R/0_exec_s02_comparison_sequencing_depth.R
In ndbrown6/MSK-GRAIL-TECHVAL: High-intensity sequencing reveals the sources of plasma circulating cell-free DNA variants
#==================================================
# David Brown
# brownd7@mskcc.org
#==================================================
rm(list=ls(all=TRUE))
source('config.R')
if (!dir.exists("../res/figureS2")) {
dir.create("../res/figureS2")
}
if (!dir.exists("../res/etc/Source_Data_Extended_Data_Fig_2")) {
dir.create("../res/etc/Source_Data_Extended_Data_Fig_2")
}
#==================================================
# qc metrics table
#==================================================
tracker_grail = read_csv(file=patient_tracker, col_types = cols(.default = col_character())) %>%
type_convert()
tracker_impact = read_csv(impact_tracker, col_types = cols(.default = col_character())) %>%
type_convert()
valid_patient_ids = tracker_grail %>%
filter(patient_id %in% tracker_impact$patient_id | tissue == "Healthy") %>%
filter(!(tissue %in% c("Breast", "Lung", "Prostate") & study=="Merlin")) %>%
.[["patient_id"]]
clinical = read_tsv(clinical_file, col_types = cols(.default = col_character())) %>%
type_convert() %>%
rename(localisation = tissue)
valid_patient_ids = intersect(valid_patient_ids, clinical$patient_id)
qc_metrics_cfdna = read.csv(file=url_qc.metrics, header=TRUE, sep="\t", stringsAsFactors=FALSE) %>%
dplyr::select(sample_id, patient_id, sample_type, tissue, volume_of_blood_mL, volume_of_DNA_source_mL, DNA_extraction_yield_ng, DNA_input_concentration_ng_uL, Library_preparation_input_ng, raw.MEAN_BAIT_COVERAGE, collapsed.MEAN_BAIT_COVERAGE, collapsed_fragment.MEAN_BAIT_COVERAGE, readErrorRate, readSubstErrorRate, Study) %>%
filter(sample_type=="cfDNA")
tracker_grail_cfdna = read.csv(file=patient_tracker, header=TRUE, sep=",", stringsAsFactors=FALSE) %>%
dplyr::select(patient_id, cfdna_sample_id) %>%
rename(msk_id = patient_id, sample_id = cfdna_sample_id)
qc_metrics_cfdna = left_join(qc_metrics_cfdna, tracker_grail_cfdna, by="sample_id")
qc_metrics_wbc = read.csv(file=url_qc.metrics, header=TRUE, sep="\t", stringsAsFactors=FALSE) %>%
dplyr::select(sample_id, patient_id, sample_type, tissue, volume_of_blood_mL, volume_of_DNA_source_mL, DNA_extraction_yield_ng, DNA_input_concentration_ng_uL, Library_preparation_input_ng, raw.MEAN_BAIT_COVERAGE, collapsed.MEAN_BAIT_COVERAGE, collapsed_fragment.MEAN_BAIT_COVERAGE, readErrorRate, readSubstErrorRate, Study) %>%
filter(sample_type=="gDNA") %>%
mutate(sample_type = "WBC")
tracker_grail_wbc = read.csv(file=patient_tracker, header=TRUE, sep=",", stringsAsFactors=FALSE) %>%
dplyr::select(patient_id, gdna_sample_id) %>%
rename(msk_id = patient_id, sample_id = gdna_sample_id)
qc_metrics_wbc = left_join(qc_metrics_wbc, tracker_grail_wbc, by="sample_id")
qc_metrics = rbind(qc_metrics_cfdna, qc_metrics_wbc) %>%
filter(msk_id %in% valid_patient_ids) %>%
dplyr::select(-sample_id, -msk_id) %>%
rename(`Patient_ID` = `patient_id`,
`Sample_Type` = `sample_type`,
`Tissue` = `tissue`,
`Volume_of_blood_mL` = `volume_of_blood_mL`,
`Volume_of_DNA_source_mL` = `volume_of_DNA_source_mL`,
`Uncollapsed_Mean_Coverage` = `raw.MEAN_BAIT_COVERAGE`,
`Collapsed_Mean_Coverage` = `collapsed.MEAN_BAIT_COVERAGE`,
`Collapsed_Fragment_Mean_Coverage` = `collapsed_fragment.MEAN_BAIT_COVERAGE`,
`Indel_and_Substitution_Error_Rate` = `readErrorRate`,
`Substitution_Error_Rate` = `readSubstErrorRate`,
`Assay_Version` = `Study`) %>%
mutate(Assay_Version = ifelse(Assay_Version=="TechVal", "V1", "V2")) %>%
arrange(Patient_ID, Sample_Type) %>%
mutate(Library_preparation_input_ng = ifelse(Library_preparation_input_ng>75, 75, Library_preparation_input_ng))
export_x = qc_metrics
colnames(export_x) = tolower(colnames(export_x))
write_tsv(export_x, path="../res/etc/Source_Data_Extended_Data_Fig_2/Extended_Data_Fig_2.tsv", append=FALSE, col_names=TRUE)
#==================================================
# comparison of sequencing depth cfDNA and gDNA
# by tissue type
#==================================================
tmp.0 = qc_metrics %>%
mutate(Tissue = ifelse(Tissue=="Healthy", "Control", Tissue)) %>%
mutate(Tissue = factor(Tissue, levels=c("Breast","Lung","Prostate", "Control")))
p = list()
p[[1]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative.0 = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
p = list()
p[[1]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
p = list()
p[[1]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
plot.0 = ggplot(tmp.0, aes(x = Tissue, y = Uncollapsed_Mean_Coverage, fill = Tissue, alpha = Sample_Type, group = interaction(Tissue, Sample_Type))) +
geom_boxplot(outlier.size=2.5, outlier.shape=21) +
scale_fill_manual(values = cohort_cols) +
scale_alpha_manual(values = c("cfDNA" = 1, "WBC"=.65)) +
theme_classic(base_size=15) +
theme(axis.text.y = element_text(size=13),
axis.text.x = element_text(size=10),
legend.title=element_text(size=10, face="bold"),
legend.position = c(0.125, 0.775),
legend.background = element_blank(),
legend.key.size = unit(1, 'lines')) +
labs(x="", y=expression("Uncollapsed mean coverage (" %.% 10^3~")")) +
guides(fill=guide_legend(title=c("Cohort"))) +
guides(alpha=guide_legend(title=c("Assay"))) +
scale_y_continuous(
breaks=c(50000, 75000, 100000, 125000, 150000),
labels=c("50", "75", "100", "125", "---")
) +
coord_cartesian(ylim = c(30000,150000))
pdf(file="../res/figureS2/UNCOLLAPSED_MEAN_BAIT_COVERAGE_cfDNA_vs_gDNA_by_tissue.pdf", width=8, height=6)
print(plot.0)
dev.off()
tmp.1 = qc_metrics %>%
mutate(Tissue = ifelse(Tissue=="Healthy", "Control", Tissue)) %>%
mutate(Tissue = factor(Tissue, levels=c("Breast","Lung","Prostate", "Control")))
p = list()
p[[1]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
p = list()
p[[1]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
plot.0 = ggplot(tmp.1, aes(x = Sample_Type, y = Collapsed_Mean_Coverage, fill = Tissue, alpha = Sample_Type, group = interaction(Sample_Type, Tissue))) +
geom_boxplot(outlier.size=2.5, outlier.shape=21) +
scale_fill_manual(values = cohort_cols) +
scale_alpha_manual(values = c("cfDNA" = 1, "WBC"=.65)) +
theme_classic(base_size=15) +
theme(axis.text.y = element_text(size=13),
axis.text.x = element_text(size=10),
legend.title=element_text(size=10, face="bold"),
legend.position = c(1-0.125, 0.775),
legend.background = element_blank(),
legend.key.size = unit(1, 'lines')) +
labs(x="", y=expression("Collapsed mean coverage (" %.% 10^3~")")) +
scale_y_continuous(
breaks=c(1000, 3000, 5000, 7000, 9000, 10000),
labels=c("1", "3", "5", "7", "9", "---")
) +
guides(fill=guide_legend(title=c("Cohort"))) +
guides(alpha=guide_legend(title=c("Assay"))) +
coord_cartesian(ylim = c(500,11000))
pdf(file="../res/figureS2/COLLAPSED_MEAN_BAIT_COVERAGE_cfDNA_vs_gDNA_by_tissue.pdf", width=8, height=6)
print(plot.0)
dev.off()
#==================================================
# correlation of input DNA to library preparation
# with mean target coverage
#==================================================
tmp.2 = qc_metrics %>%
filter(Sample_Type=="cfDNA") %>%
mutate(Tissue = ifelse(Tissue=="Healthy", "Control", Tissue)) %>%
mutate(Tissue = factor(Tissue, levels=c("Breast","Lung","Prostate", "Control")))
fit = lm(Collapsed_Mean_Coverage ~ Library_preparation_input_ng, data=tmp.2)
sfit = summary(fit)
plot.0 = ggplot(tmp.2, aes(x = Library_preparation_input_ng, y = Collapsed_Mean_Coverage, fill = Tissue)) +
geom_smooth(method = "lm", aes(x = Library_preparation_input_ng, y = Collapsed_Mean_Coverage), inherit.aes=FALSE, se = TRUE, level=1-1e-12, color="goldenrod3") +
geom_point(alpha=1, shape=21, size=3.5) +
scale_fill_manual(values = cohort_cols) +
theme_classic(base_size=15) +
theme(axis.text.y = element_text(size=15),
axis.text.x = element_text(size=15),
legend.title=element_text(size=10, face="bold"),
legend.position = c(0.125, 0.85),
legend.background = element_blank(),
legend.key.size = unit(1, 'lines')) +
labs(x="\nInput DNA for library preparation (ng)\n", y=expression("Collapsed mean coverage (" %.% 10^3~")")) +
scale_y_continuous(
breaks=c(1000, 3000, 5000, 7000, 9000),
labels=c("1", "3", "5", "7", "---")
) +
coord_cartesian(xlim = c(10, 80)) +
guides(fill=guide_legend(title=c("Cohort"))) +
annotate(geom="text", x=13.5, y=5800, label=expression(R^2~" = ")) +
annotate(geom="text", x=21, y=5800, label=signif(sfit$r.squared, 3)) +
annotate(geom="text", x=13, y=5300, label=expression(p~" = ")) +
annotate(geom="text", x=23, y=5300, label=signif(sfit$coefficients[2,4], 3))
pdf(file="../res/figureS2/COLLAPSED_MEAN_BAIT_COVERAGE_cfDNA_vs_input_DNA.pdf", width=5.5, height=6)
print(plot.0)
dev.off()
#==================================================
# correlation of input DNA to library preparation
# with sample type
#==================================================
tmp.3 = qc_metrics %>%
filter(Sample_Type=="cfDNA") %>%
mutate(Tissue = ifelse(Tissue=="Healthy", "Control", Tissue)) %>%
mutate(Tissue = factor(Tissue, levels=c("Breast","Lung","Prostate", "Control")))
p = list()
p[[1]] = wilcox.test(tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Library_preparation_input_ng"]],
tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Library_preparation_input_ng"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Library_preparation_input_ng"]],
tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Library_preparation_input_ng"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Library_preparation_input_ng"]],
tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Library_preparation_input_ng"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Library_preparation_input_ng"]],
tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Library_preparation_input_ng"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Library_preparation_input_ng"]],
tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Library_preparation_input_ng"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Library_preparation_input_ng"]],
tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Library_preparation_input_ng"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
plot.0 = ggplot(tmp.3, aes(x = Tissue, y = Library_preparation_input_ng, fill = Tissue)) +
geom_boxplot(alpha=1, outlier.shape=21, outlier.size=3.5, width=.75) +
scale_fill_manual(values = cohort_cols) +
theme_classic(base_size=15) +
theme(axis.text.y = element_text(size=15), axis.text.x = element_text(size=12)) +
labs(x="", y="Input DNA for library preparation (ng)") +
scale_y_continuous(
breaks=c(0, 20, 40, 60, 80, 100),
labels=c("0", "20", "40", "60", "80", "---")
) +
coord_cartesian(ylim = c(-10, 130)) +
guides(fill=FALSE)
pdf(file="../res/figureS2/INPUT_cfDNA_by_Tissue.pdf", width=5.5, height=6)
print(plot.0)
dev.off()
#==================================================
# comparison of error rate
#==================================================
tmp.4 = qc_metrics %>%
mutate(Tissue = ifelse(Tissue=="Healthy", "Control", Tissue)) %>%
mutate(Tissue = factor(Tissue, levels=c("Breast","Lung","Prostate", "Control")))
p = list()
p[[1]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
p = list()
p[[1]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
plot.0 = ggplot(tmp.4, aes(x = Sample_Type, y = 1e5*Indel_and_Substitution_Error_Rate, fill = Tissue, alpha = Sample_Type, group=interaction(Sample_Type, Tissue))) +
geom_boxplot(outlier.size=2.5, outlier.shape=21) +
scale_fill_manual(values = cohort_cols) +
scale_alpha_manual(values = c("cfDNA" = 1, "WBC"=.65)) +
theme_classic(base_size=15) +
theme(axis.text.y = element_text(size=13),
axis.text.x = element_text(size=10),
legend.title=element_text(size=10, face="bold"),
legend.position = c(1-0.125, 0.775),
legend.background = element_blank(),
legend.key.size = unit(1, 'lines')) +
labs(x="", y=expression("% collapsed bases (" %.% 10^-5~")")) +
scale_y_continuous(
breaks=c(6, 8, 10),
labels=c("6", "8", "---")
) +
guides(fill=guide_legend(title=c("Cohort"))) +
guides(alpha=guide_legend(title=c("Assay"))) +
coord_cartesian(ylim = c(5,11))
pdf(file="../res/figureS2/COMBINED_ERROR_RATE_cfDNA_vs_gDNA_by_tissue.pdf", width=8, height=6)
print(plot.0)
dev.off()
tmp.5 = qc_metrics %>%
mutate(Tissue = ifelse(Tissue=="Healthy", "Control", Tissue)) %>%
mutate(Tissue = factor(Tissue, levels=c("Breast","Lung","Prostate", "Control")))
p = list()
p[[1]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
p = list()
p[[1]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
plot.0 = ggplot(tmp.5, aes(x = Sample_Type, y = 1e5*Substitution_Error_Rate, fill = Tissue, alpha = Sample_Type, group=interaction(Sample_Type, Tissue))) +
geom_boxplot(outlier.size=2.5, outlier.shape=21) +
scale_fill_manual(values = cohort_cols) +
scale_alpha_manual(values = c("cfDNA" = 1, "WBC"=.65)) +
theme_classic(base_size=15) +
theme(axis.text.y = element_text(size=13),
axis.text.x = element_text(size=10),
legend.title=element_text(size=10, face="bold"),
legend.position = c(1-0.125, 0.775),
legend.background = element_blank(),
legend.key.size = unit(1, 'lines')) +
labs(x="", y=expression("% collapsed bases (" %.% 10^-5~")")) +
scale_y_continuous(
breaks=c(2, 4, 6),
labels=c("2", "4", "---")
) +
guides(fill=guide_legend(title=c("Cohort"))) +
guides(alpha=guide_legend(title=c("Assay"))) +
coord_cartesian(ylim = c(.5,7))
pdf(file="../res/figureS2/SUBSTITUTION_ERROR_RATE_cfDNA_vs_gDNA_by_tissue.pdf", width=8, height=6)
print(plot.0)
dev.off()
ndbrown6/MSK-GRAIL-TECHVAL documentation built on March 29, 2020, 4:41 p.m.
R Package Documentation
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#==================================================
# David Brown
# brownd7@mskcc.org
#==================================================
rm(list=ls(all=TRUE))
source('config.R')
if (!dir.exists("../res/figureS2")) {
dir.create("../res/figureS2")
}
if (!dir.exists("../res/etc/Source_Data_Extended_Data_Fig_2")) {
dir.create("../res/etc/Source_Data_Extended_Data_Fig_2")
}
#==================================================
# qc metrics table
#==================================================
tracker_grail = read_csv(file=patient_tracker, col_types = cols(.default = col_character())) %>%
type_convert()
tracker_impact = read_csv(impact_tracker, col_types = cols(.default = col_character())) %>%
type_convert()
valid_patient_ids = tracker_grail %>%
filter(patient_id %in% tracker_impact$patient_id | tissue == "Healthy") %>%
filter(!(tissue %in% c("Breast", "Lung", "Prostate") & study=="Merlin")) %>%
.[["patient_id"]]
clinical = read_tsv(clinical_file, col_types = cols(.default = col_character())) %>%
type_convert() %>%
rename(localisation = tissue)
valid_patient_ids = intersect(valid_patient_ids, clinical$patient_id)
qc_metrics_cfdna = read.csv(file=url_qc.metrics, header=TRUE, sep="\t", stringsAsFactors=FALSE) %>%
dplyr::select(sample_id, patient_id, sample_type, tissue, volume_of_blood_mL, volume_of_DNA_source_mL, DNA_extraction_yield_ng, DNA_input_concentration_ng_uL, Library_preparation_input_ng, raw.MEAN_BAIT_COVERAGE, collapsed.MEAN_BAIT_COVERAGE, collapsed_fragment.MEAN_BAIT_COVERAGE, readErrorRate, readSubstErrorRate, Study) %>%
filter(sample_type=="cfDNA")
tracker_grail_cfdna = read.csv(file=patient_tracker, header=TRUE, sep=",", stringsAsFactors=FALSE) %>%
dplyr::select(patient_id, cfdna_sample_id) %>%
rename(msk_id = patient_id, sample_id = cfdna_sample_id)
qc_metrics_cfdna = left_join(qc_metrics_cfdna, tracker_grail_cfdna, by="sample_id")
qc_metrics_wbc = read.csv(file=url_qc.metrics, header=TRUE, sep="\t", stringsAsFactors=FALSE) %>%
dplyr::select(sample_id, patient_id, sample_type, tissue, volume_of_blood_mL, volume_of_DNA_source_mL, DNA_extraction_yield_ng, DNA_input_concentration_ng_uL, Library_preparation_input_ng, raw.MEAN_BAIT_COVERAGE, collapsed.MEAN_BAIT_COVERAGE, collapsed_fragment.MEAN_BAIT_COVERAGE, readErrorRate, readSubstErrorRate, Study) %>%
filter(sample_type=="gDNA") %>%
mutate(sample_type = "WBC")
tracker_grail_wbc = read.csv(file=patient_tracker, header=TRUE, sep=",", stringsAsFactors=FALSE) %>%
dplyr::select(patient_id, gdna_sample_id) %>%
rename(msk_id = patient_id, sample_id = gdna_sample_id)
qc_metrics_wbc = left_join(qc_metrics_wbc, tracker_grail_wbc, by="sample_id")
qc_metrics = rbind(qc_metrics_cfdna, qc_metrics_wbc) %>%
filter(msk_id %in% valid_patient_ids) %>%
dplyr::select(-sample_id, -msk_id) %>%
rename(`Patient_ID` = `patient_id`,
`Sample_Type` = `sample_type`,
`Tissue` = `tissue`,
`Volume_of_blood_mL` = `volume_of_blood_mL`,
`Volume_of_DNA_source_mL` = `volume_of_DNA_source_mL`,
`Uncollapsed_Mean_Coverage` = `raw.MEAN_BAIT_COVERAGE`,
`Collapsed_Mean_Coverage` = `collapsed.MEAN_BAIT_COVERAGE`,
`Collapsed_Fragment_Mean_Coverage` = `collapsed_fragment.MEAN_BAIT_COVERAGE`,
`Indel_and_Substitution_Error_Rate` = `readErrorRate`,
`Substitution_Error_Rate` = `readSubstErrorRate`,
`Assay_Version` = `Study`) %>%
mutate(Assay_Version = ifelse(Assay_Version=="TechVal", "V1", "V2")) %>%
arrange(Patient_ID, Sample_Type) %>%
mutate(Library_preparation_input_ng = ifelse(Library_preparation_input_ng>75, 75, Library_preparation_input_ng))
export_x = qc_metrics
colnames(export_x) = tolower(colnames(export_x))
write_tsv(export_x, path="../res/etc/Source_Data_Extended_Data_Fig_2/Extended_Data_Fig_2.tsv", append=FALSE, col_names=TRUE)
#==================================================
# comparison of sequencing depth cfDNA and gDNA
# by tissue type
#==================================================
tmp.0 = qc_metrics %>%
mutate(Tissue = ifelse(Tissue=="Healthy", "Control", Tissue)) %>%
mutate(Tissue = factor(Tissue, levels=c("Breast","Lung","Prostate", "Control")))
p = list()
p[[1]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative.0 = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
p = list()
p[[1]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
p = list()
p[[1]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.0 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
tmp.0 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Uncollapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
plot.0 = ggplot(tmp.0, aes(x = Tissue, y = Uncollapsed_Mean_Coverage, fill = Tissue, alpha = Sample_Type, group = interaction(Tissue, Sample_Type))) +
geom_boxplot(outlier.size=2.5, outlier.shape=21) +
scale_fill_manual(values = cohort_cols) +
scale_alpha_manual(values = c("cfDNA" = 1, "WBC"=.65)) +
theme_classic(base_size=15) +
theme(axis.text.y = element_text(size=13),
axis.text.x = element_text(size=10),
legend.title=element_text(size=10, face="bold"),
legend.position = c(0.125, 0.775),
legend.background = element_blank(),
legend.key.size = unit(1, 'lines')) +
labs(x="", y=expression("Uncollapsed mean coverage (" %.% 10^3~")")) +
guides(fill=guide_legend(title=c("Cohort"))) +
guides(alpha=guide_legend(title=c("Assay"))) +
scale_y_continuous(
breaks=c(50000, 75000, 100000, 125000, 150000),
labels=c("50", "75", "100", "125", "---")
) +
coord_cartesian(ylim = c(30000,150000))
pdf(file="../res/figureS2/UNCOLLAPSED_MEAN_BAIT_COVERAGE_cfDNA_vs_gDNA_by_tissue.pdf", width=8, height=6)
print(plot.0)
dev.off()
tmp.1 = qc_metrics %>%
mutate(Tissue = ifelse(Tissue=="Healthy", "Control", Tissue)) %>%
mutate(Tissue = factor(Tissue, levels=c("Breast","Lung","Prostate", "Control")))
p = list()
p[[1]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
p = list()
p[[1]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Collapsed_Mean_Coverage"]],
tmp.1 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Collapsed_Mean_Coverage"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
plot.0 = ggplot(tmp.1, aes(x = Sample_Type, y = Collapsed_Mean_Coverage, fill = Tissue, alpha = Sample_Type, group = interaction(Sample_Type, Tissue))) +
geom_boxplot(outlier.size=2.5, outlier.shape=21) +
scale_fill_manual(values = cohort_cols) +
scale_alpha_manual(values = c("cfDNA" = 1, "WBC"=.65)) +
theme_classic(base_size=15) +
theme(axis.text.y = element_text(size=13),
axis.text.x = element_text(size=10),
legend.title=element_text(size=10, face="bold"),
legend.position = c(1-0.125, 0.775),
legend.background = element_blank(),
legend.key.size = unit(1, 'lines')) +
labs(x="", y=expression("Collapsed mean coverage (" %.% 10^3~")")) +
scale_y_continuous(
breaks=c(1000, 3000, 5000, 7000, 9000, 10000),
labels=c("1", "3", "5", "7", "9", "---")
) +
guides(fill=guide_legend(title=c("Cohort"))) +
guides(alpha=guide_legend(title=c("Assay"))) +
coord_cartesian(ylim = c(500,11000))
pdf(file="../res/figureS2/COLLAPSED_MEAN_BAIT_COVERAGE_cfDNA_vs_gDNA_by_tissue.pdf", width=8, height=6)
print(plot.0)
dev.off()
#==================================================
# correlation of input DNA to library preparation
# with mean target coverage
#==================================================
tmp.2 = qc_metrics %>%
filter(Sample_Type=="cfDNA") %>%
mutate(Tissue = ifelse(Tissue=="Healthy", "Control", Tissue)) %>%
mutate(Tissue = factor(Tissue, levels=c("Breast","Lung","Prostate", "Control")))
fit = lm(Collapsed_Mean_Coverage ~ Library_preparation_input_ng, data=tmp.2)
sfit = summary(fit)
plot.0 = ggplot(tmp.2, aes(x = Library_preparation_input_ng, y = Collapsed_Mean_Coverage, fill = Tissue)) +
geom_smooth(method = "lm", aes(x = Library_preparation_input_ng, y = Collapsed_Mean_Coverage), inherit.aes=FALSE, se = TRUE, level=1-1e-12, color="goldenrod3") +
geom_point(alpha=1, shape=21, size=3.5) +
scale_fill_manual(values = cohort_cols) +
theme_classic(base_size=15) +
theme(axis.text.y = element_text(size=15),
axis.text.x = element_text(size=15),
legend.title=element_text(size=10, face="bold"),
legend.position = c(0.125, 0.85),
legend.background = element_blank(),
legend.key.size = unit(1, 'lines')) +
labs(x="\nInput DNA for library preparation (ng)\n", y=expression("Collapsed mean coverage (" %.% 10^3~")")) +
scale_y_continuous(
breaks=c(1000, 3000, 5000, 7000, 9000),
labels=c("1", "3", "5", "7", "---")
) +
coord_cartesian(xlim = c(10, 80)) +
guides(fill=guide_legend(title=c("Cohort"))) +
annotate(geom="text", x=13.5, y=5800, label=expression(R^2~" = ")) +
annotate(geom="text", x=21, y=5800, label=signif(sfit$r.squared, 3)) +
annotate(geom="text", x=13, y=5300, label=expression(p~" = ")) +
annotate(geom="text", x=23, y=5300, label=signif(sfit$coefficients[2,4], 3))
pdf(file="../res/figureS2/COLLAPSED_MEAN_BAIT_COVERAGE_cfDNA_vs_input_DNA.pdf", width=5.5, height=6)
print(plot.0)
dev.off()
#==================================================
# correlation of input DNA to library preparation
# with sample type
#==================================================
tmp.3 = qc_metrics %>%
filter(Sample_Type=="cfDNA") %>%
mutate(Tissue = ifelse(Tissue=="Healthy", "Control", Tissue)) %>%
mutate(Tissue = factor(Tissue, levels=c("Breast","Lung","Prostate", "Control")))
p = list()
p[[1]] = wilcox.test(tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Library_preparation_input_ng"]],
tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Library_preparation_input_ng"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Library_preparation_input_ng"]],
tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Library_preparation_input_ng"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Library_preparation_input_ng"]],
tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Library_preparation_input_ng"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Library_preparation_input_ng"]],
tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Library_preparation_input_ng"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Library_preparation_input_ng"]],
tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Library_preparation_input_ng"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Library_preparation_input_ng"]],
tmp.3 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Library_preparation_input_ng"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
plot.0 = ggplot(tmp.3, aes(x = Tissue, y = Library_preparation_input_ng, fill = Tissue)) +
geom_boxplot(alpha=1, outlier.shape=21, outlier.size=3.5, width=.75) +
scale_fill_manual(values = cohort_cols) +
theme_classic(base_size=15) +
theme(axis.text.y = element_text(size=15), axis.text.x = element_text(size=12)) +
labs(x="", y="Input DNA for library preparation (ng)") +
scale_y_continuous(
breaks=c(0, 20, 40, 60, 80, 100),
labels=c("0", "20", "40", "60", "80", "---")
) +
coord_cartesian(ylim = c(-10, 130)) +
guides(fill=FALSE)
pdf(file="../res/figureS2/INPUT_cfDNA_by_Tissue.pdf", width=5.5, height=6)
print(plot.0)
dev.off()
#==================================================
# comparison of error rate
#==================================================
tmp.4 = qc_metrics %>%
mutate(Tissue = ifelse(Tissue=="Healthy", "Control", Tissue)) %>%
mutate(Tissue = factor(Tissue, levels=c("Breast","Lung","Prostate", "Control")))
p = list()
p[[1]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
p = list()
p[[1]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Indel_and_Substitution_Error_Rate"]],
tmp.4 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Indel_and_Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
plot.0 = ggplot(tmp.4, aes(x = Sample_Type, y = 1e5*Indel_and_Substitution_Error_Rate, fill = Tissue, alpha = Sample_Type, group=interaction(Sample_Type, Tissue))) +
geom_boxplot(outlier.size=2.5, outlier.shape=21) +
scale_fill_manual(values = cohort_cols) +
scale_alpha_manual(values = c("cfDNA" = 1, "WBC"=.65)) +
theme_classic(base_size=15) +
theme(axis.text.y = element_text(size=13),
axis.text.x = element_text(size=10),
legend.title=element_text(size=10, face="bold"),
legend.position = c(1-0.125, 0.775),
legend.background = element_blank(),
legend.key.size = unit(1, 'lines')) +
labs(x="", y=expression("% collapsed bases (" %.% 10^-5~")")) +
scale_y_continuous(
breaks=c(6, 8, 10),
labels=c("6", "8", "---")
) +
guides(fill=guide_legend(title=c("Cohort"))) +
guides(alpha=guide_legend(title=c("Assay"))) +
coord_cartesian(ylim = c(5,11))
pdf(file="../res/figureS2/COMBINED_ERROR_RATE_cfDNA_vs_gDNA_by_tissue.pdf", width=8, height=6)
print(plot.0)
dev.off()
tmp.5 = qc_metrics %>%
mutate(Tissue = ifelse(Tissue=="Healthy", "Control", Tissue)) %>%
mutate(Tissue = factor(Tissue, levels=c("Breast","Lung","Prostate", "Control")))
p = list()
p[[1]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Breast") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Lung") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Prostate") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="cfDNA" & Tissue=="Control") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
p = list()
p[[1]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[2]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[3]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Breast") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[4]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[5]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Lung") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p[[6]] = wilcox.test(tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Prostate") %>% .[["Substitution_Error_Rate"]],
tmp.5 %>% filter(Sample_Type=="WBC" & Tissue=="Control") %>% .[["Substitution_Error_Rate"]],
alternative = "two.sided", correct = FALSE)$p.value
p = p.adjust(unlist(p), "bonferroni")
plot.0 = ggplot(tmp.5, aes(x = Sample_Type, y = 1e5*Substitution_Error_Rate, fill = Tissue, alpha = Sample_Type, group=interaction(Sample_Type, Tissue))) +
geom_boxplot(outlier.size=2.5, outlier.shape=21) +
scale_fill_manual(values = cohort_cols) +
scale_alpha_manual(values = c("cfDNA" = 1, "WBC"=.65)) +
theme_classic(base_size=15) +
theme(axis.text.y = element_text(size=13),
axis.text.x = element_text(size=10),
legend.title=element_text(size=10, face="bold"),
legend.position = c(1-0.125, 0.775),
legend.background = element_blank(),
legend.key.size = unit(1, 'lines')) +
labs(x="", y=expression("% collapsed bases (" %.% 10^-5~")")) +
scale_y_continuous(
breaks=c(2, 4, 6),
labels=c("2", "4", "---")
) +
guides(fill=guide_legend(title=c("Cohort"))) +
guides(alpha=guide_legend(title=c("Assay"))) +
coord_cartesian(ylim = c(.5,7))
pdf(file="../res/figureS2/SUBSTITUTION_ERROR_RATE_cfDNA_vs_gDNA_by_tissue.pdf", width=8, height=6)
print(plot.0)
dev.off()
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