Combining Datasets for an Interactome Analysis with CellChat

Question

[Draka]

Hi everyone,

I’m new to bioinformatics and currently working on a project to build an interactome between immune and neuronal cells. My datasets come from two different sources:

• The immune cell data is from a publication that provides a normalized count table and a metadata file.
• The neuronal cell data is from another publication that provides a pre-processed Seurat object.

I was able to successfully run CellChat on each dataset individually and obtained results. However, my goal is to study interactions between these two major cell types. To achieve this, I attempted to combine the datasets in CellChat, but I encountered the following error when running the computeCommunProb function on the combined CellChat object:

> cellChat_combined <- computeCommunProb(cellChat_combined)
triMean is used for calculating the average gene expression per cell group. 
Error in computeCommunProb(cellChat_combined) : 
  Please check `unique(object@idents)` and ensure that the factor levels are correct!
         You may need to drop unused levels using 'droplevels' function. e.g.,
         `meta$labels = droplevels(meta$labels, exclude = setdiff(levels(meta$labels),unique(meta$labels)))`

My questions:

• Is it possible to combine such different datasets to study interactions between two major cell types (immune and neuronal) using CellChat?
  • How can I resolve this error related to factor levels in the CellChat identifiers? Do you have any concrete examples or tips for solving this issue?
  • The two datasets do not share the same cell populations — can I still identify interactions between these cells?
  • The datasets are in different formats; is it possible to compare them despite these differences?
  • Is CellChat the right tool for this type of inter-dataset analysis, or should I consider another tool like CellPhoneDB or similar alternatives?

I’m also open to any other feedback or advice from anyone who has worked on similar projects.

Thank you very much for your help, and I wish you all a Happy New Year!