In yuliangwang/talklr: Uncover Perturbed Ligand-Receptor Pairs

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(talklr)
library(dplyr)

Single condition mode

The code below will read in a tab-separated expression table (first column is gene symbol,and remaining columns are mean expression values for each cell type). The expression values must be normalized for sequencing depth, but no need to normalize for gene length. The data MUST NOT be log2 transformed.

library(talklr)
glom_normal<-read.table(system.file("extdata", "glom_normal_data.txt", package = "talklr"),header=T,sep="\t",stringsAsFactors =F)

lr_glom_normal<-make_expressed_net(glom_normal,4,receptor_ligand,'product',1)
lr_glom_normal<-arrange(lr_glom_normal,desc(KL))
head(lr_glom_normal)

We can then visualize the ligand-receptor wiring diagram for any of the top ligand-receptor pairs identified by talklr.

plot_lr_wiring(as.numeric(lr_glom_normal[1,17:19]),as.numeric(lr_glom_normal[1,20:22]),c("podo","mesa","endo"),1)

plot_lr_wiring(as.numeric(lr_glom_normal[2,17:19]),as.numeric(lr_glom_normal[2,20:22]),c("podo","mesa","endo"),1)

Optional: comparison with differentially expressed gene-based approach

Typically, studies restrict ligand-receptor pairs to genes expressed 3 fold higher in one cell type compared to each of the other cell types, in other words, cell type marker genes.
In normal glomerulus, there are 198 such ligand-receptor pairs.

lr_deg_glom_normal<-make_deg_net(glom_normal,lr_glom_normal,3,1)
nrow(lr_deg_glom_normal)
head(lr_deg_glom_normal)

Two condition mode

In the two condition mode, we will have one expression table for each condition. We will first identify a set of genes expressed in at least one cell type in either condition, and restrict ligand-receptor pairs to those expressed genes.

glom_normal<-read.table(system.file("extdata", "glom_normal_data.txt", package = "talklr"),header=T,sep="\t",stringsAsFactors =F)
glom_fsgs<-read.table(system.file("extdata", "glom_fsgs_data.txt", package = "talklr"),header=T,sep="\t",stringsAsFactors =F)
expressed_normal<-rowSums(glom_normal[,2:ncol(glom_normal)]>4)>=1
expressed_fsgs<-rowSums(glom_fsgs[,2:ncol(glom_fsgs)]>4)>=1
expressed_genes<-glom_normal$genes[expressed_normal|expressed_fsgs]

We will then identify interesting ligand receptor pairs for each condition, and then identify ligand-receptor pairs that changed between two conditions.

normal_net<-make_expressed_net_specify_expressed_genes(glom_normal,expressed_genes,receptor_ligand,'product')
fsgs_net<-make_expressed_net_specify_expressed_genes(glom_fsgs,expressed_genes,receptor_ligand,'product')
normal_net$fsgs_vs_normal_KL<-disease_vs_normal_KL(fsgs_net[,17:19],fsgs_net[,20:22],normal_net[,17:19],normal_net[,20:22],1,'produt')
perturbed_net<-arrange(normal_net,desc(fsgs_vs_normal_KL))
head(perturbed_net)

Finally, we can then visualize rewiring of ligand-receptor pairs between two conditions.

pair2plot<-'CCL2_ACKR2' 
par(mfrow=c(1,2))
par(mar = c(0.3, 0.3, 0.3, 0.3))
plot_lr_wiring(as.numeric(normal_net[normal_net$Pair.Name==pair2plot,17:19]),as.numeric(normal_net[normal_net$Pair.Name==pair2plot,20:22]),c("podo","mesa","endo"),1)

plot_lr_wiring(as.numeric(fsgs_net[fsgs_net$Pair.Name==pair2plot,17:19]),as.numeric(fsgs_net[fsgs_net$Pair.Name==pair2plot,20:22]),c("podo","mesa","endo"),1)