How to obtain output from import_intercell_networks and and nichenet_networks for mouse?

[lgeistlinger]

Hi thanks for a great package! I understand that the functions import_intercell_networks and nichenet_networks return network knowledge for human that can be provided as input for cell-cell interaction inference tools such as CellPhoneDB and NicheNet, respectively.

I was wondering whether you can suggest an approach to obtain such networks also for mouse? I understand that this will likely involve some sort of orthology transfer, and I was just wondering whether you have something easy and convenient as eg available for nichenetr? That would be very helpful. Thanks!

[deeenes]

Hi,

With OmnipathR you can do easy orthology translation based on NCBI HomoloGene data. For UniProt IDs use this function, for Gene Symbols or Entrez Gene IDs this one.

library(OmnipathR)

human_mouse_uniprot <- homologene_uniprot_orthology(mouse, human)
human_mouse_uniprot                                                                                                                                                                 
# A tibble: 15,035 × 2
   source target
   <chr>  <chr> 
 1 P11310 P45952
 2 P49748 P50544
 3 P24752 Q8QZT1
 4 Q04771 P37172
 5 Q16586 P82350
 6 P30566 P54822
 7 P20933 Q64191
 8 P13716 P10518
 9 P22557 P08680
10 P23109 Q3V1D3
# … with 15,025 more rows
# ℹ Use `print(n = ...)` to see more rows

human_mouse_genesymbol <- homologene_download(mouse, human)
human_mouse_genesymbol                                                                                                                                                              
# A tibble: 17,355 × 3
   hgroup genesymbol_source genesymbol_target
    <int> <chr>             <chr>            
 1      3 ACADM             Acadm            
 2      5 ACADVL            Acadvl           
 3      6 ACAT1             Acat1            
 4      7 ACVR1             Acvr1            
 5      9 SGCA              Sgca             
 6     12 ADSL              Adsl             
 7     13 AGA               Aga              
 8     14 AGT               Agt              
 9     16 ALAD              Alad             
10     17 ALAS2             Alas2            
# … with 17,345 more rows
# ℹ Use `print(n = ...)` to see more rows

With these tables you can do two inner_joins to translate a network data frame. A convenient single call function will be soon available, we are only waiting for some developments in our Ensembl based orthology translation methods.

[lgeistlinger]

Great, thanks, that is very helpful!