CellPhoneDB
CellPhoneDB addresses the challenges of studying cell-cell communication in scRNA-seq and spatial data. It allows researchers to move beyond just measuring gene expression and delve into the complicated cellular communication world. By analyzing the scRNA-seq or spatial data through the lens of CellPhoneDB, researchers can identify potential signaling pathways and communication networks between different cell types within the sample. Partek Flow wrapped the statistical analysis pipeline (method 2) from CellPhoneDB v5 [1][2] for this purpose.
How to use CellPhoneDB
Invoke the CellPhoneDB task from a normalized counts data node using the Exploratory analysis section. We recommend running CellPhoneDB on the log normalized data directly.
To run CellPhoneDB task,
Click a Normalized counts data node
Click the Exploratory analysis section in the toolbox
Click CellPhoneDB
Species
Currently only human, mouse and rat are supported. Select the species of the data from the drop-down list.
Cell type
Select the attribute from the drop down. Any categorical attribute associated with the data can be selected, though typically the task is performed on cell typing results.
Micro environment
The micro environment file is typically used when analysing spatial data (see the tool's documentation), and it is optional. The micro environment information can be added to the task using the text box in the task. This can be simply copy-pasted from a micro environment file, a .txt with two columns indicating which cell type (1st column) is in which spatial microenvironment (end column).
P-value
Specify the p-value cutoff to employ for significance
Threshold
By default, the value of 0.10 will be used as threshold to select which cells are used for the analysis in the cluster. However, the number could be adjusted manually or typed in directly.
Click the Finish button if you want to run the task as default.
Double click the CellPhoneDB result data node will open the task report in Data Viewer. It is a heatmap that summarizes how many significant interactions identified in the cell type pairs.
To explore more, the task of Explore CellPhoneDB results under Exploratory analysis on the pop-up menu. It allows you to focus on specific cell type pairs and genes of interest. Genes of interest are data dependent and usually come from the published results of similar studies or the differential gene analysis between different conditions (eg, cancer patient vs healthy controls). Once set up, click the Finish button to submit the job.
Double click the Output matrix data node will open the task report in Data Viewer. It is another variant of heatmap that displays how genes of your interest interact in the defined cell type pairs.
The exampled plot also indicates the data are from two environments. For instructions on setting up the Micro environment file for your spatial study. CellPhoneDB analysis classifies signaling pathways for genes of interest. These classifications are then used to annotate the heatmap within the task report.
Why are the values of clusterA-clusterB different to the values of clusterB-clusterA?
It is important to note that the interactions are not symmetric. The authors state that, "Partner A expression is considered for the first cluster/cell type (clusterA), and partner B expression is considered on the second cluster/cell type (clusterB). Thus, IL12-IL12 receptor for clusterA-clusterB (i.e. the receptor is in clusterB) is not the same as IL-12-IL-12 receptor for clusterB-clusterA (i.e. the receptor is in clusterA), and will have different values." [3][4]
Where do the interactions come from?
The interactions come from the CellphoneDB database. It is manually curated repository using reviewed molecular interactions with demonstrated evidence for a role in cellular communication. [5]
References
Troule, etc (2023). CellPhoneDB v5: Inferring cell-cell communication from single cell multiomics data. https://arxiv.org/pdf/2311.04567.pdf
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