LIANA Analysis

1. load libraries

2. load seurat object

#Load Seurat Object
load("/home/nabbasi/isilon/To_Transfer_between_computers/23-Harmony_Integration/0-robj/5-Harmony_Integrated_All_samples_Merged_CD4Tcells_final_Resolution_Selected_0.8_ADT_Normalized_cleaned_mt.robj")


DefaultAssay(All_sample_Merged) <- "SCT"

Erreur dans DefaultAssay(All_sample_Merged) <- "SCT" : 
  objet 'All_sample_Merged' introuvable

3. Liana analysis using scPubr

L3_only <- subset(All_samples_Merged, subset = sample == "L3")

Error in `FetchData()`:
! None of the requested variables were found: 
Run `]8;;x-r-run:rlang::last_trace()rlang::last_trace()]8;;` to see where the error occurred.

Ligand Receptor analysis plot

library(SCpubr)

# Ligand Receptor analysis plot.
p <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output)
p


for (name in names(liana_output)) {
    # Save each component to a CSV file
    write.csv(liana_output[[name]], paste0("liana_L3_", name, "_L3_results.csv"), row.names = FALSE)
}

Ligand Receptor analysis plot aggregate

library(tidyverse)
library(magrittr)
library(liana)


show_resources()

 [1] "Default"          "Consensus"        "Baccin2019"       "CellCall"         "CellChatDB"      
 [6] "Cellinker"        "CellPhoneDB"      "CellTalkDB"       "connectomeDB2020" "EMBRACE"         
[11] "Guide2Pharma"     "HPMR"             "ICELLNET"         "iTALK"            "Kirouac2010"     
[16] "LRdb"             "Ramilowski2015"   "OmniPath"         "MouseConsensus"  

# Resource currently included in OmniPathR (and hence `liana`) include:
show_methods()

 [1] "connectome"      "logfc"           "natmi"           "sca"             "cellphonedb"    
 [6] "cytotalk"        "call_squidpy"    "call_cellchat"   "call_connectome" "call_sca"       
[11] "call_italk"      "call_natmi"     

liana_output %>% dplyr::glimpse()

Rows: 18,011
Columns: 16
$ source                 <chr> "11", "11", "1", "1", "11", "1", "1", "12", "11", "7", "8", "12", "13", "9", "…
$ target                 <chr> "5", "8", "1", "1", "11", "6", "1", "1", "12", "12", "12", "12", "12", "12", "…
$ ligand.complex         <chr> "GZMB", "GZMB", "VWF", "ICAM4", "GZMB", "HDC", "SORBS1", "CXCL8", "LGALS1", "L…
$ receptor.complex       <chr> "IGF2R", "IGF2R", "ITGA9", "ITGA2B", "IGF2R", "HRH2", "INSR", "CD79A", "CD69",…
$ aggregate_rank         <dbl> 2.542844e-07, 3.980714e-07, 6.163933e-07, 6.163933e-07, 3.044809e-06, 3.446445…
$ mean_rank              <dbl> 1308.8, 1396.2, 3360.6, 3316.9, 1616.6, 2438.8, 3901.4, 1138.2, 500.6, 553.4, …
$ natmi.edge_specificity <dbl> 0.17942744, 0.16316430, 0.98887316, 0.98567044, 0.12868437, 0.17540981, 0.9187…
$ natmi.rank             <dbl> 23, 35, 1, 2, 71, 26, 3, 7, 299, 390, 417, 434, 439, 483, 501, 528, 651, 1139,…
$ connectome.weight_sc   <dbl> 4.5597953, 4.5030352, 7.1353034, 7.3180344, 4.3826964, 4.1193760, 5.4202919, 3…
$ connectome.rank        <dbl> 31, 36, 2, 1, 51, 69, 6, 102, 254, 267, 287, 300, 303, 342, 345, 355, 417, 495…
$ logfc.logfc_comb       <dbl> 1.5768442, 1.5504295, 0.6620321, 0.5330281, 1.4625134, 0.8680215, 0.3414363, 1…
$ logfc.rank             <dbl> 3, 4, 212, 423, 5, 74, 1311, 1, 2, 15, 22, 30, 33, 58, 64, 103, 324, 39, 45, 1…
$ sca.LRscore            <dbl> 0.8000240, 0.7923152, 0.5316317, 0.5467204, 0.7721058, 0.6836826, 0.4602975, 0…
$ sca.rank               <dbl> 5150.0, 5569.0, 15251.0, 14821.5, 6619.0, 10688.0, 16850.0, 4244.0, 611.0, 758…
$ cellphonedb.pvalue     <dbl> 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0…
$ cellphonedb.rank       <dbl> 1337.0, 1337.0, 1337.0, 1337.0, 1337.0, 1337.0, 1337.0, 1337.0, 1337.0, 1337.0…

# liana_output <- liana_output %>%
#   liana_aggregate()
#> Now aggregating natmi
#> Now aggregating connectome
#> Now aggregating logfc
#> Now aggregating sca
#> Now aggregating cellphonedb
#> Aggregating Ranks

dplyr::glimpse(liana_output)

Rows: 18,011
Columns: 16
$ source                 <chr> "11", "11", "1", "1", "11", "1", "1", "12", "11", "7", "8", "12", "13", "9", "…
$ target                 <chr> "5", "8", "1", "1", "11", "6", "1", "1", "12", "12", "12", "12", "12", "12", "…
$ ligand.complex         <chr> "GZMB", "GZMB", "VWF", "ICAM4", "GZMB", "HDC", "SORBS1", "CXCL8", "LGALS1", "L…
$ receptor.complex       <chr> "IGF2R", "IGF2R", "ITGA9", "ITGA2B", "IGF2R", "HRH2", "INSR", "CD79A", "CD69",…
$ aggregate_rank         <dbl> 2.542844e-07, 3.980714e-07, 6.163933e-07, 6.163933e-07, 3.044809e-06, 3.446445…
$ mean_rank              <dbl> 1308.8, 1396.2, 3360.6, 3316.9, 1616.6, 2438.8, 3901.4, 1138.2, 500.6, 553.4, …
$ natmi.edge_specificity <dbl> 0.17942744, 0.16316430, 0.98887316, 0.98567044, 0.12868437, 0.17540981, 0.9187…
$ natmi.rank             <dbl> 23, 35, 1, 2, 71, 26, 3, 7, 299, 390, 417, 434, 439, 483, 501, 528, 651, 1139,…
$ connectome.weight_sc   <dbl> 4.5597953, 4.5030352, 7.1353034, 7.3180344, 4.3826964, 4.1193760, 5.4202919, 3…
$ connectome.rank        <dbl> 31, 36, 2, 1, 51, 69, 6, 102, 254, 267, 287, 300, 303, 342, 345, 355, 417, 495…
$ logfc.logfc_comb       <dbl> 1.5768442, 1.5504295, 0.6620321, 0.5330281, 1.4625134, 0.8680215, 0.3414363, 1…
$ logfc.rank             <dbl> 3, 4, 212, 423, 5, 74, 1311, 1, 2, 15, 22, 30, 33, 58, 64, 103, 324, 39, 45, 1…
$ sca.LRscore            <dbl> 0.8000240, 0.7923152, 0.5316317, 0.5467204, 0.7721058, 0.6836826, 0.4602975, 0…
$ sca.rank               <dbl> 5150.0, 5569.0, 15251.0, 14821.5, 6619.0, 10688.0, 16850.0, 4244.0, 611.0, 758…
$ cellphonedb.pvalue     <dbl> 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0…
$ cellphonedb.rank       <dbl> 1337.0, 1337.0, 1337.0, 1337.0, 1337.0, 1337.0, 1337.0, 1337.0, 1337.0, 1337.0…

Ligand Receptor analysis plot aggregate

liana_output %>%
  liana_dotplot(source_groups = c("2"),
                target_groups = c("6"),
                ntop = 20)

liana_output %>%
  liana_dotplot(source_groups = c("6"),
                target_groups = c("2"),
                ntop = 20)

liana_output %>%
  liana_dotplot(source_groups = c("2"),
                target_groups = c("8"),
                ntop = 20)

liana_output %>%
  liana_dotplot(source_groups = c("8"),
                target_groups = c("2"),
                ntop = 20)

liana_output %>%
  liana_dotplot(source_groups = c("6"),
                target_groups = c("8"),
                ntop = 20)

liana_output %>%
  liana_dotplot(source_groups = c("8"),
                target_groups = c("6"),
                ntop = 20)

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