6. Communication communities

Language: Python · Input: edges_directed_KO_WT_filtered.csv

In this step, we will group the cell types into communication communities from the differential cell–cell network. The CrossTalkeR2 community-detection procedure runs on the edge list exported in step 4 and nothing else — no expression data is required.

Detection of communication communities

Currently, the function is still situated on the dev branch of the pyCrossTalkeR repository. Thus we install it to perform the clustering and generate the communication community plot.

pip install git+https://github.com/CostaLab/pyCrossTalkeR.git@dev

And then we can generate and plot the communication community layout:

from pathlib import Path
import pandas as pd
import pycrosstalker as pyct

BASE       = Path(Path("/path/to/Il1rn_KO"))
CTR_OUT    = BASE / "crosstalker"

df = pd.read_csv(CTR_OUT / "edges_directed_KO_WT_filtered.csv")

pyct.plots.cci_community_layout(
    df,
    method="leiden",
    res=1.3,
    compression=0.3,
    seed=12,
    spring_k=0.1,
    spring_iterations_community=1000,
    spring_iterations_nodes=500,
    edge_width_scale=2.5,
    edge_alpha_intra=1,
    edge_alpha_inter=1,
    node_size=300,
    arrow_size=20,
    figsize=[10, 10])

At res=1.3, four communities:

Community	Members	Direction in KO
Homeostasis (stem/progenitor)	HSC, MPPs, CMP	decreased communication
Myeloid–Vascular	monocytes, preDC, pericytes, sinusoidal	increased
Neutrophil	G0–G4	increased
Stromal	MSCs, fibroblasts, arteriolar	increased

MSCs carry the highest signal in the network, and stromal–stromal interactions are predominantly upregulated.

All following steps are for running the perturbation module of IntraTalker.

Next: Receptome construction