Overview

Data

• Ligand-receptor data is available at uniprot, GEO.

• Single-cell RNA sequencing data can be downloaded from the GEO database. (melanoma (accession code:GSE72056), head and neck squamous cell carcinomas (HNSCC) (accession code: GSE103322), and colorectal cancer (CRC) (accession code: GSE81861)).

Package Environment

Install Python 3.9.15 for running this code. And these packages should be satisfied:

• seaborn >= 0.12.0
• pandas >= 1.5.3
• numpy >= 1.25.2
• scikit-learn >= 1.2.1
• matplotlib >= 3.7.2
• xgboost == 1.6.2
• lightgbm == 3.3.0
• KTBoost == 0.2.2
• gpboost == 0.7.10

Feature Acquisition

iFeature

Cell-cell communication tools for comparative analysis

CellPhoneDB, NATMI, iTALK, CellChat, CellComNet, CellEnBoost

Basic Tutorial

See a Basic tutorial how to Use a Cell-Cell Communication Analysis Computing Framework Based on Ligand Receptor and Single Cell RNA Sequencing Data (CellDialog). The tutorial with the test data takes minutes to complete!
The following is also a brief introduction to Usage.

Usage

• scRNA-seq data: Each column is a normalised gene/protein expression profile of a cell type or an individual cell. An example snapshot of the abundance matrix is shown below. And the scRNA-seq data was processed by code/Processing_scRNA-seq_data.py

	cell_type_0	cell_type_1	cell_type_2	...
Gene1	1.56	2.84	53	...
Gene2	13	3.2	42.4	...
Gene3	0.245	0	0.66	...
...	...	...	...	...

1. First, run the model, the default 5 fold cross-validation, get LRI pairs. Or the user can user-specified LRI database directly, skip this step to the second step.

python code/CellDialog.py

2. The second step, Run the three-point estimation method, including [(cell expression)(expression product)(expression thresholding)]. (Note: the user-specified database only needs to replace the LRI.csv file and the corresponding format in the file.)

• Ligand-Receptor Interactions ：The interaction data should be represented as a two-column table, with the first column containing ligands and the second column containing receptors (as in the following example).

Ligand	Receptor
LIGAND1	RECEPTOR1
LIGAND2	RECEPTOR2
LIGAND3	RECEPTOR2
LIGAND4	RECEPTOR3
LIGAND4	RECEPTOR4
...	...

python example/The_three-point_estimation_method.py

3. Finally, output the strength of cell-cell communication.

Hardware Environment

This code was run on a computer with the following specifications:

• CPU: AMD EPYC 7302
• Memory: 256GB DDR4
• GPU: GeForce RTX 2080 Ti

However, the minimal requirements for running this protocol are:

• CPU: Intel Core i7-11800H
• Memory: 16GB DDR4
• GPU: NVIDIA GeForce RTX 3050Ti
• Storage: At least 10GB available

CCC tool computational time and memory analysis.

	CellDialog	CellPhoneDB	NATMI	iTALK	CellChat	CellComNet	CellEnBoost
Time (S)	194.72	85.56	35.22	10.31	183.04	1366.07	4104.3
Memory (MB)	164.89	387.64	102.7	724.42	1186.59	166.65	186.82