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Figure3A-F.R
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Figure3A-F.R
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#Created by Giuseppe Rubens Pascucci (pascucci.1479790@studenti.uniroma1.it)
####################################################################
library(ggplot2)
library(ggsignif)
library(RColorBrewer)
library(ggbeeswarm)
mycols <- c(Healthy = '#3780b2', 'CoV2+' = '#d7802f', 'MIS-C' = '#9da290', Kawasaki = '#bd377f')
# ================================================== PROTEOMICS DATABASE ==================================================
prot <- read.table("DataIN/Olink_KD.csv", sep = ",", header = T, check.names=F, stringsAsFactors = F)
KT0 <- prot[grepl("T0", prot$ID2, fixed = TRUE),]
COVID <- prot[grepl("CACTUS", prot$ID2, fixed = TRUE),]
prot <- rbind(KT0[,-1], COVID[,-1])
prot$ID2 <- gsub(" ","", prot$ID2)
names(prot)[1] <- "ID"
rm(KT0, COVID)
# ================================================== FREQUENCY DATABASE ==================================================
freq <- read.table("DataIN/Dataset_CleanAll.txt", sep = "\t", header = T, check.names=F, stringsAsFactors = F)
rownames(freq) <- freq[,1]
Group <- freq[,2]
Sex <- freq[,3]
Age <- freq[,4]
freq <- freq[,-c(1:4)]
tfreq <- t(freq)
# ================================================== DATABASE MERGE ==================================================
data <- merge(cbind(ID = rownames(freq), Group, Age, Sex, freq), prot, by = "ID")
irR <- which(!complete.cases(data))
data <- data[-irR,]
write.table(data, "DataIN/Dataset_FACS+Prot.txt", sep="\t", row.names = F, col.names = T, quote = F)
table(data$Group)
data <- as.matrix(data)
rownames(data) <- data[,1]
Group3 <- data[,2]
Age3 <- data[,3]
Sex3 <- data[,4]
data <- data[,-c(1:4)]
class(data) <- "numeric"
prot <- data[, -c(1:ncol(freq))]
rm(irR)
# ================================================== D'AGOSTINO-PEARSON FUNCTION ==================================================
dagostino.pearson.test <- function(x) {
# from Zar (1999), implemented by Doug Scofield, scofield at bio.indiana.edu
DNAME <- deparse(substitute(x))
n <- length(x)
x2 <- x * x
x3 <- x * x2
x4 <- x * x3
# compute Z_g1
k3 <- ((n*sum(x3)) - (3*sum(x)*sum(x2)) + (2*(sum(x)^3)/n)) /((n-1)*(n-2))
g1 <- k3 / sqrt(var(x)^3)
sqrtb1 <- ((n - 2)*g1) / sqrt(n*(n - 1))
A <- sqrtb1 * sqrt(((n + 1)*(n + 3)) / (6*(n - 2)))
B <- (3*(n*n + 27*n - 70)*(n+1)*(n+3)) / ((n-2)*(n+5)*(n+7)*(n+9))
C <- sqrt(2*(B - 1)) - 1
D <- sqrt(C)
E <- 1 / sqrt(log(D))
F <- A / sqrt(2/(C - 1))
Zg1 <- E * log(F + sqrt(F*F + 1))
# compute Z_g2
G <- (24*n*(n-2)*(n-3)) / (((n+1)^2)*(n+3)*(n+5))
k4 <- (((n*n*n + n*n)*sum(x4)) - (4*(n*n + n)*sum(x3)*sum(x)) - (3*(n*n - n)*sum(x2)^2) + (12*n*sum(x2)*sum(x)^2) - (6*sum(x)^4)) /(n*(n-1)*(n-2)*(n-3))
g2 <- k4 / var(x)^2
H <- ((n-2)*(n-3)*abs(g2)) / ((n+1)*(n-1)*sqrt(G))
J <- ((6*(n*n - 5*n + 2)) / ((n+7)*(n+9))) * sqrt((6*(n+3)*(n+5)) /(n*(n-2)*(n-3)))
K <- 6 + (8/J)*(2/J + sqrt(1 + 4/(J*J)))
L <- (1 - 2/K) / (1 + H*sqrt(2/(K-4)))
Zg2 <- (1 - 2/(9*K) - (L^(1/3))) / (sqrt(2/(9*K)))
K2 <- Zg1*Zg1 + Zg2*Zg2
pk2 <- pchisq(K2, 2, lower.tail=FALSE)
RVAL <- list(statistic = c(K2 = K2), p.value = pk2, method = "D'Agostino-Pearson normality test\n\nK2 is distributed as Chi-squared with df=2", alternative = "distribution is not normal", data.name = DNAME)
class(RVAL) <- "htest"
return(RVAL)
}
# =========================================================== Differential Analysis ===========================================================
Group1 <- c("Kawasaki", "MIS-C", "MIS-C","MIS-C", "CoV2+","CoV2+", "CoV2+All", "CoV2+All")
Group2 <- c("Healthy", "Healthy", "CoV2+","Kawasaki", "Healthy", "Kawasaki", "Healthy", "Kawasaki")
t <- data.frame()
for(j in 1:length(Group1))
{
if(Group1[j] %in% "CoV2+All"){
x <- tfreq[, Group %in% "CoV2+" | Group %in% "MIS-C"]
} else{
x <- tfreq[, Group %in% Group1[j]]
}
y <- tfreq[, Group %in% Group2[j]]
a <- ncol(x)
b <- ncol(y)
m <- cbind(x, y)
# Normality Test
norX <- apply(m[, 1:a], 1, function(z){dagostino.pearson.test(z)$p.value})
norY <- apply(m[, (a+1):(a + b)], 1, function(z){dagostino.pearson.test(z)$p.value})
norX[is.na(norX)] <- 0
norY[is.na(norY)] <- 0
# Differential Test
m <- cbind(m, norX, norY)
pv <- apply(m, 1, function(z){
if(z[a + b + 1] > 0.05 && z[a + b + 2] > 0.05){
t.test(z[1:a], z[(a + 1):(a + b)], paired = F, na.action = "na.omit")$p.value
} else { wilcox.test(z[1:a], z[(a + 1):(a + b)], paired = F, exact = F, na.action = "na.omit")$p.value }})
adj <- p.adjust(pv, method="fdr")
# Fold Change
fc <- rowMeans(m[,1:a], na.rm = T) / rowMeans(m[,(a + 1):(a + b)], na.rm = T)
# Final Table
m <- cbind(Label = rownames(m), Group1 = Group1[j], Group2 = Group2[j], Comparison = paste0(Group1[j], " vs ", Group2[j]), pValue = pv, pAdj = adj, log2FoldChange = round(log2(fc + 0.01), 2))
t <- rbind(t, m)
}
rm(x, y, a, b, m, j, norX, norY, pv, adj, fc)
write.table(t[,-c(2,3)], "DataOUT/TableDiff All.txt", sep="\t", row.names = F, col.names = T, quote = F)
t$pValue <- as.numeric(as.character(t$pValue))
t <- t[t$pValue < 0.05,]
write.table(t[,-c(2,3)], "DataOUT/TableDiff DE.txt", sep="\t", row.names = F, col.names = T, quote = F)
# =========================================================== Violin Plots ===========================================================
Group4 <- Group
Group <- factor(Group, levels = c("Healthy", "CoV2+","MIS-C","Kawasaki"))
t2 <- t[t$Group1 %in% "CoV2+" | t$Group2 %in% "CoV2+" | t$Group1 %in% "MIS-C" | t$Group2 %in% "MIS-C",]
lab <- as.character(unique(t2$Label))
for(i in 1:length(lab)){
m <- t2[t2$Label %in% lab[i],]
viol <- as.data.frame(cbind(freq[, colnames(freq) %in% lab[i]], Group))
colnames(viol) <- c("V1", "V2")
p <- ggplot(viol, aes(x=Group, y=V1, col = Group , fill = Group)) + ggtitle(lab[i]) +
scale_fill_manual(values=mycols) + scale_color_manual(values=mycols) +
geom_violin(key_glyph = "point", position="dodge") +
geom_jitter(shape=16, position=position_jitter(0.2), size=1.5, colour="black") +
theme_minimal(base_size = 23) +
ylab("%") +
theme(legend.position = "none",
axis.title.x=element_blank(),
axis.text.x = element_text(colour = mycols, face = "bold"),
plot.title = element_text(size=21, family="sans", face = "bold"),
axis.text = element_text(colour = "black", family = "sans", size = 23))
if(nrow(m) > 0) for(x in 1:nrow(m)) p <- p + geom_signif(comparisons = list(c(as.character(m[x, colnames(m) %in% "Group1"]), as.character(m[x, colnames(m) %in% "Group2"]))),
annotation = paste0 ("p = ", format(as.numeric(as.character(m[x, colnames(m) %in% "pValue"])), scientific = T, digits = 2)), tip_length = 0, size = 0.9, col = "black",
margin_top = x*0.07, textsize = 5.5, vjust = - 0.03)
p
ggsave(paste0("DataOUT/Plots/Violin - ", lab[i],".pdf"), width = 8, height = 6)
}