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06_application_index_comparison_20201010.R
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06_application_index_comparison_20201010.R
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# Identification
# correlation
# reproducibility
# CV
# SNR
if(T){
meta_data.cp = meta_data[which(meta_data$Purpose == 'Replications'),]
meta_data.cp.uids = paste(
meta_data.cp$Labcode,
meta_data.cp$SInstrument,
meta_data.cp$Batch,
sep = '_'
)
meta_data.cp$UID = meta_data.cp.uids
uids = sort(unique(meta_data.cp.uids))
uids_code = c(
paste('D0', seq(1,9), sep = ''),
paste('D1', seq(0,9), sep = ''),
paste('D2', seq(0,6), sep = '')
)
meta_data.cp$Code = NA
for (i in 1:length(uids)) {
index = which(meta_data.cp.uids==uids[i])
meta_data.cp$Code[index] = uids_code[i]
}
meta_data.cp$Code = factor(meta_data.cp$Code, levels = uids_code)
meta_data.cp = meta_data.cp[order(meta_data.cp$Code),]
a = data.frame(
unique(meta_data.cp$UID),
unique(meta_data.cp$Code)
)
}
get_pcorr <- function(df, method = 'pearson'){
df = i.px.fot_data
mat = as.matrix(i.px.fot_data[,-1])
non_zero_min = min(mat[mat>0])/10
# mat[mat==0] = non_zero_min
mat.col = ncol(mat)
corvector = NULL
for (i in 1:mat.col) {
x = mat[,i]
for (j in 1:mat.col) {
if(i!=j){
y = mat[,j]
xy.index = which(x>0|y>0)
xy.cor = cor(x[xy.index], y[xy.index], method = method)
corvector = c(corvector, xy.cor)
}
}
}
corvector = unique(corvector)
cormedian = round(median(corvector), 2)
return(cormedian)
}
get_reproducibility <- function(df){
# df = i.px.fot_data
mat = as.matrix(i.px.fot_data[,-1])
non_zero_min = min(mat[mat>0])/10
# mat[mat==0] = non_zero_min
mat.col = ncol(mat)
reprovector = NULL
for (i in 1:mat.col) {
x = mat[,i]
for (j in 1:mat.col) {
if(i!=j){
y = mat[,j]
xy.index = which(x>0 & y>0)
xy.repro = length(xy.index)/length(x[x>0])
reprovector = c(reprovector, xy.repro)
}
}
}
reprovector = unique(reprovector)
repromedian = round(median(reprovector), 2)
return(repromedian)
}
ref_names = read_excel('26_SNR_Figures_20201015.xlsx')
#### ID/PCorr/Repro/CV ####
library(ggrepel)
p_list = list()
px_list = c('D5', 'D6', 'F7', 'M8')
# if(T){
for (pxi in 1:4) {
# D5
px = px_list[pxi]
id_list = list()
uids_code_count = length(uids_code)
for (i in 1:uids_code_count) {
i.px.metadata = meta_data.cp[which(meta_data.cp$Code==uids_code[i]),]
i.px.metadata = i.px.metadata[order(i.px.metadata$Person),]
i.px.metadata = i.px.metadata[i.px.metadata$UPerson==px,]
i.px.fot_data = get_fot_df_by_expnames(fot_data, i.px.metadata$Exp_code)
i.df = data.frame(
ID = floor(mean(apply(i.px.fot_data[,-1], 2, function(x){length(which(x>0))}))),
PCorr = get_pcorr(i.px.fot_data),
Repro = get_reproducibility(i.px.fot_data),
CV = round(median(apply(i.px.fot_data[,-1], 1, function(x){sd(x)/mean(x)})), 2)
)
id_list[[i]] = i.df
print(i)
}
names(id_list) = uids
print(unlist(lapply(id_list, function(x){x$CV})))
df = data.frame(
Labcode = uids_code,
UID = uids,
ID = unlist(lapply(id_list, function(x){x$ID})),
PCorr = unlist(lapply(id_list, function(x){x$PCorr})),
Repro = unlist(lapply(id_list, function(x){x$Repro})),
CV = unlist(lapply(id_list, function(x){x$CV}))
)
mindex = match(df$UID, ref_names$UID)
df$Labcode = ref_names$UID_new
df$Labcode = factor(df$Labcode, levels = df$Labcode)
p <- ggplot(data=df, aes(x=PCorr, y=Repro, size = ID, color = CV))
p <- p + geom_point(alpha=0.7)
# p <- p + scale_fill_gradientn(colours=magma(100)[10:100])
# p + scale_size(range = c(0.21, 0.76), name="Population (M)")
p <- p + scale_color_viridis(discrete=F)
p <- p + geom_text_repel(
aes(PCorr, Repro, label= Labcode),
color="black",
box.padding=unit(0.35, "lines"), point.padding=unit(0.5, "lines"),
segment.colour = "grey50", size = 4
)
p <- p + scale_x_continuous(breaks=seq(0.4, 1, 0.1), limits = c(0.4, 1))
# p <- p + scale_y_continuous(breaks=seq(0.8, 0.95, 0.05), limits = c(0.8, 0.95))
p <- p + xlab('Pearson correlation coefficient') + ylab('Reproducibility')
p <- p + ggtitle(paste(
'4D evaluation from three replicates of single sample (', px, ')', sep = ''
))
p <- p + theme_bw()
p_list[[pxi]] = p
}
ggarrange(#11*9
p_list[[1]], p_list[[2]], p_list[[3]], p_list[[4]],
nrow = 2, ncol = 2,
labels = c('A', 'B', 'C', 'D')
)
temp <- venn.diagram(
x = list(
R1 = i.px.fot_data$GeneSymbol[i.px.fot_data$Exp036599>0],
R2 = i.px.fot_data$GeneSymbol[i.px.fot_data$Exp036600>0],
R3 = i.px.fot_data$GeneSymbol[i.px.fot_data$Exp036601>0]
),
filename = NULL,
main = 'JNU_Lumos_2',
# main.just = c(0, 5),
main.pos = c(0.5,1.05),
main.cex = 0.5,
output = TRUE ,
imagetype="png" ,
height = 480 ,
width = 480 ,
resolution = 300,
compression = "lzw",
lwd = 1,
# col = jco_col,
# fill = jco_col,
cex = 0.5,
fontfamily = "sans",
cat.cex = 0.5,
cat.default.pos = "outer",
# cat.pos = c(-2, 27, -135, 135),
# cat.dist = c(0.055, 0.055, 0.085),
cat.fontfamily = "sans"
# cat.col = p_colors
# rotation = 1,
# print.mode = 'percent'
)
grid.draw(temp)