  • .get_lr_network(self, idx, ...): reconstruct LR-specific communication network from communication pattters.
  • .identify_dominant_LR_from_Ce(self, ...): identify domiant LR pairs for each tensor factor.
  • .prepare_ROI_from_Ae_Be(self, atlas_nii, ...): prepare ROI files of each tensor factor for Neurosynth analysis.
• ottl.UOTRegression(pattern, ...)
  • .fit(src, dst, ...): solve UOT regression between source state and target state.
  • .get_opt_UOT_dist(self, beta, P): compute UOT distance value.
  • .get_opt_obj_val(self, beta, P, src, dst): compute objective function values.
• ottl.OTRegression(pattern, ...): an balanced version of UOTRegression
  • .fit(src, dst, ...): solve OT regression between source state and target state.
  • .get_opt_OT_dist(self, beta, P): compute OT distance value.
  • .get_opt_obj_val(self, beta, P, src, dst): compute objective function values.

plotting: otpl

• .otpl.plot_sankey(tca_obj, annot, ...): sankey plot of communication patterns, LR or pathway communication network.
• .otpl.plot_circos(tca_obj, annot, name, ...): circos plot of communication patterns, LR or pathway communication network.

simmulation: otsim

• otsim.simu_data(n_r, n1, n2, n_lr, ...): simulating data using inverse optimal transport (iOT).