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Figure3.R
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Figure3.R
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library(tidyverse)
library(data.table)
library(ComplexHeatmap)
library(ggrepel)
library(openxlsx)
library(protti)
########################
### ID numbers plot
#######################
### Missing values estimate
missing_summarize = function(report_data, method){
col_num = ncol(report_data)
report_data_gather = gather(report_data, raw_files, quant, (col_num-39):col_num)
report_data_group = report_data_gather %>%
filter(!is.na(quant)) %>%
group_by(V1) %>%
summarise(n=n()) %>%
ungroup()
report_data_group_out_25 = report_data_group %>%
filter(n >= 10)
report_data_group_out_50 = report_data_group %>%
filter(n >= 20)
report_data_group_out_100 = report_data_group %>%
filter(n == 40)
report_data_group_out_25_pure = report_data_group_out_25 %>%
filter(!V1 %in% report_data_group_out_50$V1)
report_data_group_out_50_pure = report_data_group_out_50 %>%
filter(!V1 %in% report_data_group_out_100$V1)
report_data_group_out_other = report_data_group %>%
filter(!V1 %in% report_data_group_out_25$V1)
id_portion = c("100%", ">50%", ">25%", "<25%")
id_num = c(nrow(report_data_group_out_100), nrow(report_data_group_out_50_pure), nrow(report_data_group_out_25_pure), nrow(report_data_group_out_other))
out_data = data_frame(id_portion, id_num)
out_data$method = method
return(out_data)
}
read_maxlfq = function(file_path){
out_data = fread(file_path) %>%
filter(V1 != "")
}
# a. missing value comp with paper
plasma_diann_try_pro_processed = read_maxlfq("./plasmaData/DIA_NN_LF_result/protein_maxlfq.tsv")
plasma_dia_try_pro_processed = read_maxlfq("./plasmaData/DIA_lib_try_result/diann-output/protein_maxlfq.tsv")
plasma_dia_semi_pro_processed = read_maxlfq("./plasmaData/DIA_lib_semi_result/diann-output/protein_maxlfq.tsv")
plasma_diann_try_pro_processed_miss = missing_summarize(plasma_diann_try_pro_processed, "DIA-NN\nlib-free(tryptic)")
plasma_dia_try_pro_processed_miss = missing_summarize(plasma_dia_try_pro_processed, "FP-diaTracer\n(tryptic)")
plasma_dia_semi_pro_processed_miss = missing_summarize(plasma_dia_semi_pro_processed, "FP-diaTracer\n(semi-tryptic)")
plasma_num_pro_miss = bind_rows(plasma_diann_try_pro_processed_miss) %>%
bind_rows(plasma_dia_try_pro_processed_miss) %>%
bind_rows(plasma_dia_semi_pro_processed_miss)
plasma_num_pro_miss$small_id_num = plasma_num_pro_miss$id_num/1000
plasma_num_pro_miss$id_portion = factor(plasma_num_pro_miss$id_portion, levels = c("<25%", ">25%", ">50%", "100%" ), ordered = TRUE)
plasma_num_pro_miss$method = factor(plasma_num_pro_miss$method, levels = c("FP-diaTracer\n(tryptic)", "FP-diaTracer\n(semi-tryptic)", "DIA-NN\nlib-free(tryptic)"), ordered = TRUE)
plasma_num_pro_miss_plot = ggplot(plasma_num_pro_miss, aes(x=method, y=small_id_num, fill= id_portion)) +
geom_bar(stat="identity", color="black", size=0.05, width = 0.8) +
scale_fill_brewer(palette="Blues") +
scale_y_continuous(expand = c(0.01, 0)) +
ylab("# Proteins (x1000)") +
xlab("Method") +
theme_light() +
labs(fill= "Non-missing\nvalue filter") +
#theme(legend.position="none") +
#annotate(geom="text", x=0.8, y=4, label="x 1000", size = 2) +
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=0.5, size = 5),
axis.text.y = element_text(size = 5),
axis.title = element_text(size = 5),
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black", size = 0.05),
legend.position = "right",
legend.title = element_text(size=4, face="bold"),
legend.text = element_text(size = 4),
legend.key.size = unit(0.15, "cm"),
legend.margin = margin(-10, 2, 0, -10))
plasma_num_pro_miss_plot
plasma_num_pro_miss_sum = plasma_num_pro_miss %>%
group_by(method) %>%
summarise(n=sum(id_num))
plasma_dia_try_pre_processed = read_maxlfq("./plasmaData/DIA_lib_try_result/diann-output/precursor_maxlfq.tsv")
plasma_dia_semi_pre_processed = read_maxlfq("./plasmaData/DIA_lib_semi_result/diann-output/precursor_maxlfq.tsv")
plasma_diann_try_pre_processed = read_maxlfq("./plasmaData/DIA_NN_LF_result/precursor_maxlfq.tsv")
plasma_diann_try_pre_processed_miss = missing_summarize(plasma_diann_try_pre_processed, "DIA-NN\nlib-free(tryptic)")
plasma_dia_try_pre_processed_miss = missing_summarize(plasma_dia_try_pre_processed, "FP-diaTracer\n(tryptic)")
plasma_dia_semi_pre_processed_miss = missing_summarize(plasma_dia_semi_pre_processed, "FP-diaTracer\n(semi-tryptic)")
plasma_num_pre_miss =
bind_rows(plasma_diann_try_pre_processed_miss) %>%
bind_rows(plasma_dia_try_pre_processed_miss) %>%
bind_rows(plasma_dia_semi_pre_processed_miss)
plasma_num_pre_miss$small_id_num = plasma_num_pre_miss$id_num/1000
plasma_num_pre_miss$id_portion = factor(plasma_num_pre_miss$id_portion, levels = c("<25%", ">25%", ">50%", "100%" ), ordered = TRUE)
plasma_num_pre_miss$method = factor(plasma_num_pre_miss$method, levels = c("FP-diaTracer\n(tryptic)", "FP-diaTracer\n(semi-tryptic)", "DIA-NN\nlib-free(tryptic)"), ordered = TRUE)
plasma_num_pre_miss_plot = ggplot(plasma_num_pre_miss, aes(x=method, y=small_id_num, fill= id_portion)) +
geom_bar(stat="identity", color="black", size=0.05, width = 0.8) +
scale_fill_brewer(palette="Blues", name="Portion") +
scale_y_continuous(expand = c(0.01, 0)) +
ylab("# Precursors (x1000)") +
xlab("Method") +
theme_light() +
labs(fill= "Non-missing\nvalue filter") +
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=0.5, size = 5),
axis.text.y = element_text(size = 5),
axis.title = element_text(size = 5),
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black", size = 0.05),
legend.position = "right",
legend.title = element_text(size=4, face="bold"),
legend.text = element_text(size = 4),
legend.key.size = unit(0.15, "cm"),
legend.margin = margin(-10, 2, 0, -10))
plasma_num_pre_miss_plot
plasma_num_pre_miss_sum = plasma_num_pre_miss %>%
group_by(method) %>%
summarise(n=sum(id_num))
# gene expression
plasma_try_de_result = fread("./plasmaData/DIA_lib_try_result/frapipe_analyst/DE_results.csv")
plasma_semi_de_result = fread("./plasmaData/DIA_lib_semi_result/fragpipe_analyst/DE_results.csv")
plasma_de_result = list("Tryptic" = plasma_try_de_result, "Semi-Tryptic" = plasma_semi_de_result)
write.xlsx(plasma_de_result, file = './supplements/table_s3.xlsx')
plasma_try_de_result_sig = plasma_try_de_result %>%
filter(significant == TRUE)
plasma_semi_de_result_sig = plasma_semi_de_result %>%
filter(significant == TRUE)
plasma_semi_de_result_sig_unique = plasma_semi_de_result_sig %>%
filter(!`Gene Name` %in% plasma_try_de_result_sig$`Gene Name`)
plasma_semi_de_result_cc = plasma_semi_de_result %>%
select(`Gene Name`, `cancer_vs_control_log2 fold change`, cancer_vs_control_p.adj)
colnames(plasma_semi_de_result_cc) = c("gene", "fold_change", "p_adj")
plasma_semi_de_result_cc$label_name = ifelse((abs(plasma_semi_de_result_cc$fold_change)>=1 & -log10(plasma_semi_de_result_cc$p_adj) >1.6), plasma_semi_de_result_cc$gene, NA)
plasma_semi_de_result_cc$sig = ifelse((abs(plasma_semi_de_result_cc$fold_change)>=0.6 & plasma_semi_de_result_cc$p_adj <0.05), "sig", "nsg")
plasma_semi_de_result_cc_volcano = ggplot(plasma_semi_de_result_cc, aes(x=fold_change, y=-log10(p_adj), color=sig)) +
geom_point(size=0.2) +
ylab("Adjusted P-value (-log10)") +
xlab("log2 Fold Change") +
scale_color_manual(values=c("#999999", "black")) +
geom_vline(xintercept = c(-0.6, 0.6), col = "gray", linetype = 'dashed') +
geom_hline(yintercept = -log10(0.05), col = "gray", linetype = 'dashed') +
geom_text_repel(label = plasma_semi_de_result_cc$label_name, max.overlaps = Inf, color="black", size=1.5, segment.color = 'transparent', box.padding = 0.05) +
theme_light() +
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=0.5, size = 5),
axis.text.y = element_text(size = 5),
axis.title = element_text(size = 5),
panel.grid.major = element_blank(),
panel.border = element_rect(colour = "black", size = 0.5),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black", size = 0.05),
legend.position = "none")
plasma_semi_de_result_cc_volcano
plasma_semi_de_result_up = plasma_semi_de_result %>%
filter(significant == TRUE) %>%
filter(`cancer_vs_control_log2 fold change` > 0)
plasma_semi_de_result_down = plasma_semi_de_result %>%
filter(significant == TRUE) %>%
filter(`cancer_vs_control_log2 fold change` < 0)
# feature
plasma_annotation_data = fread("./plasmaData/experiment_annotation.txt") %>%
select(sample_name, condition)
feature_data_try = plasma_dia_try_pro_processed %>%
filter(V1 %in% c("P16104", "P31751"))
feature_data_try$V1 = str_replace_all(feature_data_try$V1, "P31751", "AKT2")
feature_data_try$V1 = str_replace_all(feature_data_try$V1, "P16104", "H2AX")
feature_data_try_gather = gather(feature_data_try, sample_name, quant, 2:41)
feature_data_try_gather_con = inner_join(feature_data_try_gather, plasma_annotation_data, by="sample_name")
feature_data_try_gather_con$quant_log = log2(feature_data_try_gather_con$quant)
feature_data_try_gather_con$type = "Tryptic"
feature_data_semi = plasma_dia_semi_pro_processed %>%
filter(V1 %in% c("P16104", "P31751"))
feature_data_semi$V1 = str_replace_all(feature_data_semi$V1, "P31751", "AKT2")
feature_data_semi$V1 = str_replace_all(feature_data_semi$V1, "P16104", "H2AX")
feature_data_semi_gather = gather(feature_data_semi, sample_name, quant, 2:41)
feature_data_semi_gather_con = inner_join(feature_data_semi_gather, plasma_annotation_data, by="sample_name")
feature_data_semi_gather_con$quant_log = log2(feature_data_semi_gather_con$quant)
feature_data_semi_gather_con$type = "Semi-tryptic"
feature_data_all = bind_rows(feature_data_try_gather_con, feature_data_semi_gather_con)
plasma_feature_plot = ggplot(feature_data_all, aes(x=condition, y=quant_log, color=condition)) +
geom_boxplot(outlier.size = 0, size=0.1) +
geom_point(position=position_jitterdodge(0.1), size=0.1, alpha=0.6)+
scale_x_discrete(labels=c('NSCLC', 'Control')) +
scale_color_manual(values = c("#E54D37", "#5CBED3")) +
stat_compare_means(label = "p.signif", method = "t.test", label.x = 1.5 , label.y = 13, size=2)+
ylab("Protein Abun (log2)") +
xlab("Group") +
theme_light() +
theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=0.5, size = 5),
axis.text.y = element_text(size = 5),
axis.title = element_text(size = 5),
panel.grid.major = element_blank(),
panel.border = element_rect(colour = "black", size = 0.5),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black", size = 0.05),
legend.position = "none") +
theme(strip.text = element_text(size = 5, color = "black", margin = margin(b=1)),
strip.background = element_blank())+
facet_grid(V1~type)
plasma_feature_plot
plasma_plot = ggarrange(ggarrange(plasma_num_pro_miss_plot, plasma_num_pre_miss_plot, widths = c(1.5, 1.5),
ncol = 2, nrow = 1, align="h", labels = c("a", "b"), font.label = list(size = 10),
common.legend = T, legend = "right"),
ggarrange(plasma_semi_de_result_cc_volcano, plasma_feature_plot, widths = c(2, 1.5),
ncol = 2, nrow = 1, align="h", labels = c("c", "d"), font.label = list(size = 10)),
nrow = 2, ncol=1, heights = c(1.3,2.2))
plasma_plot
ggsave("./figures/Figure3.pdf", plasma_plot, width=5.3, height = 4, units = c("in"), dpi=400)
### Supplement
semi_precursor_de_report = fread("./revisionData/plasma/Precursor_DE_results.csv")
semi_precursor_de_report_AKT2 = semi_precursor_de_report %>%
filter(`Gene Name` %in% c("AKT2")) %>%
filter(num_NAs <36)
semi_precursor_de_report_H2AX = semi_precursor_de_report %>%
filter(`Gene Name` %in% c("H2AX")) %>%
filter(num_NAs <36)
semi_precursor_psm = fread("./plasmaData/DIA_lib_semi_result/psm.tsv")
semi_precursor_psm_used = semi_precursor_psm %>%
distinct(Peptide, `Protein Start`, `Protein End`, Gene, `Protein ID`, `Prev AA`)
semi_precursor_psm_used$Index = paste(semi_precursor_psm_used$`Protein ID`, semi_precursor_psm_used$Peptide, sep = "_")
semi_precursor_psm_used$semi = ifelse(semi_precursor_psm_used$`Prev AA` %in% c("R", "K"), F, T)
semi_precursor_de_report_AKT2_index = left_join(semi_precursor_de_report_AKT2, semi_precursor_psm_used, by = "Index")
semi_precursor_de_report_AKT2_index$protein_length = max(semi_precursor_de_report_AKT2_index$`Protein End`) + 20
semi_precursor_de_report_H2AX_index = left_join(semi_precursor_de_report_H2AX, semi_precursor_psm_used, by = "Index")
semi_precursor_de_report_H2AX_index$protein_length = max(semi_precursor_de_report_H2AX_index$`Protein End`) + 20
colnames(semi_precursor_de_report_H2AX_index)[5] = "p-value"
### Add the wood's plot reference
semi_precursor_de_report_H2AX_woods = woods_plot(
data = semi_precursor_de_report_H2AX_index,
fold_change = `cancer_vs_control_log2 fold change`,
start_position = `Protein Start`,
end_position = `Protein End`,
protein_length = protein_length,
protein_id = Gene,
colouring = `p-value`,
highlight = semi
)
pdf("./supplements/FigureS4.pdf", width=7, height = 5)
semi_precursor_de_report_H2AX_woods
dev.off()
## Manually re-assign the star position
#### Peptide level comparison
plasma_try_psm_table = fread("./plasmaData/DIA_lib_try_result/psm.tsv")
plasma_semi_psm_table = fread("./plasmaData/DIA_lib_semi_result/psm.tsv")
plasma_try_psm_table_pep = plasma_try_psm_table %>%
distinct(Peptide)
plasma_semi_psm_table_pep = plasma_semi_psm_table %>%
distinct(Peptide)
plasma_try_semi_psm_table_pep_over = plot(euler(list(tryptic = plasma_try_psm_table_pep$Peptide,
`semi-\ntryptic` = plasma_semi_psm_table_pep$Peptide),
labels = list(labels=c("tryptic", "semi-tryptic"), fontsize=4),
quantities = list(fontsize = 5),
fills = c("#2270B5", "#9ECAE1")), quantities = TRUE)
pdf("./supplements/FigureS3.pdf", width=7, height = 5)
plasma_try_semi_psm_table_pep_over
dev.off()
### Protein level
plasma_semi_psm_table_pep_full = plasma_semi_psm_table %>%
distinct(Peptide, `Prev AA`, `Next AA`, `Protein ID`)
plasma_semi_psm_table_pep_full_unique = plasma_semi_psm_table_pep_full %>%
filter(!Peptide %in% plasma_try_psm_table_pep$Peptide)
plasma_semi_psm_table_pep_full_unique_semi_nterm = plasma_semi_psm_table_pep_full_unique %>%
filter(!`Prev AA` %in% c("K", "R", "-"))
plasma_semi_psm_table_pep_full_unique_semi_cterm = plasma_semi_psm_table_pep_full_unique %>%
filter(!`Next AA` %in% c("-")) %>%
filter(!str_ends(Peptide, "K")) %>%
filter(!str_ends(Peptide, "R"))
plasma_semi_psm_table_pep_full_unique_semi = rbind(plasma_semi_psm_table_pep_full_unique_semi_nterm, plasma_semi_psm_table_pep_full_unique_semi_cterm)
plasma_semi_psm_table_pep_full_unique_semi_pro_num = plasma_semi_psm_table_pep_full_unique_semi %>%
group_by(`Protein ID`) %>%
summarise(semi_num=n())
plasma_try_de_result_semi_sig = inner_join(plasma_try_de_result, plasma_semi_psm_table_pep_full_unique_semi_pro_num, by="Protein ID") %>%
filter(significant) %>%
select(`Protein ID`, `cancer_vs_control_log2 fold change`, semi_num)
colnames(plasma_try_de_result_semi_sig)[2] = "tryptic"
plasma_semi_de_result_semi_sig = inner_join(plasma_semi_de_result, plasma_semi_psm_table_pep_full_unique_semi_pro_num, by="Protein ID") %>%
filter(significant)%>%
select(`Protein ID`, `cancer_vs_control_log2 fold change`)
colnames(plasma_semi_de_result_semi_sig)[2] = "semi-tryptic"
plasma_def_sig = inner_join(plasma_try_de_result_semi_sig, plasma_semi_de_result_semi_sig, by="Protein ID")
plasma_def_sig$fc_diff = abs(plasma_def_sig$`semi-tryptic`)-abs(plasma_def_sig$tryptic)
plasma_def_sig$type = ifelse(plasma_def_sig$fc_diff>0, "increase", "decrease")
plasma_def_sig_gather = gather(plasma_def_sig, key = "cleavage", value = "fc", c(2,4))
plasma_fc_plot = ggplot() +
geom_point(data=plasma_def_sig_gather, aes(x=`Protein ID`, y=abs(fc), color=cleavage)) +
geom_bar(data=plasma_def_sig, aes(x=`Protein ID`, y=fc_diff, fill=type), stat="identity", width=0.75) +
theme_light() +
ylab("Absolute Fold Change")+
scale_fill_manual(values = c("#1F77B4", "#FF7F0E")) +
scale_color_manual(values = c("#1F77B4", "#FF7F0E")) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1, size = 10),
axis.text.y = element_text(size = 10),
axis.title = element_text(size = 10),
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black", size = 0.05),
legend.position = "right",
legend.title = element_text(size=10, face="bold"),
legend.text = element_text(size = 10),
legend.key.size = unit(0.2, "cm"))
plasma_fc_plot
ggsave("./supplements/FigureS5.pdf", plasma_fc_plot, width=10, height = 4, units = c("in"), dpi=400)