general question

[Flu09]

does it make sense to make an object of sorted samples that should contain a specific cell type and another object from a different study that contain a different cell type and do cell-cell communication?!

Another question if does the data need to be integrated then subsetted before such analysis to have the same UMAP reduction?

[sqjin]

@Flu09 You can do it in this way. When you interpret the results, you'd better to check if the data have strong batch effects.

No need to run umap. You should normalize the data together and take the normalized data as input of cellchat

[xiongfx-spec]

@Flu09 You can do it in this way. When you interpret the results, you'd better to check if the data have strong batch effects.

No need to run umap. You should normalize the data together and take the normalized data as input of cellchat

Hi,

Thanks for such a great package. I have a similar question, but my data involves multiple groups.

I have two batches of single-cell transcriptome sequencing data. The first batch includes hematopoietic stem cells from the bone marrow, with four groups labeled as a, b, c, and d. The second batch includes bone marrow stromal cells, with four groups labeled as A, B, C, and D. The treatment conditions correspond as follows: a corresponds to A, b corresponds to B, c corresponds to C, and d corresponds to D. I want to analyze the interaction between hematopoietic stem cells and bone marrow stromal cells under these four conditions.

Can I use the merge() function to combine a with A, b with B, c with C, and d with D separately, creating four datasets Aa, Bb, Cc, and Dd?

Afterward, can I run CellChat on each of these four datasets individually, and then analyze the data following the tutorial(Comparison analysis of multiple datasets using CellChat)? Is this operation reasonable？

How can I determine if there is a batch effect between stromal cells and hematopoietic stem cells?

Any help would be greatly appreciated! Thanks!