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Fig1_single_AB.R
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Fig1_single_AB.R
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library(binom)
AB_data <- read.csv("C:\\U\\CoronaVirus\\SeroSig_Phase2\\DATA\\SARS-CoV-2_serology.csv")
AB_data <- AB_data[-which(AB_data$days_post <= 10),]
N_positive <- length(which(AB_data$status == "positive"))
N_negative <- length(which(AB_data$status == "negative"))
####################################
####################################
## ##
## ONE AT A TIME ##
## ##
####################################
####################################
N_ant <- 13
N_SS <- 50000
dil_cut <- exp(seq(from=log(1e-5), to=log(0.3), length=N_SS))
MFI_cut <- exp(seq(from=log(1), to=log(100000), length=N_SS))
sens_mat <- matrix(NA, ncol=2*N_ant, nrow=N_SS)
spec_mat <- matrix(NA, ncol=2*N_ant, nrow=N_SS)
colnames(sens_mat) <- c( "Stri_IPP_IgG_dil","S1RBD_NA_IgG_dil", "RBD_IPP_IgG_dil", "S1_NA_IgG_dil", "S2_NA_IgG_dil",
"NP_IPP_IgG_dil", "NP_NA_IgG_dil", "X229E_NP_NA_IgG_MFI", "NL63_NP_NA_IgG_MFI", "fluA_NA_IgG_MFI",
"ade40_NA_IgG_MFI", "rub_NA_IgG_MFI", "mumps_NA_IgG_MFI",
"Stri_IPP_IgM_dil", "S1RBD_NA_IgM_dil", "RBD_IPP_IgM_dil", "S1_NA_IgM_dil", "S2_NA_IgM_dil",
"NP_IPP_IgM_dil", "NP_NA_IgM_dil", "X229E_NP_NA_IgM_MFI", "NL63_NP_NA_IgM_MFI", "fluA_NA_IgM_MFI",
"ade40_NA_IgM_MFI", "rub_NA_IgM_MFI", "mumps_NA_IgM_MFI" )
colnames(spec_mat) <- c( "Stri_IPP_IgG_dil", "S1RBD_NA_IgG_dil", "RBD_IPP_IgG_dil", "S1_NA_IgG_dil", "S2_NA_IgG_dil",
"NP_IPP_IgG_dil", "NP_NA_IgG_dil", "X229E_NP_NA_IgG_MFI", "NL63_NP_NA_IgG_MFI", "fluA_NA_IgG_MFI",
"ade40_NA_IgG_MFI", "rub_NA_IgG_MFI", "mumps_NA_IgG_MFI",
"Stri_IPP_IgM_dil", "S1RBD_NA_IgM_dil", "RBD_IPP_IgM_dil", "S1_NA_IgM_dil", "S2_NA_IgM_dil",
"NP_IPP_IgM_dil", "NP_NA_IgM_dil", "X229E_NP_NA_IgM_MFI", "NL63_NP_NA_IgM_MFI", "fluA_NA_IgM_MFI",
"ade40_NA_IgM_MFI", "rub_NA_IgM_MFI", "mumps_NA_IgM_MFI" )
for(i in 1:N_SS)
{
sens_mat[i,1] <- length(which( AB_data$Stri_IPP_IgG_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$Stri_IPP_IgG_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,1] <- length(which( AB_data$Stri_IPP_IgG_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$Stri_IPP_IgG_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,2] <- length(which( AB_data$S1RBD_NA_IgG_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$S1RBD_NA_IgG_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,2] <- length(which( AB_data$S1RBD_NA_IgG_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$S1RBD_NA_IgG_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,3] <- length(which( AB_data$RBD_IPP_IgG_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$RBD_IPP_IgG_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,3] <- length(which( AB_data$RBD_IPP_IgG_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$RBD_IPP_IgG_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,4] <- length(which( AB_data$S1_NA_IgG_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$S1_NA_IgG_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,4] <- length(which( AB_data$S1_NA_IgG_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$S1_NA_IgG_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,5] <- length(which( AB_data$S2_NA_IgG_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$S2_NA_IgG_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,5] <- length(which( AB_data$S2_NA_IgG_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$S2_NA_IgG_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,6] <- length(which( AB_data$NP_IPP_IgG_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$NP_IPP_IgG_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,6] <- length(which( AB_data$NP_IPP_IgG_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$NP_IPP_IgG_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,7] <- length(which( AB_data$NP_NA_IgG_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$NP_NA_IgG_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,7] <- length(which( AB_data$NP_NA_IgG_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$NP_NA_IgG_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,8] <- length(which( AB_data$X229E_NP_NA_IgG_MFI[which(AB_data$status == "positive")] > MFI_cut[i] ))/length(which(is.na(AB_data$X229E_NP_NA_IgG_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,8] <- length(which( AB_data$X229E_NP_NA_IgG_MFI[which(AB_data$status == "negative")] < MFI_cut[i] ))/length(which(is.na(AB_data$X229E_NP_NA_IgG_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,9] <- length(which( AB_data$NL63_NP_NA_IgG_MFI[which(AB_data$status == "positive")] > MFI_cut[i] ))/length(which(is.na(AB_data$NL63_NP_NA_IgG_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,9] <- length(which( AB_data$NL63_NP_NA_IgG_MFI[which(AB_data$status == "negative")] < MFI_cut[i] ))/length(which(is.na(AB_data$NL63_NP_NA_IgG_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,10] <- length(which( AB_data$fluA_NA_IgG_MFI[which(AB_data$status == "positive")] > MFI_cut[i] ))/length(which(is.na(AB_data$fluA_NA_IgG_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,10] <- length(which( AB_data$fluA_NA_IgG_MFI[which(AB_data$status == "negative")] < MFI_cut[i] ))/length(which(is.na(AB_data$fluA_NA_IgG_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,11] <- length(which( AB_data$ade40_NA_IgG_MFI[which(AB_data$status == "positive")] > MFI_cut[i] ))/length(which(is.na(AB_data$ade40_NA_IgG_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,11] <- length(which( AB_data$ade40_NA_IgG_MFI[which(AB_data$status == "negative")] < MFI_cut[i] ))/length(which(is.na(AB_data$ade40_NA_IgG_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,12] <- length(which( AB_data$rub_NA_IgG_MFI[which(AB_data$status == "positive")] > MFI_cut[i] ))/length(which(is.na(AB_data$rub_NA_IgG_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,12] <- length(which( AB_data$rub_NA_IgG_MFI[which(AB_data$status == "negative")] < MFI_cut[i] ))/length(which(is.na(AB_data$rub_NA_IgG_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,13] <- length(which( AB_data$mumps_NA_IgG_MFI[which(AB_data$status == "positive")] > MFI_cut[i] ))/length(which(is.na(AB_data$mumps_NA_IgG_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,13] <- length(which( AB_data$mumps_NA_IgG_MFI[which(AB_data$status == "negative")] < MFI_cut[i] ))/length(which(is.na(AB_data$mumps_NA_IgG_dil[which(AB_data$status == "negative")])==FALSE))
}
for(i in 1:N_SS)
{
sens_mat[i,14] <- length(which( AB_data$Stri_IPP_IgM_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$Stri_IPP_IgM_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,14] <- length(which( AB_data$Stri_IPP_IgM_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$Stri_IPP_IgM_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,15] <- length(which( AB_data$S1RBD_NA_IgM_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$S1RBD_NA_IgM_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,15] <- length(which( AB_data$S1RBD_NA_IgM_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$S1RBD_NA_IgM_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,16] <- length(which( AB_data$RBD_IPP_IgM_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$RBD_IPP_IgM_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,16] <- length(which( AB_data$RBD_IPP_IgM_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$RBD_IPP_IgM_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,17] <- length(which( AB_data$S1_NA_IgM_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$S1_NA_IgM_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,17] <- length(which( AB_data$S1_NA_IgM_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$S1_NA_IgM_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,18] <- length(which( AB_data$S2_NA_IgM_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$S2_NA_IgM_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,18] <- length(which( AB_data$S2_NA_IgM_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$S2_NA_IgM_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,19] <- length(which( AB_data$NP_IPP_IgM_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$NP_IPP_IgM_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,19] <- length(which( AB_data$NP_IPP_IgM_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$NP_IPP_IgM_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,20] <- length(which( AB_data$NP_NA_IgM_dil[which(AB_data$status == "positive")] > dil_cut[i] ))/length(which(is.na(AB_data$NP_NA_IgM_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,20] <- length(which( AB_data$NP_NA_IgM_dil[which(AB_data$status == "negative")] < dil_cut[i] ))/length(which(is.na(AB_data$NP_NA_IgM_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,21] <- length(which( AB_data$X229E_NP_NA_IgM_MFI[which(AB_data$status == "positive")] > MFI_cut[i] ))/length(which(is.na(AB_data$X229E_NP_NA_IgM_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,21] <- length(which( AB_data$X229E_NP_NA_IgM_MFI[which(AB_data$status == "negative")] < MFI_cut[i] ))/length(which(is.na(AB_data$X229E_NP_NA_IgM_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,22] <- length(which( AB_data$NL63_NP_NA_IgM_MFI[which(AB_data$status == "positive")] > MFI_cut[i] ))/length(which(is.na(AB_data$NL63_NP_NA_IgM_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,22] <- length(which( AB_data$NL63_NP_NA_IgM_MFI[which(AB_data$status == "negative")] < MFI_cut[i] ))/length(which(is.na(AB_data$NL63_NP_NA_IgM_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,23] <- length(which( AB_data$fluA_NA_IgM_MFI[which(AB_data$status == "positive")] > MFI_cut[i] ))/length(which(is.na(AB_data$fluA_NA_IgM_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,23] <- length(which( AB_data$fluA_NA_IgM_MFI[which(AB_data$status == "negative")] < MFI_cut[i] ))/length(which(is.na(AB_data$fluA_NA_IgM_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,24] <- length(which( AB_data$ade40_NA_IgM_MFI[which(AB_data$status == "positive")] > MFI_cut[i] ))/length(which(is.na(AB_data$ade40_NA_IgM_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,24] <- length(which( AB_data$ade40_NA_IgM_MFI[which(AB_data$status == "negative")] < MFI_cut[i] ))/length(which(is.na(AB_data$ade40_NA_IgM_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,25] <- length(which( AB_data$rub_NA_IgM_MFI[which(AB_data$status == "positive")] > MFI_cut[i] ))/length(which(is.na(AB_data$rub_NA_IgM_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,25] <- length(which( AB_data$rub_NA_IgM_MFI[which(AB_data$status == "negative")] < MFI_cut[i] ))/length(which(is.na(AB_data$rub_NA_IgM_dil[which(AB_data$status == "negative")])==FALSE))
sens_mat[i,26] <- length(which( AB_data$mumps_NA_IgM_MFI[which(AB_data$status == "positive")] > MFI_cut[i] ))/length(which(is.na(AB_data$mumps_NA_IgM_dil[which(AB_data$status == "positive")])==FALSE))
spec_mat[i,26] <- length(which( AB_data$mumps_NA_IgM_MFI[which(AB_data$status == "negative")] < MFI_cut[i] ))/length(which(is.na(AB_data$mumps_NA_IgM_dil[which(AB_data$status == "negative")])==FALSE))
}
######################################
## Calculate Area Under Curve (AUC)
AUC_one_ant <- rep(NA, 2*N_ant)
for(j in 1:(2*N_ant))
{
AUC_one_ant[j] <- sum( (sens_mat[1:(N_SS-1),j] - sens_mat[2:N_SS,j])*
0.5*(spec_mat[1:(N_SS-1),j] + spec_mat[2:N_SS,j]) )
}
sens_target <- matrix(NA, nrow=3, ncol=2*N_ant)
colnames(sens_target) <- colnames(sens_mat)
rownames(sens_target) <- c("sens_99", "sens_spec", "spec_99")
spec_target <- matrix(NA, nrow=3, ncol=2*N_ant)
colnames(spec_target) <- colnames(spec_mat)
rownames(spec_target) <- c("sens_99", "sens_spec", "spec_99")
for(j in 1:(2*N_ant))
{
sens_target[1,j] <- sens_mat[max(which(sens_mat[,j] > 0.99)),j]
spec_target[1,j] <- spec_mat[max(which(sens_mat[,j] > 0.99)),j]
sens_target[2,j] <- sens_mat[which.min(abs(sens_mat[,j] - spec_mat[,j])),j]
spec_target[2,j] <- spec_mat[which.min(abs(sens_mat[,j] - spec_mat[,j])),j]
sens_target[3,j] <- sens_mat[min(which(spec_mat[,j] > 0.99)),j]
spec_target[3,j] <- spec_mat[min(which(spec_mat[,j] > 0.99)),j]
}
sens_target_lwr <- sens_target
for(i in 1:nrow(sens_target_lwr))
{
for(j in 1:ncol(sens_target_lwr))
{
sens_target_lwr[i,j] <- binom.confint( sens_target_lwr[i,j]*N_positive, N_positive, method="wilson")[1,5]
sens_target_lwr[i,j] <- round( 100*sens_target_lwr[i,j], 1)
}
}
sens_target_upr <- sens_target
for(i in 1:nrow(sens_target_upr))
{
for(j in 1:ncol(sens_target_upr))
{
sens_target_upr[i,j] <- binom.confint( sens_target_upr[i,j]*N_positive, N_positive, method="wilson")[1,6]
sens_target_upr[i,j] <- round( 100*sens_target_upr[i,j], 1)
}
}
spec_target_lwr <- spec_target
for(i in 1:nrow(spec_target_lwr))
{
for(j in 1:ncol(sens_target_upr))
{
spec_target_lwr[i,j] <- binom.confint( spec_target_lwr[i,j]*N_negative, N_negative, method="wilson")[1,5]
spec_target_lwr[i,j] <- round( 100*spec_target_lwr[i,j], 1)
}
}
spec_target_upr <- spec_target
for(i in 1:nrow(spec_target_upr))
{
for(j in 1:ncol(sens_target_upr))
{
spec_target_upr[i,j] <- binom.confint( spec_target_upr[i,j]*N_negative, N_negative, method="wilson")[1,6]
spec_target_upr[i,j] <- round( 100*spec_target_upr[i,j], 1)
}
}
sens_target <- round( 100*sens_target, 1)
spec_target <- round( 100*spec_target, 1)
################################################################################
################################################################################
## ##
## #### #### ##### ##### ##### ## #### ###### #### #### # ## ##
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
## ## ## ## ##### ##### #### ## ###### ## ## ## ## ### ## ##
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ### ##
## #### #### ## ## ## ## ##### ##### ## ## ## #### #### ## ## ##
## ##
################################################################################
################################################################################
library(fields)
AB_cor <- matrix(NA, nrow=24, ncol=24)
cor_seq <- c(10:16, 43:47, 23:29, 56:60)
for(i in 1:24)
{
for(j in 1:24)
{
AB_i <- AB_data[,cor_seq[i]]
AB_j <- AB_data[,cor_seq[j]]
index_ij <- which( is.na(AB_i)==FALSE & is.na(AB_j)==FALSE )
AB_i <- AB_i[index_ij]
AB_j <- AB_j[index_ij]
AB_cor[i,j] = cor( AB_i, AB_j, method="spearman")
}
}
##################################
##################################
## ##
## ##### ## #### ###### ##
## ## ## ## ## ## ## ##
## ##### ## ## ## ## ##
## ## ## ## ## ## ##
## ## ##### #### ## ##
## ##
##################################
##################################
plot_names <- c("anti-Stri IgG", "anti-RBDv1 IgG", "anti-RBDv2 IgG", "anti-S1 IgG", "anti-S2 IgG",
"anti-NPv1 IgG", "anti-NPv2 IgG", "anti-229E_NP IgG",
"anti-NL63_NP IgG", "anti-fluA IgG", "anti-ade40 IgG", "anti-rub IgG",
"anti-Stri IgM", "anti-RBDv1 IgM", "anti-RBDv2 IgM", "anti-S1 IgM", "anti-S2 IgM",
"anti-NPv1 IgM", "anti-NPv2 IgM", "anti-229E_NP IgM","anti-NL63_NP IgM",
"anti-fluA IgM", "anti-ade40 IgM", "anti-rub IgM")
plot_names_short <- c("Stri IgG", "RBDv1 IgG", "RBDv2 IgG", "S1 IgG", "S2 IgG",
"NPv1 IgG", "NPv2 IgG", "229E_NP IgG",
"NL63_NP IgG", "fluA IgG", "ade40 IgG", "rub IgG",
"Stri IgM", "RBDv1 IgM", "RBDv2 IgM", "S1 IgM", "S2 IgM",
"NPv1 IgM", "NPv2 IgM", "229E_NP IgM","NL63_NP IgM",
"fluA IgM", "ade40 IgM", "rub IgM")
ant_cols <- c("red", "brown", "orangered", "gold", "yellow",
"forestgreen", "limegreen", "mediumblue", "royalblue",
"burlywood", "darkkhaki", "cornsilk4",
"red", "brown", "orangered", "gold", "yellow",
"forestgreen", "limegreen", "mediumblue", "royalblue",
"burlywood", "darkkhaki", "cornsilk4")
tiff(file="Fig1_single_AB.tif", width=40, height=30, units="cm", res=500)
lay.mat <- rbind( c( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ),
c( 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7 ),
c( 8, 8, 9, 9,10,10,11,11,12,12,13,13 ),
c(14,14,14,14,14,14,14,14,14,14,14,14 ),
c(15,15,16,16,17,17,18,18,19,19,20,20 ),
c(21,21,22,22,23,23,24,24,25,25,26,26 ),
c(27,27,27,28,28,28,29,29,29,30,30,30 ),
c(27,27,27,28,28,28,29,29,29,31,31,31 ) )
layout(lay.mat, heights=c(1.5,8,8,1.5,8,8,14,2), widths=c(1,1,1,1,1,1))
layout.show(31)
############################
## Labels on top ##
############################
par(mar = c(0,0,0,0))
plot.new()
title( "(A) IgG antibody responses",
cex.main=3.0, line=-2)
#####################################
##
## PANELS 1:7
par(mar=c(3,5,3,1))
par(mgp=c(2.5, 0.7, 0))
point.size = 0.75
lab.size = 1.5
axis.size = 1.1
main.size = 2
plot_seq <- 10:16
line_seq_x <- c(0.00001, 0.00003, 0.0001, 0.0003, 0.001, 0.003, 0.01)
for( k in 1:7 )
{
boxplot( AB_data[which(AB_data$site=="Bichat"),plot_seq[k]],
AB_data[which(AB_data$site=="Strasbourg"),plot_seq[k]],
AB_data[which(AB_data$site=="Cochin"),plot_seq[k]],
AB_data[which(AB_data$site=="EFS"),plot_seq[k]],
AB_data[which(AB_data$site=="PNC"),plot_seq[k]],
AB_data[which(AB_data$site=="TRC"),plot_seq[k]],
log="y",
pch=19, yaxt='n', xaxt='n',
ylim=c(1e-5, 0.03),
col=ant_cols[k],
ylab="",
main=plot_names[k],
cex.lab=lab.size, cex.axis=axis.size, cex.main=main.size)
for(i in 1:length(line_seq_x))
{
points(x=c(0,100), y=rep(line_seq_x[i],2), type='l', col="grey", lty="dashed")
}
mtext(side = 2, line = 3.5,
cex=0.8*axis.size,
text=" antibody dilution")
axis(1, at=c(1, 3, 5), label=c("Bichat", "Cochin", "PNC"), cex.axis=0.8*axis.size )
axis(1, at=c(2, 4, 6), label=c( "Sburg", "TRC", "EFS"), cex.axis=0.8*axis.size )
axis(2, at=c(0.00001, 0.0001, 0.001, 0.01),
label=c("0.00001", "0.0001", "0.001", "0.01"),
las=2, cex.axis=axis.size )
}
#####################################
##
## PANELS 8:12
plot_seq <- 43:47
line_seq_x <- c(10, 30, 100, 300, 1000, 3000, 10000, 30000)
for( k in 1:5 )
{
boxplot( AB_data[which(AB_data$site=="Bichat"),plot_seq[k]],
AB_data[which(AB_data$site=="Strasbourg"),plot_seq[k]],
AB_data[which(AB_data$site=="Cochin"),plot_seq[k]],
AB_data[which(AB_data$site=="EFS"),plot_seq[k]],
AB_data[which(AB_data$site=="PNC"),plot_seq[k]],
AB_data[which(AB_data$site=="TRC"),plot_seq[k]],
log="y",
pch=19, yaxt='n', xaxt='n',
ylim=c(10, 30000),
col=ant_cols[7+k],
ylab="",
main=plot_names[7+k],
cex.lab=lab.size, cex.axis=axis.size, cex.main=main.size)
for(i in 1:length(line_seq_x))
{
points(x=c(0,100), y=rep(line_seq_x[i],2), type='l', col="grey", lty="dashed")
}
mtext(side = 2, line = 3.5,
cex=0.8*axis.size,
text=" MFI")
axis(1, at=c(1, 3, 5), label=c("Bichat", "Cochin", "PNC"), cex.axis=0.8*axis.size )
axis(1, at=c(2, 4, 6), label=c( "Sburg", "TRC", "EFS"), cex.axis=0.8*axis.size )
axis(2, at=c(10, 30, 100, 300, 1000, 3000, 10000, 30000),
label=c("10", "", "100", "", "1000", "", "10000", ""),
las=2, cex.axis=axis.size )
}
############################
## Labels on top ##
############################
par(mar = c(0,0,0,0))
plot.new()
title( "(B) IgM antibody responses",
cex.main=3.0, line=-2)
#####################################
##
## PANELS 13:19
par(mar=c(3,5,3,1))
par(mgp=c(2.5, 0.7, 0))
plot_seq <- 23:29
line_seq_x <- c(0.00001, 0.00003, 0.0001, 0.0003, 0.001, 0.003, 0.01)
for( k in 1:7 )
{
boxplot( AB_data[which(AB_data$site=="Bichat"),plot_seq[k]],
AB_data[which(AB_data$site=="Strasbourg"),plot_seq[k]],
AB_data[which(AB_data$site=="Cochin"),plot_seq[k]],
AB_data[which(AB_data$site=="EFS"),plot_seq[k]],
AB_data[which(AB_data$site=="PNC"),plot_seq[k]],
AB_data[which(AB_data$site=="TRC"),plot_seq[k]],
log="y",
pch=19, yaxt='n', xaxt='n',
ylim=c(1e-5, 0.03),
col=ant_cols[12+k],
ylab="",
main=plot_names[12+k],
cex.lab=lab.size, cex.axis=axis.size, cex.main=main.size)
for(i in 1:length(line_seq_x))
{
points(x=c(0,100), y=rep(line_seq_x[i],2), type='l', col="grey", lty="dashed")
}
mtext(side = 2, line = 3.5,
cex=0.8*axis.size,
text=" antibody dilution")
axis(1, at=c(1, 3, 5), label=c("Bichat", "Cochin", "PNC"), cex.axis=0.8*axis.size )
axis(1, at=c(2, 4, 6), label=c( "Sburg", "TRC", "EFS"), cex.axis=0.8*axis.size )
axis(2, at=c(0.00001, 0.0001, 0.001, 0.01),
label=c("0.00001", "0.0001", "0.001", "0.01"),
las=2, cex.axis=axis.size )
}
#####################################
##
## PANELS 20:24
plot_seq <- 56:60
line_seq_x <- c(10, 30, 100, 300, 1000, 3000, 10000, 30000)
for( k in 1:5 )
{
boxplot( AB_data[which(AB_data$site=="Bichat"),plot_seq[k]],
AB_data[which(AB_data$site=="Strasbourg"),plot_seq[k]],
AB_data[which(AB_data$site=="Cochin"),plot_seq[k]],
AB_data[which(AB_data$site=="EFS"),plot_seq[k]],
AB_data[which(AB_data$site=="PNC"),plot_seq[k]],
AB_data[which(AB_data$site=="TRC"),plot_seq[k]],
log="y",
pch=19, yaxt='n', xaxt='n',
ylim=c(10, 30000),
col=ant_cols[19+k],
ylab="",
main=plot_names[19+k],
cex.lab=lab.size, cex.axis=axis.size, cex.main=main.size)
for(i in 1:length(line_seq_x))
{
points(x=c(0,100), y=rep(line_seq_x[i],2), type='l', col="grey", lty="dashed")
}
mtext(side = 2, line = 3.5,
cex=0.8*axis.size,
text=" MFI")
axis(1, at=c(1, 3, 5), label=c("Bichat", "Cochin", "PNC"), cex.axis=0.8*axis.size )
axis(1, at=c(2, 4, 6), label=c( "Sburg", "TRC", "EFS"), cex.axis=0.8*axis.size )
axis(2, at=c(10, 30, 100, 300, 1000, 3000, 10000, 30000),
label=c("10", "", "100", "", "1000", "", "10000", ""),
las=2, cex.axis=axis.size )
}
#####################################
#####################################
##
## PANEL 25
## IgG ROC analysis
line_seq <- c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0)
par(mar=c(5,7,2,1.0))
par(mgp=c(2.5, 1.25,0))
plot(x=c(0,1), y=c(0,1), type='l', lty="dashed",
xlim=c(0,1.002), ylim=c(0,1.002),
#xaxs='i', yaxs='i',
xaxt='n', yaxt='n', bty='n',
xlab="", ylab="",
main="(C) IgG ROC analysis",
cex.lab=2, cex.axis=1.5, cex.main=1.75)
mtext(side = 1, line = 3.4,
cex=2,
text="1 - specificity")
mtext(side = 2, line = 5,
cex=2,
text="sensitivity")
for(i in 1:length(line_seq))
{
points(x=c(0,1), y=rep(line_seq[i],2), type='l', col="grey", lty="dashed")
points(x=rep(line_seq[i],2), y=c(0,1), type='l', col="grey", lty="dashed")
}
for(k in 1:12)
{
points( x=1-spec_mat[,k], y=sens_mat[,k],
type='S', lwd=2, col=ant_cols[k] )
}
axis(1, at=c(0.0, 0.2, 0.4, 0.6, 0.8, 1),
labels=c("0%", "20%", "40%", "60%", "80%", "100%"),
cex.axis=2)
axis(2, at=c(0.0, 0.2, 0.4, 0.6, 0.8, 1),
labels=c("0%", "20%", "40%", "60%", "80%", "100%"),
las=2, cex.axis=2 )
#####################################
#####################################
##
## PANEL 26
## IgM ROC analysis
line_seq <- c(0.0, 0.2, 0.4, 0.6, 0.8, 1.0)
par(mar=c(5,7,2,1.0))
par(mgp=c(2.5, 1.25,0))
plot(x=c(0,1), y=c(0,1), type='l', lty="dashed",
xlim=c(0,1.002), ylim=c(0,1.002),
#xaxs='i', yaxs='i',
xaxt='n', yaxt='n', bty='n',
xlab="", ylab="",
main="(D) IgM ROC analysis",
cex.lab=2, cex.axis=1.5, cex.main=1.75)
mtext(side = 1, line = 3.4,
cex=2,
text="1 - specificity")
mtext(side = 2, line = 5,
cex=2,
text="sensitivity")
for(i in 1:length(line_seq))
{
points(x=c(0,1), y=rep(line_seq[i],2), type='l', col="grey", lty="dashed")
points(x=rep(line_seq[i],2), y=c(0,1), type='l', col="grey", lty="dashed")
}
for(k in 1:12)
{
points( x=1-spec_mat[,13+k], y=sens_mat[,13+k],
type='S', lwd=2, col=ant_cols[k] )
}
axis(1, at=c(0.0, 0.2, 0.4, 0.6, 0.8, 1),
labels=c("0%", "20%", "40%", "60%", "80%", "100%"),
cex.axis=2)
axis(2, at=c(0.0, 0.2, 0.4, 0.6, 0.8, 1),
labels=c("0%", "20%", "40%", "60%", "80%", "100%"),
las=2, cex.axis=2 )
###################################
## Panel 27: AUC
ant_cols <- c("red", "brown", "orangered", "gold", "yellow",
"forestgreen", "limegreen", "mediumblue", "royalblue",
"burlywood", "darkkhaki", "cornsilk4",
"red", "brown", "orangered", "gold", "yellow",
"forestgreen", "limegreen", "mediumblue", "royalblue",
"burlywood", "darkkhaki", "cornsilk4")
plot_names_short <- c("Stri IgG", "RBDv1 IgG", "RBDv2 IgG", "S1 IgG", "S2 IgG",
"NPv1 IgG", "NPv2 IgG", "229E_NP IgG",
"NL63_NP IgG", "fluA IgG", "ade40 IgG", "rub IgG",
"Stri IgM", "RBDv1 IgM", "RBDv2 IgM", "S1 IgM", "S2 IgM",
"NPv1 IgM", "NPv2 IgM", "229E_NP IgM","NL63_NP IgM",
"fluA IgM", "ade40 IgM", "rub IgM")
PP <- 1
gap <- 0.1
line_seq_y <- c(0.0, 0.25, 0.5, 0.75, 1)
par(mar=c(5,7,2,1.0))
par(mgp=c(2.5, 1.25,0))
plot(x=100, y=100, type='l', lty="dashed",
xlim=c(0,24*(1+gap)), ylim=c(0,1.002),
xaxs='i',
# yaxs='i',
xaxt='n', yaxt='n', bty='n',
xlab="", ylab="",
main="(E) Area under ROC curve",
cex.lab=2, cex.axis=1.5, cex.main=1.75)
mtext(side = 2, line = 4.5,
cex=2,
text="AUC")
for(k in 1:26)
{
polygon( x=gap*(k-1) + c(k-1,k,k,k-1), c(0,0,AUC_one_ant[k]^PP,AUC_one_ant[k]^PP), col=ant_cols[k], border=NA)
}
for(i in 1:length(line_seq_y))
{
points(x=c(0,100), y=rep(line_seq_y[i]^PP,2), type='l', col="grey", lty="dashed")
}
axis(2, at=c(0.0, 0.25, 0.5, 0.75, 1)^PP,
labels=c("0.0", "0.25", "0.5", "0.75", "1.0"),
las=2, cex.axis=2 )
axis(1, at=seq(from=0.5, by=1+gap, length=24), label=plot_names_short, las=2, cex.axis=0.5)
#####################################
#####################################
##
## PANEL 28
## Correlation
par(mar=c(2,3,2,1))
par(mgp=c(1.5,0.75,0))
##N_cor_steps <- 100
##
##cor_cols <- rev(heat.colors(N_cor_steps))
cor_cols = c("springgreen4", "springgreen", "palegreen", "yellowgreen", "yellow",
"gold", "orange", "orangered", "firebrick1", "red3" )
N_cor_steps = length(cor_cols)
plot(x=100, y=100,
xlim=c(0,24), ylim=c(0,24),
xlab="", ylab="",
main="(F) Correlation between antibody titres",
xaxt='n', yaxt='n', xaxs='i', yaxs='i',
cex.lab=2, cex.axis=1.5, cex.main=1.75)
for(i in 1:24)
{
for(j in 1:24)
{
polygon(x=c(i-1,i,i,i-1), y=c(j-1,j-1,j,j),
border=NA, col="blue" )
polygon(x=c(i-1,i,i,i-1), y=c(j-1,j-1,j,j),
border=NA, col=cor_cols[ceiling(N_cor_steps*AB_cor[i,j])] )
}
}
axis(1, at=seq(from=0.5, by=1, length=24), label=plot_names_short, las=2, cex.axis=0.5)
axis(2, at=seq(from=0.5, by=1, length=24), label=plot_names_short, las=2, cex.axis=0.5)
#############
## LEGEND
par(mar=c(2,1,1,1))
plot(x=100, y=100,
xlim=c(-10,100), ylim=c(0,1),
xlab="", ylab="",
main="",
xaxt='n', yaxt='n', xaxs='i', yaxs='i', bty='n')
polygon(y=c(0,1,1,0), x=c(-10,-10,0,0),
border=NA, col="blue")
for(i in 1:length(cor_cols))
{
polygon(y=c(0,1,1,0), x=c(i-1,i-1,i,i)*100/N_cor_steps,
border=NA, col=cor_cols[i])
}
axis(1, at=100*c(-0.1,0,0.25,0.5,0.75,1), label=c("-100%", "0%", "25%", "50%", "75%", "100%"), cex.axis=1)
dev.off()