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all_l-r_pairs_cellchat.R
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all_l-r_pairs_cellchat.R
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#generate 4 basic functions showing possible gene expression shifts
low <- function(x) {
0 + runif(1, 0, 0.7)
}
high <- function(x) {
2.3 + runif(1, 0, 0.7)
}
up <- function(x) {
exp(x/2)-1 + runif(1, -0.4, 0.4)
}
down <- function(x) {
1/x + runif(1, 0, 0.4)
}
library(ggplot2)
library(patchwork)
#manual matrix generation for 256 patterns
AA <- c('AA','Aa','aA','aa')
dim(AA) <- c(1,4)
BB <- c('BB','Bb','bB','bb')
dim(BB) <- c(4,1)
AABB <- cbind(rep(AA,each=nrow(BB)),rep(BB,nrow(AA)))
write.csv(AABB, 'C://Users/Emil/10X/matric.csv')
Excel =B2&""&B3
CC <- c('CC','Cc','cC','cc')
dim(CC) <- c(1,4)
DD <- c('DD','Dd','dD','dd')
dim(DD) <- c(4,1)
CCDD <- rbind(rep(CC,each=nrow(DD)),rep(DD,nrow(CC)))
write.csv(CCDD, 'C://Users/Emil/10X/matric1.csv')
Excel =B2&""&B3
AABB <- read.table(file = "clipboard", sep = "\t", header=FALSE)
dim(AABB) <- c(1,16)
AABB <- as.matrix(AABB)
CCDD <- read.table(file = "clipboard", sep = "\t", header=FALSE)
CCDD <- as.matrix(CCDD)
dim(CCDD) <- c(16,1)
abcd <- rbind(rep(AABB,each=nrow(CCDD)),rep(CCDD,nrow(AABB)))
write.csv(abcd, 'C://Users/Emil/10X/matricall.csv')
Excel =B2&""&B3
matabcd <- read.table(file = "clipboard", sep = "\t", header=FALSE)
#this part should be performed if you have already prepared the matrix. If no, check below
#After the last function is done, to visualize the results, subset the barcodes
barcodesall1 <- int5$barcode #subset
my_tab <- table(barcodesall1) #now you have barcode and its amount
tab_numbers <- as.numeric(my_tab) #subset amounts
tab_names <- names(my_tab) #subset names
#run function to visualize your patterns. This could be also performed for all 256 patterns if the input matrix is the one generated above
tabfun <- function(matgenerated, matnumbers) {
for(i in 1:length(matgenerated)) {
tmp <- matgenerated[i]
number <- matnumbers[i]
tmp <- unlist(strsplit(as.character(tmp), ""))
ifelse(tmp[1] == 'A',ifelse(tmp[2] == 'A',function1 <- high, function1 <- down),
ifelse(tmp[2] == 'A',function1 <- up, function1 <- low))
ifelse(tmp[3] == 'D',ifelse(tmp[4] == 'D',function2 <- high, function2 <- down),
ifelse(tmp[4] == 'D',function2 <- up, function2 <- low))
ifelse(tmp[5] == 'B',ifelse(tmp[6] == 'B',function3 <- high, function3 <- down),
ifelse(tmp[6] == 'B',function3 <- up, function3 <- low))
ifelse(tmp[7] == 'C',ifelse(tmp[8] == 'C',function4 <- high, function4 <- down),
ifelse(tmp[8] == 'C',function4 <- up, function4 <- low))
p <- ggplot(data = data.frame(x = c(0,pi)), mapping = aes(x = x)) + ggtitle(number)
p <- p +
stat_function(fun=function1,aes(colour="RGC Receptor"), size=1.5) +
stat_function(fun=function2,aes(colour="Retina Ligand"), size=1.5) +
stat_function(fun=function3,aes(colour="Retina Receptor"), size=1.5) +
stat_function(fun=function4, aes(colour='RGC Ligand'), size=1.5) +
ylim(0,3) + theme_bw()
ifelse(i == 1, ggsum <- p +xlab('Age') + ylab('Expression') +
theme(axis.text.y=element_blank(),
axis.text.x=element_blank()), ggsum <- ggsum + p +xlab('Age') + ylab('Expression') +
theme(axis.text.y=element_blank(),
axis.text.x=element_blank()) + plot_layout(guides = "collect") & theme(legend.position = "bottom")) }
print(ggsum)}
tabfun(tab_names, tab_numbers) #run the function
#load CellChat
library(CellChat)
CellChatDB <- CellChatDB.human #download human/mouse L-R database from CellChat
interaction_input <- CellChatDB$interaction
library(dplyr)
library(tidyr)
#Here we work on multiple L - one R, one L - multiple R combinations to build unique pairs
int1 <- interaction_input %>% separate(interaction_name, c('Ligand','R1','R2'))
int2 <- int1[c(1:3)]
int2 <- head(int2, - 4)
lrpairs <- function(tablet) {
for(i in 1:length(tablet$Ligand)) {
if(is.na(tablet$R2[i]) == FALSE) {
tablet <- rbind(tablet, data.frame(Ligand = tablet$Ligand[i], R1 = tablet$R2[i], R2 = tablet$R2[i])) } }
return(tablet) }
int2 <- lrpairs(int2)
df_added <- data.frame(Ligand = c('ITGA4','ITGA9','ITGB1','ITGB7','VSIR'), R1 = c('VCAM1','VCAM1','VCAM1','VCAM1','IGSF11')) #manual addition of leftovers
int3 <- int2[c(1:2)]
int3 <- rbind(int3, df_added)
human <- merge(FD59, y = c(FD82, FD125, adult))
humanRGC <- subset(human, idents = c('RGC'))
human$labels <- [email protected]
DefaultAssay(humanRGC) <- 'RNA'
#start building matrix with expression amounts for Retina R/L, RGC R/L
p <- AverageExpression(humanRGC, features = int3$Ligand, assays = 'RNA', group.by = 'stage')
p <- as.data.frame(p)
int3 <- int3 %>% distinct(Ligand, R1, .keep_all = TRUE)
exprassign <- function(tabletexpr, tabletall) {
for(i in 1:length(tabletexpr$RNA.Adult)) {
for(j in 1:length(tabletall$Ligand)) {
if(tabletall$Ligand[j] == rownames(tabletexpr)[i]) {
tabletall[j,4] <- tabletexpr[i,1]
tabletall[j,5] <- tabletexpr[i,2]
tabletall[j,6] <- tabletexpr[i,3]
tabletall[j,7] <- tabletexpr[i,4]
} } }
return(tabletall) }
int4$LigandRGCAdult <- 0
int4$LigandRGCFD125 <- 0
int4$LigandRGCFD59 <- 0
int4$LigandRGCFD82 <- 0
int4 <- exprassign(p, int3)
p <- AverageExpression(humanRGC, features = int3$R1, assays = 'RNA', group.by = 'stage')
p <- as.data.frame(p)
int4$RecRGCAdult <- 0
int4$RecRGCFD125 <- 0
int4$RecRGCFD59 <- 0
int4$RecRGCFD82 <- 0
exprassign1 <- function(tabletexpr, tabletall, tabletfinal) {
for(i in 1:length(tabletexpr$RNA.Adult)) {
for(j in 1:length(tabletall$R1)) {
if(tabletall$R1[j] == rownames(tabletexpr)[i]) {
tabletfinal[j,8] <- tabletexpr[i,1]
tabletfinal[j,9] <- tabletexpr[i,2]
tabletfinal[j,10] <- tabletexpr[i,3]
tabletfinal[j,11] <- tabletexpr[i,4]
} } }
return(tabletfinal) }
int4 <- exprassign1(p, int3, int4)
p <- AverageExpression(human, features = int3$Ligand, assays = 'RNA', group.by = 'stage')
p <- as.data.frame(p)
int4$LigandAdult <- 0
int4$LigandFD125 <- 0
int4$LigandFD59 <- 0
int4$LigandFD82 <- 0
exprassign2 <- function(tabletexpr, tabletall, tabletfinal) {
for(i in 1:length(tabletexpr$RNA.Adult)) {
for(j in 1:length(tabletall$Ligand)) {
if(tabletall$Ligand[j] == rownames(tabletexpr)[i]) {
tabletfinal[j,12] <- tabletexpr[i,1]
tabletfinal[j,13] <- tabletexpr[i,2]
tabletfinal[j,14] <- tabletexpr[i,3]
tabletfinal[j,15] <- tabletexpr[i,4]
} } }
return(tabletfinal) }
int4 <- exprassign2(p, int3, int4)
p <- AverageExpression(human, features = int3$R1, assays = 'RNA', group.by = 'stage')
p <- as.data.frame(p)
int4$RecAdult <- 0
int4$RecFD125 <- 0
int4$RecFD59 <- 0
int4$RecFD82 <- 0
exprassign3 <- function(tabletexpr, tabletall, tabletfinal) {
for(i in 1:length(tabletexpr$RNA.Adult)) {
for(j in 1:length(tabletall$R1)) {
if(tabletall$R1[j] == rownames(tabletexpr)[i]) {
tabletfinal[j,16] <- tabletexpr[i,1]
tabletfinal[j,17] <- tabletexpr[i,2]
tabletfinal[j,18] <- tabletexpr[i,3]
tabletfinal[j,19] <- tabletexpr[i,4]
} } }
return(tabletfinal) }
int4 <- exprassign3(p, int3, int4)
#remove NAs
matrixlr[is.na(matrixlr)] <- 0
#function to generate the 8-lettered barcode for every pattern out of 256
tabfunfinal <- function(matgenerated) {
matgenerated$barcode <- 'NA'
for(i in 1:length(matgenerated$RecRGCFD59)) {
ifelse(matgenerated$RecRGCAdult[i] <= 0.01 & matgenerated$RecRGCFD125[i] <= 0.01 &
matgenerated$RecRGCFD82[i] <= 0.01 & matgenerated$RecRGCFD59[i] <= 0.01, matgenerated$barcode[i] <- 'aa',
ifelse(matgenerated$RecRGCAdult[i] > max(matgenerated$RecRGCFD125[i],matgenerated$RecRGCFD59[i],
matgenerated$RecRGCFD82[i]), matgenerated$barcode[i] <- 'aA',
ifelse(matgenerated$RecRGCAdult[i] < min(matgenerated$RecRGCFD125[i],matgenerated$RecRGCFD59[i],matgenerated$RecRGCFD82[i]),
matgenerated$barcode[i] <- 'Aa', matgenerated$barcode[i] <- 'AA')))
ifelse(matgenerated$LigandRGCAdult[i] <= 0.01 & matgenerated$LigandRGCFD125[i] <= 0.01 &
matgenerated$LigandRGCFD82[i] <= 0.01 & matgenerated$LigandRGCFD59[i] <= 0.01,
matgenerated$barcode[i] <- paste(matgenerated$barcode[i], 'dd', sep = ''),
ifelse(matgenerated$LigandRGCAdult[i] > max(matgenerated$LigandRGCFD125[i],
matgenerated$LigandRGCFD59[i],matgenerated$LigandRGCFD82[i]),
matgenerated$barcode[i] <- paste(matgenerated$barcode[i],'dD',sep=''),
ifelse(matgenerated$LigandRGCAdult[i] < min(matgenerated$LigandRGCFD125[i],matgenerated$LigandRGCFD59[i],
matgenerated$LigandRGCFD82[i]),
matgenerated$barcode[i] <- paste(matgenerated$barcode[i],'Dd',sep=''),
matgenerated$barcode[i] <- paste(matgenerated$barcode[i],'DD',sep=''))))
ifelse(matgenerated$LigandAdult[i] <= 0.01 & matgenerated$LigandFD125[i] <= 0.01 &
matgenerated$LigandFD82[i] <= 0.01 & matgenerated$LigandFD59[i] <= 0.01,
matgenerated$barcode[i] <- paste(matgenerated$barcode[i], 'bb', sep = ''),
ifelse(matgenerated$LigandAdult[i] > max(matgenerated$LigandFD125[i],
matgenerated$LigandFD59[i],matgenerated$LigandFD82[i]),
matgenerated$barcode[i] <- paste(matgenerated$barcode[i],'bB',sep=''),
ifelse(matgenerated$LigandAdult[i] < min(matgenerated$LigandFD125[i],matgenerated$LigandFD59[i],
matgenerated$LigandFD82[i]),
matgenerated$barcode[i] <- paste(matgenerated$barcode[i],'Bb',sep=''),
matgenerated$barcode[i] <- paste(matgenerated$barcode[i],'BB',sep=''))))
ifelse(matgenerated$RecAdult[i] <= 0.01 & matgenerated$RecFD125[i] <= 0.01 &
matgenerated$RecFD82[i] <= 0.01 & matgenerated$RecFD59[i] <= 0.01,
matgenerated$barcode[i] <- paste(matgenerated$barcode[i], 'cc', sep = ''),
ifelse(matgenerated$RecAdult[i] > max(matgenerated$RecFD125[i],
matgenerated$RecFD59[i],matgenerated$RecFD82[i]),
matgenerated$barcode[i] <- paste(matgenerated$barcode[i],'cC',sep=''),
ifelse(matgenerated$RecAdult[i] < min(matgenerated$RecFD125[i],
matgenerated$RecFD59[i],matgenerated$RecFD82[i]),
matgenerated$barcode[i] <- paste(matgenerated$barcode[i],'Cc',sep=''),
matgenerated$barcode[i] <- paste(matgenerated$barcode[i],'CC',sep='')))) }
return(matgenerated)}