Investigating bilateral symmetry¶

This notebook uses cect, the C. elegans Connectome Toolbox, to investigate how bilateral symmetry in C. elegans changes over development

Source of this file on GitHub

1) Install the cect package using pip¶

If the package has not already been installed, use pip to install it.

In [1]:

#!pip install -q cect # uncomment this line to install the latest version 
# of cect from PyPI if you haven't already

print(f"cect version: {__import__('cect').__version__} is installed")

#!pip install -q cect # uncomment this line to install the latest version # of cect from PyPI if you haven't already print(f"cect version: {__import__('cect').__version__} is installed")

cect version: 0.2.7 is installed

2) Import methods from cect¶

In [2]:

# Helper method to load data sets by name
from cect.Utils import get_connectome_dataset       
# Helper method to apply a specific "view" (e.g. only Sensory or motor neurons) to a connectome dataset
from cect.ConnectomeView import get_view    
# Method to convert a connectivity matrix into a binary array indicating symmetric connections
from cect.Analysis import convert_to_symmetry_array         

# Helper method to load data sets by name from cect.Utils import get_connectome_dataset # Helper method to apply a specific "view" (e.g. only Sensory or motor neurons) to a connectome dataset from cect.ConnectomeView import get_view # Method to convert a connectivity matrix into a binary array indicating symmetric connections from cect.Analysis import convert_to_symmetry_array

cect      >>>  Opened the Excel file for Ripoll Sanchez et al: /opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/cect/data/1-s2.0-S0896627323007560-mmc7.xlsx

3) Specify connectome datasets to use, views to examine, and extract symmetry info¶

In [3]:

datasets = ["Witvliet1", "Witvliet2", "Witvliet3", "Witvliet4", "Witvliet5", \
    "Witvliet6", "Yim2024", "White_L4", "Witvliet7", "Witvliet8", "Cook2019Herm"]
views = ["SensorySomaticH", "MotorSomaticH", "InterneuronsSomaticH", "Neurons"] 
synclass = "Chemical"

symmetries = {} # to store the percentage of symmetric connections

for dataset_name in datasets:
    cds = get_connectome_dataset(dataset_name, from_cache=True)

    for view in views:
        v = get_view(view) # get the ConnectomeView object for the current view name

        if v.name not in symmetries: symmetries[v.name] = []
        cds_view = cds.get_connectome_view(v)

        conns, percentage, sym_info = convert_to_symmetry_array(cds_view, [synclass])
        symmetries[v.name].append(percentage)

datasets = ["Witvliet1", "Witvliet2", "Witvliet3", "Witvliet4", "Witvliet5", \ "Witvliet6", "Yim2024", "White_L4", "Witvliet7", "Witvliet8", "Cook2019Herm"] views = ["SensorySomaticH", "MotorSomaticH", "InterneuronsSomaticH", "Neurons"] synclass = "Chemical" symmetries = {} # to store the percentage of symmetric connections for dataset_name in datasets: cds = get_connectome_dataset(dataset_name, from_cache=True) for view in views: v = get_view(view) # get the ConnectomeView object for the current view name if v.name not in symmetries: symmetries[v.name] = [] cds_view = cds.get_connectome_view(v) conns, percentage, sym_info = convert_to_symmetry_array(cds_view, [synclass]) symmetries[v.name].append(percentage)

4) Plot bilateral symmetry across datasets¶

In [4]:

import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10, 5))
ax.set_ylabel("Symmetry (%)", fontsize=12)

for view in symmetries:
    ax.plot(datasets, symmetries[view], marker="o", linestyle="-", \
        label=view, linewidth=3 if view == "Neurons" else 1,)

plt.setp(ax.get_xticklabels(), rotation=45, ha="right")

ax.legend()
plt.show()

import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(10, 5)) ax.set_ylabel("Symmetry (%)", fontsize=12) for view in symmetries: ax.plot(datasets, symmetries[view], marker="o", linestyle="-", \ label=view, linewidth=3 if view == "Neurons" else 1,) plt.setp(ax.get_xticklabels(), rotation=45, ha="right") ax.legend() plt.show()