Skip to content

Cell-cell communication#

Motivation#

Cell-cell communication analysis offers insights into the interactions among different cell types within a tissue or tumor. These interactions form intricate networks that can reveal mechanisms associated with alterations in the tumor microenvironment and disease progression. To decipher this complex orchestration, we utilize well-established methods from the literature, specifically CellChat and LIANA.

Step-by-step#

1. Running pipeline#

1.1. On HPC#

HPC

  • workflow_level = nonMalignant
  • input_source_groups = all
  • input_target_groups = all
  • input_cellchat_annotation = Secreted Signaling
nextflow run main.nf --workflow_level nonMalignant --project_name Training --sample_csv sample_table.csv --meta_data meta_data.csv --cancer_type Ovarian -resume -profile seadragon

1.2. On Cirro#

Alternatively, we execute this task on Cirro.

Cirro

  • Defining the pipeline entrypoint = nonMalignant
  • Source cell type names = all
  • Target cell type names = all
  • CellChat interactions type = Secreted Signaling

On Cirro, users should (Do not run):

  • Navigate to the Pipelines tab and enter "BTC scRNA Pipeline" in the search engine.
  • Change the Dataset to BTC Training dataset and the Copy Parameters From option to Run_01.
  • Double-check the aforementioned parameters and click Run.

2. Inspecting report#

For convenience the figures can be located in the Test_communication_report.html report within the Run_02 dataset.

2.1. LIANA output#

The bubble plot illustrates the interactions between ligand-receptor (L-R) pairs across various cell types, including interaction specificity and expression magnitude metrics. Interaction specificity measures the degree of L-R exclusivity among cell types, i.e., putative a preferential "communication" pathway. Meanwhile, expression magnitude indicates the strength of L-R interactions within a cell population.

Image caption

The heatmap displays the interaction directionality between Sender and Receiver populations.

Image caption

2.2. CellChat output#

Alternatively, we can explore results obtained exclusively from CellChat. The network displays various metrics related to interaction strength and frequency across populations. These can be further divided into cell-based plots, as detailed below.

Image caption

Image caption

3. Exercise: Manipulating cell-cell communication database#

Question

What happens when switching the interaction type to Cell-Cell Contact? A: Run_Cell_Contact

Please note: When configuring the pipeline on Cirro, ensure that the Dataset is set to BTC Training dataset and select Run_02 for the Copy Parameters From option. Additionally, configure the Entrypoint parameter to nonMalignant.

Tip: Accelerate the process by skipping DEG and Doublets analyses

Reference#

  1. Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data
  2. Inference and analysis of cell-cell communication using CellChat