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HyperSCP_byGradient.R
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HyperSCP_byGradient.R
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#boost:126
#not used: 127C
#reference: 134N
#for carrier-free samples: no 126 and 127C
setwd("E:/Hyperplex/PD2.5_EclipseData/R_processing") # source code location
source("HyperSCP_Function.R", echo=TRUE)
figure_save = "E:/Hyperplex/PD2.5_ExplorisData/Exploris_ByGradient/Figure/" # where to save figures
setwd("E:/Hyperplex/PD2.5_ExplorisData/Exploris_ByGradient") # where the PD output saves
ref_channel = "134N" # which channel is the reference channel
TMTchannels = c("126","127N","127C","128N","128C","129N","129C","130N","130C",
"131N","131C", "132N","132C","133N","133C","134N", "134C", "135N")
sc_channels = paste0("X", TMTchannels[-which(TMTchannels %in% c("126","127C",ref_channel))])
ref_channel = paste0("X", ref_channel)
TMTchannels = paste0("X", TMTchannels)
### read and assign channels, report annotation H and L as 2 different files.
channel_assign = read_csv("E:/Hyperplex/PD2.5_ExplorisData/Table S1. 2_cells in channles.csv")
PDrunName = "ExMloading_60min" #fill here
file_name_L = read_file_name(PDrunName,HorL = "L")
file_name_H = read_file_name(PDrunName,HorL = "H")
file_name = rbind(file_name_L, file_name_H)
rm(file_name_L, file_name_H)
fileIDs = unique(file_name$FileID_SILAC)
cells_in_channel = data.frame()
for (file in fileIDs){
row = which(file_name$FileID_SILAC == file)
field = file_name[[row, "field"]]
chip = file_name[[row, "chip"]]
fileID = file_name[[row, "File ID"]]
cell_in_channel = read_channel(channel_assign, field, file) %>%
dplyr::mutate(FileID_SILAC = file, FileID = fileID, Rawfile = file_name[[row, "RawFile"]],
Gradient = file_name[[row, "Gradient"]],
Field = as.numeric(str_replace(Location,"F",""))) %>%
dplyr::mutate(Carrier = ifelse(Field <= 21, "10 ng", "None")) %>%
dplyr::select(FileID, FileID_SILAC, Carrier, Rawfile, Gradient, starts_with("1"))
if (str_detect(chip,"Chip1|Chip2")){
cell_in_channel = actual_cell(cell_in_channel, "HeLa", "K562")
}
else if(chip == "Chip3"){
cell_in_channel = actual_cell(cell_in_channel, "HeLa", "A549")
}
else if(chip == "Chip4"){
cell_in_channel = actual_cell(cell_in_channel, "HFL1", "A549")
}
cells_in_channel = rbind(cells_in_channel, cell_in_channel)
}
cells_in_channel_longer = cells_in_channel %>%
pivot_longer(cols = starts_with("x1"), names_to = "Channel", values_to = "Cell")%>%
dplyr::mutate(Identifier = paste0(FileID_SILAC, "_", Channel), Raw = Rawfile) %>%
separate(Raw, into=c("Chip","left","Field"), sep = "_") %>%
select(FileID_SILAC,Carrier, Rawfile, Gradient,Channel,Cell,Identifier, Chip)
rm(cell_in_channel, chip, field, file, fileID, row)
# write_csv(cells_in_channel, paste0("annotation_PDrunName", HorL, ".csv"))
#cells_in_channel_chip4 = cells_in_channel_longer %>%
# filter(Chip == "Chip4", Gradient == "60min") %>%
# mutate(Id_cell = paste0(Identifier, ".", Cell))
#cells_in_channel_chip4 = cells_in_channel_chip4[-which(str_detect(cells_in_channel_chip4$Cell, "NotUsed|Boost|Reference")),]
#Chip4_sample = cells_in_channel_chip4$Id_cell
### read PSM and normalization ###xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
PSM_L = read_PSM(PDrunName, HorL = "L")
PSM_H = read_PSM(PDrunName, HorL = "H")
PSM = rbind(PSM_L, PSM_H)
PSM = dplyr::full_join(PSM,cells_in_channel[,c("Carrier","FileID_SILAC", "Gradient")], by = "FileID_SILAC")
rm(PSM_L, PSM_H)
#filter and select the carrier
PSM_noCarrier = PSM %>%
dplyr::filter(Carrier == "None")
PSM_Carrier = PSM %>%
dplyr::filter(Carrier == "10 ng") #reduce possible contaminated channels
PSM_Carrier$CarrierRatio = apply(PSM_Carrier[, sc_channels]/PSM_Carrier$X126, 1, median, na.rm = TRUE)
PSM_Carrier = PSM_Carrier %>%
dplyr::filter(CarrierRatio <= 0.05) #reduce possible contaminated channels
# start data including carrier/or not
PSM_all = as.data.frame(PSM_Carrier) ##change here: "PSM_Carrier" or "PSM_noCarrier"
# Reporter ion intensity distributions #
PSM_sc_longer1 = PSM_all %>%
dplyr::mutate(`Annotated Sequence` = paste0(`Annotated Sequence`, "_", Charge)) %>%
dplyr::select(`Spectrum File`, Carrier, FileID_SILAC,`Annotated Sequence`, `Master Protein Accessions`, all_of(sc_channels))
colnames(PSM_sc_longer1) = c("RawFile", "Carrier", "FileID_SILAC", "Sequence", "Protein", sc_channels)
PSM_sc_longer1 = PSM_sc_longer1 %>%
pivot_longer(cols = starts_with("X1"), names_to = "Channel", values_to = "Quan")%>%
dplyr::mutate(Identifier = paste0(FileID_SILAC, "_", Channel)) %>%
dplyr::mutate(Quan = log(Quan, base = 2))
PSM_sc_longer1[which(is.na(PSM_sc_longer1$Quan)), "Quan"] = 0
PSM_sc_longer1 = dplyr::left_join(PSM_sc_longer1, cells_in_channel_longer[, c("Cell", "Identifier")], by = "Identifier")
Q = PSM_sc_longer1 %>%
dplyr::group_by(Cell) %>%
summarise(Q99 = quantile(Quan, 0.99, na.rm = TRUE))
PSM_sc_99p = data.frame()
for(cell in Q$Cell){
PSM_sc_99p_temp = PSM_sc_longer1 %>%
dplyr::filter(Cell == cell, Quan <= dplyr::pull(Q[which(Q$Cell == cell), "Q99"]))
PSM_sc_99p = rbind(PSM_sc_99p, PSM_sc_99p_temp)
rm(PSM_sc_99p_temp)
}
ggplot(PSM_sc_99p, aes(x = Cell, y = Quan, fill = Cell, color = Cell)) +
geom_violin(scale = "area") + theme_bw(base_size = 18) + coord_cartesian(ylim=c(0,20))+
ylab("Log2(Reporter Ion Itensity)") +
xlab("Cell Type") +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),
legend.position = "None")+
theme(axis.text.x = element_text(angle = 0, vjust = 1, size = rel(1.4), color = "black"))+
theme(axis.text.y = element_text(angle = 0, hjust = 1,size = rel(1.4), color = "black"))+
theme(axis.title.x = element_text(angle = 0, vjust = 0.5,size = rel(1.5), color = "black"),
axis.title.y = element_text(angle = 90, hjust = 0.5,size = rel(1.5), color = "black"))+
scale_fill_hue(c=55, l=80)
ggsave(filename = paste0(figure_save,
"RI wcarrier 60min_area.png"), width = 7, height = 6, dpi = 500)
# Reporter ion intensity distributions ends #
#normalization using reference channel:
PSM_all[,TMTchannels] = PSM_all[,TMTchannels]/PSM_all[,ref_channel]
# filter the single-cell channels for later data processing:
PSM_sc = PSM_all %>%
dplyr::mutate(`Annotated Sequence` = paste0(`Annotated Sequence`, "_", Charge)) %>%
dplyr::select(`Spectrum File`, Carrier, FileID_SILAC,`Annotated Sequence`, `Master Protein Accessions`, all_of(sc_channels))
colnames(PSM_sc) = c("RawFile", "Carrier", "FileID_SILAC", "Sequence", "Protein", sc_channels)
PSM_sc_longer = PSM_sc %>%
pivot_longer(cols = starts_with("X1"), names_to = "Channel", values_to = "Quan")%>%
dplyr::mutate(Identifier = paste0(FileID_SILAC, "_", Channel))
PSM_sc_longer = dplyr::left_join(PSM_sc_longer, cells_in_channel_longer[, c("Cell", "Identifier")], by = "Identifier")
PSM_sc_longer = PSM_sc_longer %>%
dplyr::filter(!is.na(Protein)) %>%
dplyr::mutate(find_duplicate = paste0(Sequence, "_", Identifier), Id_cell = paste0(Identifier, ".", Cell))
#find out the duplicate sequence from the same channel and then keep the max quan item
#duplicate_name = PSM_sc_longer %>% group_by(find_duplicate) %>% summarise(freq = n()) %>%
# filter(freq>1) %>% select (find_duplicate)
# This is the same but faster
# Take the mean of the replicates
duplicate_name = sqldf("select find_duplicate, count(find_duplicate)
count from PSM_sc_longer group by find_duplicate having count >1")$find_duplicate
duplicate_data = PSM_sc_longer[PSM_sc_longer$find_duplicate %in% duplicate_name, ]
duplicate_data = duplicate_data %>%
dplyr::group_by(find_duplicate) %>%
mutate(Quan = mean(Quan))
duplicate_data = duplicate_data[!duplicated(duplicate_data[,"find_duplicate"]),]
PSM_sc_longer = rbind(PSM_sc_longer[!PSM_sc_longer$find_duplicate %in% duplicate_name, ], duplicate_data)
rm(duplicate_name,duplicate_data)
########### This is to find the missing value rates between H & L ###################
pep_longer = PSM_sc_longer %>%
mutate(pep_find = paste0(Sequence, "__", FileID_SILAC))
pep_list = as.data.frame(unique(pep_longer$pep_find)) %>%
separate(`unique(pep_longer$pep_find)`, sep = "__", into = c("Sequence", "FileID_SILAC")) %>%
mutate(count = 1)
pep_list = pep_list %>%
pivot_wider(names_from = "FileID_SILAC", values_from = count)
#with carrier or not
files = unique(cells_in_channel[which(cells_in_channel$Carrier == "10 ng"),"FileID"])
# if not:
files = unique(cells_in_channel[which(cells_in_channel$Carrier == "None"),"FileID"])
#count
find_missing_rate = data.frame()
i = 1
for (file in files){
Lfile = paste0("L", file)
Hfile = paste0("H", file)
pep_file = pep_list[, c("Sequence", Lfile, Hfile)]
pep_file[, "NAno"] = is.na(pep_file[,Lfile]) + is.na(pep_file[,Hfile])
pep_file = pep_file %>% filter(NAno != 2)
find_missing_rate[i, "File"] = file
find_missing_rate[i, "Light"] = sum(is.na(pep_file[,Lfile]))/ nrow(pep_file)*100
find_missing_rate[i, "Heavy"] = sum(is.na(pep_file[,Hfile]))/ nrow(pep_file)*100
i = i + 1
}
find_missing_rate = find_missing_rate %>%
pivot_longer(cols = c("Light", "Heavy"), names_to = "label", values_to = "value")
# if combine 60min and 90min separately
MR_60mim = find_missing_rate
MR_60mim = MR_60mim %>%
mutate(Gradient = "60 min")
MR_90mim = find_missing_rate
MR_60mim = MR_90mim %>%
mutate(Gradient = "90 min")
MR = rbind(MR_60mim, MR_90mim)
ggplot(data = MR, aes(x = label, y = value, fill = Gradient))+
geom_boxplot(width = 0.3, alpha = 0.5, na.rm = TRUE)+
xlab("SILAC Label")+
ylab("Missing Rate (%)")+
theme_bw(base_size = 18) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),
axis.text.x = element_text(angle = 0, hjust = 0.5,size = rel(1.1), color = "black"),
axis.text.y = element_text(angle = 0, hjust = 0.5,size = rel(1.1), color = "black"),
axis.title.x = element_text(angle = 0, vjust = 0.5,size = rel(1.1), color = "black"),
axis.title.y = element_text(angle = 90, hjust = 0.5,size = rel(1.1), color = "black"),
legend.title = element_text(size = 18),
legend.text = element_text(size = 17),
legend.position = c(0.75, 0.8),
legend.background = element_rect(fill = "transparent"))
ggsave(filename = paste0(figure_save, "Missing Value 60and90 min wCarrier.png"),
width = 5, height = 5, dpi = 500)
# end
ggplot(data = find_missing_rate, aes(x = label, y = value))+
geom_boxplot(width = 0.3, alpha = 0.5, na.rm = TRUE)+
xlab("SILAC Label")+
ylab("Missing Rate (%)")+
theme_bw(base_size = 18) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),
axis.text.x = element_text(angle = 0, hjust = 0.5,size = rel(1.05), color = "black"),#legend.position = "top",
axis.text.y = element_text(size = 16),
legend.title = element_text(size = 16),
legend.text = element_text(size = 14))
# + geom_jitter(color="black", size=0.4, alpha=0.9)
ggsave(filename = paste0(figure_save, "Missing Value 90 min Carrier.png"),
width = 5, height = 5, dpi = 500)
#Select data in Chip4:
#PSM_sc_longer = PSM_sc_longer[which(PSM_sc_longer$Id_cell %in% Chip4_sample),]
#PSM_sc_chip4_output = PSM_sc_longer %>%
# pivot_wider(names_from = "Id_cell", values_from = "Quan", id_cols = c("Sequence", "Protein"))
#write_tsv(as.data.frame(PSM_sc_chip4_output), "PSM_noCarrier_sc_chip4_all_ref_norm.txt")
#rm(PSM_sc_chip4_output)
################################################################################
# calculate CV -PSM (actually protein level, based on SCoPE2) #
PSM_sc_cv = PSM_sc_longer
PSM_sc_cv$Quan[PSM_sc_cv$Quan == Inf] = NA
PSM_sc_cv$Quan[PSM_sc_cv$Quan == 0] = NA
PSM_sc_cv = PSM_sc_cv %>%
dplyr::group_by(Identifier) %>%
dplyr::mutate(cell_median = median(Quan, na.rm= TRUE), norm_cell = Quan/cell_median) %>% # norm_cell = Reporter ion intensity divided by the median of each cell
dplyr::group_by(Sequence, RawFile) %>% #FileID_SILAC RawFile
dplyr::mutate(norm_raw_seq = Quan / mean(norm_cell, na.rm=TRUE)) %>% # for each FileID_SILAC, and each sequence, Quan data was normalized by the mean of norm_cell
dplyr::group_by(Protein, Identifier) %>%
dplyr::mutate(cv = sd(norm_raw_seq, na.rm=T) / mean(norm_raw_seq, na.rm=T)) %>%
dplyr::group_by(Identifier) %>%
dplyr::mutate(cv_median = median(cv, na.rm=T))
# PSM_sc_cv1 is the kept peptides for cv calculation (has more than 1 normalization value /sequence/rawfile, otherwise no cv)
PSM_sc_cv1 = PSM_sc_cv%>%
dplyr::group_by(Protein, Identifier) %>%
dplyr::mutate(cvn = sum(!is.na(norm_raw_seq)))%>%
dplyr::filter(cvn > 1)
hist(unique(PSM_sc_cv1$cv_median[PSM_sc_cv1$Cell != "Control"]), col=rgb(0,1,0,1/4), prob=T, breaks=50, main = "X single cells ", xlab="CV")
hist(unique(PSM_sc_cv1$cv_median[PSM_sc_cv1$Cell == "Control"]), col=rgb(1,0,0,1/4), prob=T, add=T, breaks=50)
# Filter out variable wells and controls
cvBar = 0.3
sc_kept = unique(PSM_sc_cv1$Id_cell[PSM_sc_cv1$Cell != "Control" & PSM_sc_cv1$cv_median < cvBar])
control_kept = unique(PSM_sc_cv1$Id_cell[PSM_sc_cv1$Cell == "Control"& PSM_sc_cv1$cv_median > cvBar])
sc_total = unique(PSM_sc_cv1$Id_cell[PSM_sc_cv1$Cell != "Control"])
control_total = unique(PSM_sc_cv1$Id_cell[PSM_sc_cv1$Cell == "Control"])
PSM_sc_cv1$control = "sc"
PSM_sc_cv1$control[PSM_sc_cv1$Cell == "Control"] = "Control"
# Plot CV
Colors = c( "#fdae61", "#2c7bb6", "#a6dba0", "#d7191c")
plot_cv = ggplot(data = PSM_sc_cv1, aes(x = cv_median)) +
geom_density(aes(fill = control, alpha = 0.6), adjust = 3) +
theme_pubr( ) +
scale_fill_manual(values = Colors[c(1,2)]) +
xlab("Quantification variability") + ylab("Density") + rremove("y.ticks") + rremove("y.text") +
font("xylab", size=25) +
font("x.text", size=20) +
# font("y.text", size=20) +
coord_cartesian(xlim=c(0.0,0.7))+
annotate("text", x=0.07, y= 16, label=paste0(length(sc_kept)," Cells"), size=8, color=Colors[c(2)])+
annotate("text", x=0.55, y= 14, label=paste0(length(control_kept)," Controls"), size=8, color=Colors[c(1)])+
annotate("text", x=0.51, y= 16, label=paste0(length(sc_total) -length(sc_kept)," Cells"), size=8, color=Colors[c(2)])+
annotate("text", x=0.08, y= 14, label=paste0(length(control_total) - length(control_kept)," Controls"), size=8, color=Colors[c(1)])+
rremove("legend") +
geom_vline(xintercept=cvBar, lty=2, size=1.5, color="gray50")
plot_cv
ggsave(plot_cv, filename = paste0(figure_save,
"CVfilter_Carrier_90min_chip4.png"),
width = 7, height = 5, dpi = 500)
#build longer data frame
#PSM_sc_unmelt = dcast(PSM_sc_longer, Sequence ~ Id_cell, value.var = "Quan", fill=NA)
PSM_sc_wider = PSM_sc_longer %>%
dplyr::filter(!is.na(Protein)) %>%
dplyr::select(Sequence, Protein, Quan, Id_cell) %>%
pivot_wider(names_from = "Id_cell", values_from = "Quan")
PSM_sc_wider[PSM_sc_wider == 0] = NA
PSM_sc_wider[PSM_sc_wider == Inf] = NA
PSM_sc_wider[PSM_sc_wider == -Inf] = NA
# filter based on the cv-kept single cells
PSM_sc_sconly = PSM_sc_wider[,c(1,2, which(colnames(PSM_sc_wider) %in% sc_kept))]
PSM_sc_scncon = PSM_sc_wider[,c(1,2,which(colnames(PSM_sc_wider) %in% c(sc_kept,control_kept)))]
# histgram of sc data
par(mfrow=c(3,3))
# normalized by ref
scPSM_by_ref = PSM_sc_sconly
scPSM_by_ref_m = as.matrix(scPSM_by_ref[,-c(1,2)])
hist(as.matrix(scPSM_by_ref_m), breaks="FD", xlim=c(-2,2))
rm(scPSM_by_ref_m)
# normalized by column (by median) and row (by mean)
scPSM_by_CR = cr_norm(scPSM_by_ref,2)
scPSM_by_CR_m = as.matrix(scPSM_by_CR[,-c(1,2)])
hist(scPSM_by_CR_m, breaks="FD", xlim=c(-2,2))
rm(scPSM_by_CR_m)
# filter by missing value
scPSM_filter_NA = filterNApercent(scPSM_by_CR, 0.95, 0.99, n=2) #n: how many cols are not numbers from the left
scPSM_filter_NA_m = as.matrix(scPSM_filter_NA[,-c(1,2)])
hist(scPSM_filter_NA_m, breaks="FD", xlim=c(-2,2))
rm(scPSM_filter_NA_m)
# collapse to protein level by mean
scPSM_longer = scPSM_filter_NA %>%
dplyr::filter(!str_detect(Protein, "\\;")) %>% # delete non-unique peptides?
pivot_longer(cols = starts_with("LF")|starts_with("HF"), names_to = "Id_cell", values_to = "Quan")
scPSM_longer2 = scPSM_longer %>%
filter(!is.na(Quan))
scPSM_longer2 = scPSM_longer2 %>%
mutate(ProteinIdentifier = paste0(Protein, "-", Id_cell))%>%
dplyr::group_by(ProteinIdentifier) %>%
dplyr::summarize(mean = mean(Quan, na.rm=T), freq = n())
scPSM_longer2 = scPSM_longer2 %>%
# dplyr::filter( freq > 1) %>% # if keep >=2 unique peptides
separate(ProteinIdentifier, into = c("Protein", "Id_cell"), sep = "-" ) %>%
select("Protein", "Id_cell", "mean")
scProtein_protein = scPSM_longer2 %>%
pivot_wider(names_from = "Id_cell", values_from = "mean")
rm(scPSM_longer, scPSM_longer2)
# log2 transform
scPSM_log2 = scProtein_protein
scPSM_log2[, -1] = log(scPSM_log2[, -1], base = 2)
scPSM_log2_m = as.matrix(scPSM_log2[, -1])
hist(scPSM_log2_m, breaks="FD", xlim=c(-2,2))
rm(scPSM_log2_m)
#write_tsv(scPSM_log2, "PSM_Carrier_sc_chip4_norm_log2.txt")
# filter proteins
# all data
scProtein = filterNApercent(scPSM_log2, 0.9, 0.99, n=1) #pre-filter, not necessary
#by group - cells, keep the protein at least 60% valid in at least a group
group_HeLa = filterNApercent(Within_Cell(scProtein, "HeLa"), 0.4, 0.99, n = 1)
group_K562 = filterNApercent(Within_Cell(scProtein, "K562"), 0.4, 0.99, n = 1)
group_A549 = filterNApercent(Within_Cell(scProtein, "A549"), 0.4, 0.99, n = 1)
group_HFL1 = filterNApercent(Within_Cell(scProtein, "HFL1"), 0.4, 0.99, n = 1)
scProtein0 = full_join(group_HeLa,group_K562, by = "Protein")
scProtein0 = full_join(scProtein0,group_HFL1, by = "Protein")
scProtein0 = full_join(scProtein0,group_A549, by = "Protein")
# scProtein0 = full_join(group_HFL1,group_A549, by = "Protein")
#imputation of proteins found in each cell types, KNN, by different cell types
imp_HeLa = imp_by_cell(group_HeLa, k=5)
imp_K562 = imp_by_cell(group_K562, k=5)
imp_A549 = imp_by_cell(group_A549, k=5)
imp_HFL1 = imp_by_cell(group_HFL1, k=5)
scProtein_imp = full_join(imp_HeLa,imp_K562, by = "Protein")
scProtein_imp = full_join(scProtein_imp,imp_HFL1, by = "Protein")
scProtein_imp = full_join(scProtein_imp,imp_A549, by = "Protein")
# scProtein_imp = full_join(imp_A549,imp_HFL1, by = "Protein") #chip4 only
#write_tsv(scProtein, "Protein_Carrier_sc_chip4_norm_log2.txt")
#Quantifiable protein ID
par(mfrow=c(1,1))
proteinID_PSM = data.frame()
i = 1
for (colname in colnames(scProtein0)[-1]){
proteinID_PSM[i,"sample"] = colname
proteinID_PSM[i,"ProteinID"] = sum(!is.na(scProtein0[, colname]))
i = i + 1
}
proteinID_PSM = proteinID_PSM %>%
separate(sample, into = c("file", "Cell"), sep = "\\.")
ggplot(data = proteinID_PSM, aes(x = Cell, y = ProteinID, fill = Cell)) +
#geom_violin(na.rm = TRUE)+
geom_boxplot(width = 0.3, alpha = 0.5, na.rm = TRUE)+
xlab("Cell Line")+
ylab("Quantifiable Protein Groups")+
theme_bw(base_size = 18) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank()) +
theme(axis.text.x = element_text(angle = 0, hjust = 1,size = rel(0.9), color = "black"),
axis.text.y = element_text(size = 16)) + rremove("legend")
# + geom_jitter(color="black", size=0.4, alpha=0.9)
ggsave(filename = paste0(figure_save, "QuanProteinID_noCarrier_chip4_60vv.png"),
width = 6, height = 5, dpi = 500)
#filter protein list found in different cell types
scProtein = filterNApercent(scProtein_imp, 0.01, 0.99, n = 1)
scProtein_m = as.matrix(scProtein[, -1])
rownames(scProtein_m) = scProtein[,1]
hist(scProtein_m, breaks="FD", xlim=c(-2,2))
#found in one cell line only
HeLa_special = group_HeLa[which(!group_HeLa[,"Protein"] %in% scProtein[,"Protein"]),"Protein"]
K562_special = group_K562[which(!group_K562[,"Protein"] %in% scProtein[,"Protein"]),"Protein"]
A549_special = group_A549[which(!group_A549[,"Protein"] %in% scProtein[,"Protein"]),"Protein"]
HFL1_special = group_HFL1[which(!group_HFL1[,"Protein"] %in% scProtein[,"Protein"]),"Protein"]
rm(group_HeLa, group_K562, group_A549, group_HFL1,
imp_HeLa, imp_K562, imp_A549, imp_HFL1)
# Batch correction with ComBat
Id_cell = as.data.frame(colnames(scProtein_m)) %>%
separate(`colnames(scProtein_m)`, into = c("File","Channel","Cell"), sep = "[_\\.]" )
Combat_mod = as.data.frame(Id_cell$Cell)
colnames(Combat_mod) = "Cell"
Combat_mod = model.matrix(~as.factor(Cell), data = Combat_mod)
ComBat_norm = ComBat(scProtein_m, batch = Id_cell[,"File"], mod = Combat_mod)
hist(scProtein_m, breaks="FD", xlim=c(-2,2))
hist(ComBat_norm, breaks="FD", xlim=c(-2,2))
## PCA ##############################
#use the imputated data for PCA and volcano (that means, proteins found in 1/2/3 cell types only not included)
# If H or L doesn't need to be shown in figure
protein_List = as.data.frame(t(ComBat_norm))
Id_cell = as.data.frame(rownames(protein_List)) %>%
separate(`rownames(protein_List)`, into = c("File","Channel","Cell"), sep = "[_\\.]" )
# no frame
protein_List = cbind(Cell = Id_cell$Cell, protein_List)
protein_List.pca = prcomp(protein_List[, -1])
summary(protein_List.pca)
autoplot(protein_List.pca, data = protein_List, colour = 'Cell', frame = T, frame.type = 'norm') + #shape = FALSE, label.size = 3
theme_bw(base_size = 18, ) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),
legend.key.size = unit(1, 'cm'), legend.text = element_text(size=20),
legend.title = element_text(size=24))+
theme(axis.text.x = element_text(angle = 0, hjust = 1,size = rel(1.7), color = "black"))+
theme(axis.text.y = element_text(angle = 0, hjust = 1,size = rel(1.7), color = "black"))+
theme(axis.title.x = element_text(angle = 0, vjust = 0.5,size = rel(1.5), color = "black"),
axis.title.y = element_text(angle = 90, hjust = 0.5,size = rel(1.5), color = "black"))
ggsave(filename = paste0(figure_save,
"PCA carrier 90min_.png"), width = 9.4, height = 6, dpi = 500)
### if H or L need to be shown in figure
protein_List = as.data.frame(t(ComBat_norm))
Id_cell = as.data.frame(rownames(protein_List)) %>%
separate(`rownames(protein_List)`, into = c("File","Channel","Cell"), sep = "[_\\.]" ) %>%
separate(File, into = c("SILAC","File"),sep = "F" ) %>%
dplyr::mutate(Label = str_replace_all(SILAC, c("L" = "", "H" = "SILAC_"))) %>%
dplyr::mutate(Cell = paste0(Label, Cell))
# with frame
protein_List = cbind(Cell = Id_cell$Cell, protein_List)
protein_List.pca = prcomp(protein_List[, -1])
summary(protein_List.pca)
cPalette <- c("#a50026", "#1b7837", "#6baed6", "#542788", "#f46d43", "#41ae76", "#08519c", "#dd3497")
autoplot(protein_List.pca, data = protein_List, colour = 'Cell', scale = 0,frame = T, frame.type = 'norm')+
theme_bw(base_size = 18) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),
legend.key.size = unit(1, 'cm'), legend.text = element_text(size=20),
legend.title = element_text(size=24))+
theme(axis.text.x = element_text(angle = 0, hjust = 1,size = rel(2), color = "black"))+
theme(axis.text.y = element_text(angle = 0, hjust = 1,size = rel(2), color = "black"))+
theme(axis.title.x = element_text(angle = 0, vjust = 0.5,size = rel(1.7), color = "black"),
axis.title.y = element_text(angle = 90, hjust = 0.5,size = rel(1.7), color = "black"))
# + scale_fill_manual(values=cPalette) +
# scale_colour_manual(values=cPalette)
ggsave(filename = paste0(figure_save,
"PCA nocarrier Chip4_HandL.png"), width = 10, height = 6, dpi = 500)
######################################################################
# correlation
#correlation can use the data with unique proteins found only in 1/2/3 cell lines scProtein0
#however, the combat normalization has to be used to eliminate the batch effect, which requires no NAs
# Replicates
Accession = as.data.frame(rownames(ComBat_norm))
names(Accession) = "Protein"
dfComBat_norm = cbind(Accession, as.data.frame(ComBat_norm))
# Replicates
cells = c("HeLa", "K562","HFL1","A549")
scProtein_mean = as.data.frame(dfComBat_norm$Protein); colnames(scProtein_mean) = "Protein"
for (Cell in cells){
Within = Within_Cell(dfComBat_norm, cell = Cell)
Average_H = mean_of_cell(Within, cell = "HF[1-9]+"); colnames(Average_H) = paste0("SILAC ", Cell)
Average_L = mean_of_cell(Within, cell = "LF[1-9]+"); colnames(Average_L) = Cell
scProtein_mean = cbind(scProtein_mean, Average_H, Average_L)
}
rm(Cell,Within,Average_H,Average_L)
library(GGally)
ggpairs(scProtein_mean[,-1], upper = list(continuous = wrap(ggally_cor, method = "spearman",size= 4, stars = F)),
lower = list(continuous = wrap("points",size = 0.2))) +
theme_bw() + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank()) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggsave(filename = paste0(figure_save,
"Cor_nocarrier_chip4.png"), width = 7, height = 7, dpi = 500)
library(corrplot)
library(RColorBrewer)
M <-cor(scProtein_mean[,-1])
corrplot(M, type="upper", order="hclust",
col=brewer.pal(n=8, name="RdYlBu"))
# chip 4 : volcano
ComBat_norm_chip4 = as.data.frame(ComBat_norm)
HFL1_col = which(str_detect(colnames(ComBat_norm_chip4), "HFL1"))
A549_col = which(str_detect(colnames(ComBat_norm_chip4), "A549"))
Chip4_dif = ComBat_norm_chip4 %>%
mutate(average.HFL1 = apply(., 1, function(x) mean(x[HFL1_col])),
average.A549 = apply(., 1, function(x) mean(x[A549_col])),
difference = average.A549 - average.HFL1,
pvalue = apply(., 1, function(x) t.test(x[HFL1_col], x[A549_col], var.equal = TRUE)$p.value),
padjust = p.adjust(pvalue, method = "BH", n = length(pvalue)),
nlogpadjust = -log(padjust, base = 10),
nlogp = -log(pvalue, base = 10))
Chip4_dif$diffexpressed = "F"
Chip4_dif$diffexpressed[Chip4_dif$difference > 0.6 & Chip4_dif$pvalue < 0.05] = "up"
Chip4_dif$diffexpressed[Chip4_dif$difference < -0.6 & Chip4_dif$pvalue < 0.05] = "down"
Chip4_dif$BHdiffexpressed = "F"
Chip4_dif$BHdiffexpressed[Chip4_dif$difference > 0.6 & Chip4_dif$padjust < 0.05] = "up"
Chip4_dif$BHdiffexpressed[Chip4_dif$difference < -0.6 & Chip4_dif$padjust < 0.05] = "down"
Chip4_dif$label = NA
Chip4_dif$label[Chip4_dif$diffexpressed != "F"| Chip4_dif$BHdiffexpressed != "F"] =
rownames(Chip4_dif)[Chip4_dif$diffexpressed != "F"| Chip4_dif$BHdiffexpressed != "F"]
Chip4_dif$Protein = rownames(Chip4_dif)
#functions:
proteinlist_L = read_protein(PDrunName, HorL = "L") %>%
select(Accession, Description, `Biological Process`, `Biological Process`, `Molecular Function`, WikiPathways, `Gene ID`)
proteinlist_H = read_protein(PDrunName, HorL = "H") %>%
select(Accession, Description, `Biological Process`, `Biological Process`, `Molecular Function`, WikiPathways, `Gene ID`)
proteinlist = full_join(proteinlist_L, proteinlist_H, by = "Accession")
rm(proteinlist_L, proteinlist_H)
Chip4_dif_output = left_join(Chip4_dif,proteinlist, by = c("Protein" = "Accession") )
write_tsv(Chip4_dif_output, "Protein_Carrier_sc_Chip4_different.txt")
only_A549 = proteinlist[which(proteinlist$Accession %in% A549_special),]
only_HFL1 = proteinlist[which(proteinlist$Accession %in% HFL1_special),]
write_tsv(only_A549, "Chip4_only_A549.txt")
write_tsv(only_HFL1, "Chip4_only_HFL1.txt")
library(ggrepel)
ggplot(data = Chip4_dif, aes(x = difference, y = nlogpadjust, col = diffexpressed, label = label))+
geom_point()+
theme_bw(base_size = 18) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())+
geom_vline(xintercept=c(-0.6, 0.6), col="red") +
geom_hline(yintercept= -log10(0.05), col="red")+
scale_color_manual(values=c("blue", "black", "red"))+
geom_text_repel() +
rremove("legend") +
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = rel(1.1), color = "black"),
axis.text.y = element_text(angle = 0, hjust = 1,size = rel(1.1), color = "black"),
axis.title.x = element_text(angle = 0, vjust = 0.5,size = rel(1.1), color = "black"),
axis.title.y = element_text(angle = 90, hjust = 0.5,size = rel(1.1), color = "black"))+
xlab("Log2(fold change A549/HFL1)") + ylab("-log(p-value)")
ggsave(filename = paste0(figure_save, "Chip4Volcano_carrier_BHadjust.png"), width = 7, height = 5, dpi = 500)
#ggsave(filename = paste0(figure_save,"Chip4Volcano_noLabel.png"), width = 5, height = 4, dpi = 500)
### Protein and peptide ID from Protein file ###########################################################
# Protein ID identified:
get_ID = function(PDrunName, cell.names){
#generate a list of files:
file_name_L = read_file_name(PDrunName,HorL = "L")
file_name_H = read_file_name(PDrunName,HorL = "H")
file_name = rbind(file_name_L, file_name_H)
rm(file_name_L, file_name_H)
fileIDs = unique(file_name$FileID_SILAC)
cells_in_channel = data.frame()
for (file in fileIDs){
row = which(file_name$FileID_SILAC == file)
field = file_name[[row, "field"]]
chip = file_name[[row, "chip"]]
fileID = file_name[[row, "File ID"]]
cell_in_channel = read_channel(channel_assign, field, file) %>%
dplyr::mutate(FileID_SILAC = file, FileID = fileID, Rawfile = file_name[[row, "RawFile"]],
Gradient = file_name[[row, "Gradient"]],
Field = as.numeric(str_replace(Location,"F",""))) %>%
dplyr::mutate(Carrier = ifelse(Field <= 21, "10 ng", "None")) %>%
dplyr::select(FileID, FileID_SILAC, Carrier, Rawfile, Gradient, starts_with("1"))
if (str_detect(chip,"Chip1|Chip2")){
cell_in_channel = actual_cell(cell_in_channel, "HeLa", "K562")
}
else if(chip == "Chip3"){
cell_in_channel = actual_cell(cell_in_channel, "HeLa", "A549")
}
else if(chip == "Chip4"){
cell_in_channel = actual_cell(cell_in_channel, "HFL1", "A549")
}
cells_in_channel = rbind(cells_in_channel, cell_in_channel)
}
cells_in_channel_longer = cells_in_channel %>%
pivot_longer(cols = starts_with("x1"), names_to = "Channel", values_to = "Cell")%>%
dplyr::mutate(Identifier = paste0(FileID_SILAC, "_", Channel), Raw = Rawfile) %>%
separate(Raw, into=c("Chip","left","Field"), sep = "_") %>%
select(FileID_SILAC,Carrier, Rawfile, Gradient,Channel,Cell,Identifier, Chip)
#read protein ID identified:
proteinL = read_protein(PDrunName, HorL = "L")
proteinH = read_protein(PDrunName, HorL = "H")
ProteinL_ID = proteinL %>%
dplyr::select(Accession, str_subset(colnames(proteinL), "ID_"))
ProteinH_ID = proteinH %>%
dplyr::select(Accession, str_subset(colnames(proteinH), "ID_"))
Protein_ID = dplyr::full_join(ProteinL_ID, ProteinH_ID, by = "Accession")
rm(ProteinL_ID, ProteinH_ID)
proteinID = data.frame()
i = 1
for(identifier in colnames(Protein_ID[,-1])){
proteinID[i, "Identifier"] = str_replace(identifier, "ID_", "")
proteinID[i, "Protein_ID"] = sum(Protein_ID[,which(colnames(Protein_ID) == identifier)] == "High", na.rm = TRUE)
i = i + 1
}
#read peptide ID identified:
peptideL = read_peptide(PDrunName, HorL = "L")
peptideH = read_peptide(PDrunName, HorL = "H")
peptideL_ID = peptideL %>%
dplyr::select(Annotated_Sequence, str_subset(colnames(peptideL), "ID_"))
peptideH_ID = peptideH %>%
dplyr::select(Annotated_Sequence, str_subset(colnames(peptideH), "ID_"))
peptide_ID = dplyr::full_join(peptideL_ID, peptideH_ID, by = "Annotated_Sequence")
rm(peptideL_ID, peptideH_ID)
peptideID = data.frame()
i = 1
for(identifier in colnames(peptide_ID[,-1])){
peptideID[i, "Identifier"] = str_replace(identifier, "ID_", "")
peptideID[i, "Peptide_ID"] = sum(peptide_ID[,which(colnames(peptide_ID) == identifier)] == "High", na.rm = TRUE)
i = i + 1
}
Identifications = dplyr::full_join(cells_in_channel_longer, proteinID, by = "Identifier")
Identifications = dplyr::full_join(Identifications, peptideID, by = "Identifier")
rm(peptide_ID, Protein_ID, peptideID, proteinID, identifier, i)
# select cells
Identifications = Identifications %>%
dplyr::filter(str_detect(Cell, cell.names))%>%
mutate(Carrier_gradient = paste0(Carrier, ", ", Gradient))
return(Identifications)
}
#combine 60 and 90 min
Identifications_90 = get_ID(PDrunName = "ExMloading_90min", cell.names = "HeLa|K562|A549")#|HFL1
Identifications_60 = get_ID(PDrunName = "ExMloading_60min", cell.names = "HeLa|K562|A549")#|HFL1
Identifications = rbind(Identifications_90, Identifications_60)
# plot protein and peptide identifications for different gradient
ggplot(data = Identifications, aes(x = Cell, y = Protein_ID, fill = Carrier_gradient))+
geom_boxplot(width = 0.3, alpha = 0.5, na.rm = TRUE)+
xlab("Cell Type")+
ylab("Identified Protein Groups")+
theme_bw(base_size = 18) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),
axis.text.x = element_text(angle = 0, hjust = 0.5, size = rel(1), color = "black"),
axis.text.y = element_text(angle = 0, hjust = 1,size = rel(1), color = "black"),
axis.title.x = element_text(angle = 0, vjust = 0.5,size = rel(1.1), color = "black"),
axis.title.y = element_text(angle = 90, hjust = 0.5,size = rel(1.1), color = "black"),
legend.position = c(0.75, 0.4),
legend.background = element_rect(fill = "transparent"),
legend.title = element_blank(),
legend.text = element_text(size = 17))
# + geom_jitter(color="black", size=0.4, alpha=0.9)
ggsave(filename = paste0(figure_save, "Protein ID 60 and 90 min.png"),
width = 5, height = 5, dpi = 500)
ggplot(data = Identifications, aes(x = Cell, y = Peptide_ID, fill = Carrier_gradient))+
#geom_violin(na.rm = TRUE)+
geom_boxplot(width = 0.3, alpha = 0.5, na.rm = TRUE)+
xlab("Cell Type")+
ylab("Identified Peptide Groups")+
theme_bw(base_size = 18) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(), legend.position = "top",
axis.text.x = element_text(angle = 0, hjust = 0.5,size = rel(1.05), color = "black"),
axis.text.y = element_text(size = 16),
legend.title = element_text(size = 16),
legend.text = element_text(size = 14)) +
guides(fill=guide_legend(title=""))
# +geom_jitter(color="black", size=0.4, alpha=0.9)
ggsave(filename = paste0(figure_save, "Pepetide ID2.png"),
width = 6, height = 5, dpi = 500)