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MScProject.Rmd
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---
title: "MSc_Project.Rmd"
author: "Liam Porter"
date: "`r Sys.Date()`"
output: html_document
---
```{r}
# MUTATION RATE AND EXPECTED MUTATIONS IN PON REGION
```
C509.TCGA-78-7159-01A-11D-2036-08.1_gdc_realn.bam
sum=4391333584
totalNumLoci=72196718
Average_depth_of_coverage = 60.8246
C509.TCGA-78-7160-01A-11D-2036-08.1_gdc_realn.bam
sum=4297634866
totalNumLoci=72804080
Average depth of coverage = 59.0301
C509.TCGA-78-7161-01A-11D-2036-08.1_gdc_realn.bam
sum=4400886315
totalNumLoci=72718476
Average depth of coverage = 60.5195
C509.TCGA-78-7162-01A-21D-2063-08.1_gdc_realn.bam
sum=5610805155
totalNumLoci=78071707
Average depth of coverage = 71.8673
C509.TCGA-78-7163-01A-12D-2063-08.1_gdc_realn.bam
sum=5077875345
totalNumLoci=75392622
Average depth of coverage = 67.3524
C509.TCGA-78-7166-01A-12D-2063-08.1_gdc_realn.bam
sum=5511615551
totalNumLoci=78578629
Average depth of coverage = 70.1414
```{r}
sum_of_depths = c(4391333584,4297634866,4400886315,5610805155,5077875345,5511615551)
ave_depth_of_coverage = c(60.8246,59.0301,60.5195,71.8673,67.3524,70.1414)
target_area = sum_of_depths/ave_depth_of_coverage
ave_target_area = mean(target_area)
print(ave_target_area)
ave_target_area.Mb =ave_target_area/1000000
print(ave_target_area.Mb)
```
```{r}
# 455,319 variants passed all filters (true somatic variants)
# 507,682 bases of variants that passed all filters
# 630 vcf files
# 74,960,383 average bases in target area
mutation_rate <- 507682/(630*74960383)
print(mutation_rate)
```
```{r}
# 74,960,383 average bases in target area
snp_bases <-c(2842011,2851863,2833714,2996267,2930555, 2982399)
target_area_bases <-c(72196670,72804126,72718484,78071740,75392641,78578636)
snp_densities <- snp_bases/target_area_bases
ave_snp_density <- mean(snp_densities)
print(ave_snp_density)
est_snp_total.bases <- ave_snp_density*74960383*630
print(est_snp_total.bases)
est_mutations_in_PoN <- mutation_rate*est_snp_total.bases
print(est_mutations_in_PoN)
```
```{r}
mean(2842011,2851863,2833714,2996267,2930555, 2982399)
```
```{r}
# match and mismatch are in reference to the alt allele of the variant compared to the PoN
# matches are normal SNPs in the dataset and mismatches are candidate somatic mutations
onlypon.matches<-read.table('/home/liamporter/aThesis/only.pon/LUAD.onlypon.matches.txt',
col.names = c("Chr", "Pos", "rs ID",
"ref allele", "alt allele", "filtered", "AF" ))
onlypon.matches$AF <- gsub("^[^:]+:[^:]+:([^:]+):.*$", "\\1", onlypon.matches$AF)
onlypon.mismatches<-read.table('/home/liamporter/aThesis/only.pon/LUAD.onlypon.mismatches.txt',
col.names = c("Chr", "Pos", "rs ID",
"ref allele", "alt allele", "filtered", "AF" ))
onlypon.mismatches$AF <- gsub("^[^:]+:[^:]+:([^:]+):.*$", "\\1", onlypon.mismatches$AF)
allpon.matches <- read.table('/home/liamporter/aThesis/all.pon/LUAD.allpon.matches.txt',
col.names = c("Chr", "Pos", "rs ID",
"ref allele", "alt allele", "filtered", "AF" ))
allpon.matches$AF <- gsub("^[^:]+:[^:]+:([^:]+):.*$", "\\1", allpon.matches$AF)
allpon.mismatches<-read.table('/home/liamporter/aThesis/all.pon/LUAD.allpon.mismatches.txt',
col.names = c("Chr", "Pos", "rs ID",
"ref allele", "alt allele", "filtered", "AF" ))
allpon.mismatches$AF <- gsub("^[^:]+:[^:]+:([^:]+):.*$", "\\1", allpon.mismatches$AF)
```
```{r}
## FILTERING MUST HAVE AN RS ID
onlypon.matches.rsids<-read.table('/home/liamporter/aThesis/only.pon/LUAD.onlypon.matches.rsid.txt',
col.names = c("Chr", "Pos", "rs ID",
"ref allele", "alt allele", "filtered", "AF" ))
onlypon.matches.rsids$AF <- gsub("^[^:]+:[^:]+:([^:]+):.*$", "\\1", onlypon.matches.rsids$AF)
onlypon.mismatches.rsids <-read.table('/home/liamporter/aThesis/only.pon/LUAD.onlypon.mismatches.rsid.txt',
col.names = c("Chr", "Pos", "rs ID",
"ref allele", "alt allele", "filtered", "AF" ))
onlypon.mismatches.rsids$AF <- gsub("^[^:]+:[^:]+:([^:]+):.*$", "\\1", onlypon.mismatches.rsids$AF)
allpon.matches.rsids <- read.table('/home/liamporter/aThesis/all.pon/LUAD.allpon.matches.rsid.txt',
col.names = c("Chr", "Pos", "rs ID",
"ref allele", "alt allele", "filtered", "AF" ))
allpon.matches.rsids$AF <- gsub("^[^:]+:[^:]+:([^:]+):.*$", "\\1", allpon.matches.rsids$AF)
allpon.mismatches.rsids<-read.table('/home/liamporter/aThesis/all.pon/LUAD.allpon.mismatches.rsid.txt',
col.names = c("Chr", "Pos", "rs ID",
"ref allele", "alt allele", "filtered", "AF" ))
allpon.mismatches.rsids$AF <- gsub("^[^:]+:[^:]+:([^:]+):.*$", "\\1", allpon.mismatches.rsids$AF)
```
```{r}
# rs IDs do not need to match PoN
variant_count <- data.frame(
group= c('normal_snps.allpon',
'candidate_variants.allpon',
'normal_snps.onlypon',
'candidate_variants.onlypon'),
count= c(nrow(allpon.matches),
nrow(allpon.mismatches),
nrow(onlypon.matches),
nrow(onlypon.mismatches))
)
print(variant_count)
```
```{r}
# rs IDs match PoN
variant_count_rsIDs <- data.frame(
group= c('normal_snps.allpon.rsid',
'candidate_variants.allpon.rsid',
'normal_snps.onlypon.rsid',
'candidate_variants.onlypon.rsid'),
count= c(nrow(allpon.matches.rsids),
nrow(allpon.mismatches.rsids),
nrow(onlypon.matches.rsids),
nrow(onlypon.mismatches.rsids))
)
print(variant_count_rsIDs)
```
```{r}
# compare variants to bases
all.pon.mismatches<- 186812
only.pon.mismatches<-13451
all.pon.mismatches.rsid<-2474
only.pon.mismatches.rsid<-513
bases.all.pon.mismatches<- 443151
bases.only.pon.mismatches<-21934
bases.all.pon.mismatches.rsid<-2474
bases.only.pon.mismatches.rsid<-513
candidates_bases<- c(bases.all.pon.mismatches,
bases.only.pon.mismatches,
bases.all.pon.mismatches.rsid,
bases.only.pon.mismatches.rsid)
candidate_variants<- c(all.pon.mismatches,
only.pon.mismatches,
all.pon.mismatches.rsid,
only.pon.mismatches.rsid)
filter_group<- c("PoN +Others",
"Only PoN",
"PoN +Others (rsID)",
"Only PoN (rsID)")
df1 <- data.frame(filter_group, candidate_variants,candidates_bases)
df1
```
```{r}
library(ggplot2)
library(dplyr)
library(tidyr)
library(gt)
library(gtExtras)
library(gridExtra)
library(ggrepel)
library(scales)
```
```{r}
filter_group <- c("PoN Only",
"PoN Only",
"PoN + Others",
"PoN + Others",
"PoN Only (rs IDs)",
"PoN Only (rs IDs)",
"PoN + Others (rs IDs)",
"PoN + Others (rs IDs)")
status <- c("Candidate Somatic Variants",
"Normal SNPs",
"Candidate Somatic Variants",
"Normal SNPs",
"Candidate Somatic Variants",
"Normal SNPs",
"Candidate Somatic Variants",
"Normal SNPs")
count= c(nrow(onlypon.mismatches),
nrow(onlypon.matches),
nrow(allpon.mismatches),
nrow(allpon.matches),
nrow(onlypon.mismatches.rsids),
nrow(onlypon.matches.rsids),
nrow(allpon.mismatches.rsids),
nrow(allpon.matches.rsids))
variant.count <- data.frame(filter_group,status, count)
colnames(variant.count)<- c("filter group",
"status",
"count")
print(variant.count) %>%
gt() %>%
gt_theme_538()
```
```{r}
groups.df <- data.frame(
group = c('PoN + Others',
'PoN + Others',
'PoN Only',
'PoN Only',
'PoN + Others (rs ID)',
'PoN + Others (rs ID)',
'PoN Only (rs ID)',
'PoN Only (rs ID)'),
status = c('mismatch',
'match',
'mismatch',
'match',
'mismatch',
'match',
'mismatch',
'match'),
count = c(nrow(allpon.mismatches),
nrow(allpon.matches),
nrow(onlypon.mismatches),
nrow(onlypon.matches),
nrow(allpon.mismatches.rsids),
nrow(allpon.matches.rsids),
nrow(onlypon.mismatches.rsids),
nrow(onlypon.matches.rsids)))
totals <- aggregate(count ~ group, groups.df, sum)
groups.df <- merge(groups.df, totals, by = "group")
colnames(groups.df)<- c('group', 'status', 'count', 'total')
groups.df$percentage <- groups.df$count / groups.df$total * 100
print(groups.df)
#png(file="final_graphs/plot5.stackedbar.png")
ggplot(groups.df, aes(x = group, y=count)) +
geom_col(aes(fill = status)) +
geom_text(aes(y= count/2,
label = ifelse((count > 180000), count, "")), size = 4) +
geom_text_repel(aes(x = group,
y = count,
label = ifelse(count < 30000, count, "")),
size = 4,
hjust=-.4,
vjust=-2.5,
segment.color="brown2")+
geom_text_repel(aes(x = group,
y = count,
label = ifelse(count > 30000 & count < 180000, count, "")),
size = 4,
hjust=1.5,
vjust=-4,
segment.color="deepskyblue")+
scale_fill_manual(values = c("deepskyblue", "brown2"),
labels = c("Normal SNPs","Candidate Somatic Variants"))+
labs(title = "Portion of Candidate Somatic Variants",
x="Filter Reason and if rs ID matches PoN",
y="Number of Variants")+
scale_y_continuous(breaks = seq(0, 180000000, by = 500000))+
theme_classic() +
theme(
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y= element_blank(),
axis.text.x = element_text(vjust = 5,
size=9,
face="bold"),
axis.text.y = element_text(size=10),
legend.text = element_text(size=8),
legend.position = c(0.8,0.8),
legend.key.size = unit(1.5,"line"),
panel.grid.major.y = element_line(linewidth = 0.1,
linetype = 'solid',
colour = "grey"),
panel.grid.minor.y = element_line(linewidth = 0.1,
linetype = 'solid',
colour = "grey"),
legend.title = element_blank())
onlypon.groups.df <- subset(groups.df, group %in% c("PoN Only", "PoN Only (rs ID)"))
#png(file="final_graphs/plot6.density.onlypon.png")
ggplot(onlypon.groups.df, aes(x = group, y = count)) +
geom_col(aes(fill = status)) +
geom_text(aes(y = count/2,
label = ifelse(count > 600, count, "")), size = 4) +
geom_text_repel(aes(x = group,
y = count,
label = ifelse(count < 600, count, "")),
size = 4,
hjust = -1.5,
vjust = -1.5,
segment.color = "brown2") +
scale_fill_manual(values = c("deepskyblue", "brown2"),
labels = c("Normal SNPs","Candidate Somatic Variants"))+
labs(title = "Portion of Candidate Somatic Variants",
subtitle = "Filtered only due to PoN",
x="Filter Reason and if rs ID matches PoN",
y="Number of Variants")+
scale_y_continuous(limits = c(0, 60000))+
theme_classic() +
theme(
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y= element_blank(),
axis.text.x = element_text(vjust = 5,
size=9,
face="bold"),
axis.text.y = element_text(size=10),
legend.text = element_text(size=8),
legend.position = c(0.8,0.8),
legend.key.size = unit(1.5,"line"),
panel.grid.major.y = element_line(linewidth = 0.1,
linetype = 'solid',
colour = "grey"),
panel.grid.minor.y = element_line(linewidth = 0.1,
linetype = 'solid',
colour = "grey"),
legend.title = element_blank())
```
```{r}
allpon_pie <- subset(groups.df, group == "PoN + Others")
allpon.rsid_pie <- subset(groups.df, group == "PoN + Others (rs ID)")
onlypon_pie <- subset(groups.df, group == "PoN Only")
onlypon.rsid_pie <- subset(groups.df, group == "PoN Only (rs ID)")
# all pon
#png(file="plot7.pie.allpon.png")
ggplot(allpon_pie, aes(x = "", y = count, fill = status)) +
geom_bar(stat = "identity", width = 1) +
geom_text(aes(y=count/2, label=ifelse((percentage > 10),
paste0(round(percentage, 3), "%"), "")), size=4)+
geom_text_repel(aes(y = count/2,
label = ifelse((percentage <= 10), paste0(round(percentage, 3), "%"), "")),
size = 4,
segment.color = "black",
segment.size = 0.3,
nudge_x = 0.68) +
coord_polar("y", start = 0) +
theme_void() +
scale_fill_manual(values = c("deepskyblue", "brown2"),
labels = c("Normal SNPs","Candidate Somatic Variants"))+
labs(fill = "Status") +
ggtitle("PoN + Others") +
theme(legend.position = "right")+
labs(subtitle = paste0(allpon_pie$total, " Total Variants")) +
theme(legend.title = element_blank(), legend.position = "right")
#dev.off()
# all pon rsID
#png(file="plot8.pie.allpon.rsid.png")
ggplot(allpon.rsid_pie, aes(x = "", y = percentage, fill = status)) +
geom_bar(stat = "identity", width = 1) +
geom_text(aes(y=percentage/2, label=ifelse((percentage > 4),
paste0(round(percentage, 3), "%"), "")), size=4)+
geom_text_repel(aes(y = percentage/2,
label = ifelse((percentage <= 4),paste0(round(percentage, 3), "%"), "")),
size = 4,
segment.color = "black",
segment.size = 0.3,
nudge_x = 0.68) +
coord_polar("y", start = 0) +
theme_void() +
scale_fill_manual(values = c("deepskyblue", "brown2"),
labels = c("Normal SNPs","Candidate Somatic Variants"))+
labs(fill = "Status") +
ggtitle("PoN + Others, rs IDs match PoN") +
theme(legend.position = "right")+
labs(subtitle = paste0(allpon.rsid_pie$total, " Total Variants")) +
theme(legend.title = element_blank(), legend.position = "right")
#dev.off()
# only pon
#png(file="plot9.pie.onlypon.png")
ggplot(onlypon_pie, aes(x = "", y = count, fill = status)) +
geom_bar(stat = "identity", width = 1) +
geom_text(aes(y=count/2,
label=ifelse((percentage > 4), paste0(round(percentage, 3), "%"), "")), size=4)+
geom_text_repel(aes(y = count/2,
label = ifelse((percentage <= 4), paste0(round(percentage, 3), "%"), "")),
size = 4,
segment.color = "black",
segment.size = 0.3,
nudge_x = 0.68) +
coord_polar("y", start = 0) +
theme_void() +
scale_fill_manual(values = c("deepskyblue", "brown2"),
labels = c("Normal SNPs","Candidate Somatic Variants"))+
labs(fill = "Status") +
ggtitle("PoN Only") +
theme(legend.position = "right")+
labs(subtitle = paste0(onlypon_pie$total, " Total Variants")) +
theme(legend.title = element_blank(), legend.position = "right")
#dev.off()
# only pon rsid
#png(file="plot10.pie.allpon.rsid.png")
ggplot(onlypon.rsid_pie, aes(x = "", y = count, fill = status)) +
geom_bar(stat = "identity", width = 1) +
geom_text(aes(y=count/2,
label=ifelse((percentage > 4), paste0(round(percentage, 3), "%"), "")), size=4)+
geom_text_repel(aes(y = count/2,
label = ifelse((percentage <= 4), paste0(round(percentage, 3), "%"), "")),
size = 4,
segment.color = "black",
segment.size = 0.3,
nudge_x = 0.68) +
coord_polar("y", start = 0) +
theme_void() +
scale_fill_manual(values = c("deepskyblue", "brown2"),
labels = c("Normal SNPs","Candidate Somatic Variants"))+
labs(fill = "Status") +
ggtitle("PoN Only, rs IDs match PoN") +
theme(legend.position = "right")+
labs(subtitle = paste0(onlypon.rsid_pie$total, " Total Variants")) +
theme(legend.title = element_blank(), legend.position = "right")
#dev.off()
```
```{r}
## PERCENTAGE OF REASONS FILTERED FOR PON + OTHER REASONS
filter_reasons_allpon.matches <- separate(
data=data.frame(allpon.matches$filtered),
col = allpon.matches.filtered,
into = paste0("Reason", 1:5),
sep = ";",
fill = "right")
unique_reasons <- c(unique(filter_reasons_allpon.matches$Reason1),
unique(filter_reasons_allpon.matches$Reason2),
unique(filter_reasons_allpon.matches$Reason3),
unique(filter_reasons_allpon.matches$Reason4),
unique(filter_reasons_allpon.matches$Reason5))
unique_reasons <- unique(unique_reasons)
unique_reasons <- subset(unique_reasons, unique_reasons != "NA")
#print(unique_reasons)
print('percentage of filter reasons for all filtered by PoN Normal SNPs')
allpon.match.reasons.df <- data.frame(reason = character(), percentage = numeric())
percentages_match <- numeric()
for (reason in unique_reasons) {
percentage <- sum(apply(filter_reasons_allpon.matches, 1, function(row)
any(grepl(reason, row)))) / nrow(filter_reasons_allpon.matches) * 100
percentage <- round(percentage, 2)
percentages_match <- c(percentages_match, (percentage/100))
result <- paste0(as.character(percentage), "% ", reason)
allpon.match.reasons.df <- rbind(allpon.match.reasons.df, data.frame(reason, percentage))
print(result)
}
filter_reasons_allpon.mismatches <- separate(
data=data.frame(allpon.mismatches$filtered),
col = allpon.mismatches.filtered,
into = paste0("Reason", 1:5),
sep = ";",
fill = "right")
unique_reasons2 <- c(unique(filter_reasons_allpon.mismatches$Reason1),
unique(filter_reasons_allpon.mismatches$Reason2),
unique(filter_reasons_allpon.mismatches$Reason3),
unique(filter_reasons_allpon.mismatches$Reason4),
unique(filter_reasons_allpon.mismatches$Reason5))
unique_reasons2 <- unique(unique_reasons2)
unique_reasons2 <- subset(unique_reasons2, unique_reasons2 != "NA")
print('percentage for filter reasons for all filtered by PoN Candidate Somatic Variants' )
allpon.mismatch.reasons.df <- data.frame(reason = character(), percentage.mismatch.group = numeric())
percentages_mismatch <- numeric()
for (reason in unique_reasons2) {
percentage <- sum(apply(filter_reasons_allpon.mismatches, 1, function(row)
any(grepl(reason, row)))) / nrow(filter_reasons_allpon.mismatches) * 100
percentage <- round(percentage, 2)
percentages_mismatch <- c(percentages_mismatch, (percentage/100))
result <- paste0(as.character(percentage), "% ", reason)
allpon.mismatch.reasons.df <- rbind(allpon.mismatch.reasons.df, data.frame(reason, percentage))
print(result)
}
```
```{r}
filter_reasons_allpon <- merge(allpon.match.reasons.df,
allpon.mismatch.reasons.df,
by="reason",
all=T)
names(filter_reasons_allpon) <- c("reason",
"percentage.matchgroup",
"percentage.mismatchgroup")
combined_df <- merge(allpon.match.reasons.df,
allpon.mismatch.reasons.df,
by = "reason",
suffixes = c(".match", ".mismatch"))
reshaped_df <- tidyr::gather(combined_df,
group,
percentage,
-reason)
#png(file="final_graphs/plot11.perc.allpon.png")
ggplot(reshaped_df, aes(x = reorder(reason, -percentage), y = percentage, fill = group)) +
geom_bar(position = position_dodge(width = 0.8), stat = "identity") +
labs(title="Percentage of Filter Reasons",
subtitle = " ",
x = "Filter Reason",
y = "%") +
scale_fill_manual(values = c("deepskyblue", "brown2"),
labels = c("Normal SNPs","Candidate Somatic Variants"))+
theme(panel.background = element_rect(fill = "white"),
panel.grid.major.y = element_line(size = 0.1,
linetype = 'solid',
colour = "lightgrey"),
panel.grid.minor.y = element_line(size = 0.1,
linetype = 'solid',
colour = "lightgrey"),
panel.grid.major.x = element_line(size = 0.1,
linetype = 'solid',
colour = "lightgrey"),
axis.ticks = element_blank(),
axis.text = element_text(size =8),
axis.title.y = element_text(angle = 0,
vjust = 0.55,
hjust = 0,
margin = margin(r=15)),
axis.text.x = element_text(angle = 50,
vjust = 1,
hjust = 1,
size = 11))
```
```{r}
## WITH MATCHING RS IDs
filter_reasons_allpon.matches.rsid <- separate(
data=data.frame(allpon.matches.rsids$filtered),
col = allpon.matches.rsids.filtered,
into = paste0("Reason", 1:5),
sep = ";",
fill = "right")
unique_reasons3 <- c(unique(filter_reasons_allpon.matches.rsid$Reason1),
unique(filter_reasons_allpon.matches.rsid$Reason2),
unique(filter_reasons_allpon.matches.rsid$Reason3),
unique(filter_reasons_allpon.matches.rsid$Reason4),
unique(filter_reasons_allpon.matches.rsid$Reason5))
unique_reasons3 <- unique(unique_reasons3)
unique_reasons3 <- subset(unique_reasons3, unique_reasons3 != "NA")
print(unique_reasons3)
###
filter_reasons_allpon.mismatches.rsid <- separate(
data=data.frame(allpon.mismatches.rsids$filtered),
col = allpon.mismatches.rsids.filtered,
into = paste0("Reason", 1:5),
sep = ";",
fill = "right")
unique_reasons4 <- c(unique(filter_reasons_allpon.mismatches.rsid$Reason1),
unique(filter_reasons_allpon.mismatches.rsid$Reason2),
unique(filter_reasons_allpon.mismatches.rsid$Reason3),
unique(filter_reasons_allpon.mismatches.rsid$Reason4),
unique(filter_reasons_allpon.mismatches.rsid$Reason5))
unique_reasons4 <- unique(unique_reasons4)
unique_reasons4 <- subset(unique_reasons4, unique_reasons4 != "NA")
print(unique_reasons4)
```
```{r}
unique_reasons4 <- c(unique_reasons4, "str_contraction")
print('percentage for filter reasons for all filtered by PoN with matching rs IDs and that match alt alleles of SNPs' )
allpon.match.reasons.rsid.df <- data.frame(reason = character(), percentage.match.group = numeric())
for (reason in unique_reasons3) {
percentage <- sum(apply(filter_reasons_allpon.matches.rsid, 1, function(row)
any(grepl(reason, row)))) / nrow(filter_reasons_allpon.matches.rsid) * 100
percentage <- round(percentage, 2)
result <- paste0(as.character(percentage), "% ", reason)
allpon.match.reasons.rsid.df <- rbind(allpon.match.reasons.rsid.df, data.frame(reason, percentage))
print(result)
}
print('percentage for filter reasons for all filtered by PoN with matching rs IDs and that do not match alt alleles of SNPs' )
allpon.mismatch.reasons.rsid.df <- data.frame(reason = character(), percentage.mismatch.group = numeric())
for (reason in unique_reasons4) {
percentage <- sum(apply(filter_reasons_allpon.mismatches.rsid, 1, function(row)
any(grepl(reason, row)))) / nrow(filter_reasons_allpon.mismatches.rsid) * 100
percentage <- round(percentage, 2)
result <- paste0(as.character(percentage), "% ", reason)
allpon.mismatch.reasons.rsid.df <- rbind(allpon.mismatch.reasons.rsid.df, data.frame(reason, percentage))
print(result)
}
```
```{r}
filter_reasons_allpon.rsid <- merge(allpon.match.reasons.rsid.df,
allpon.mismatch.reasons.rsid.df,
by="reason",
all=T)
names(filter_reasons_allpon) <- c("reason",
"percentage.matchgroup",
"percentage.mismatchgroup")
combined_df <- merge(allpon.match.reasons.rsid.df,
allpon.mismatch.reasons.rsid.df,
by = "reason",
suffixes = c(".match", ".mismatch"))
reshaped_df <- tidyr::gather(combined_df, group, percentage, -reason)
#png(file="final_graphs/plot12.perc.allpon.rsid.png")
ggplot(reshaped_df, aes(x = reorder(reason, -percentage), y = percentage, fill = group)) +
geom_bar(position = position_dodge(width = 0.8), stat = "identity") +
labs(title="Percentage of Filter Reasons",
subtitle = "rs IDs match PoN",
x = "Filter Reason",
y = "%") +
scale_fill_manual(values = c("deepskyblue", "brown2"),
labels = c("Normal SNPs","Candidate Somatic Variants"))+
theme(panel.background = element_rect(fill = "white"),
panel.grid.major.y = element_line(size = 0.1,
linetype = 'solid',
colour = "lightgrey"),
panel.grid.minor.y = element_line(size = 0.1,
linetype = 'solid',
colour = "lightgrey"),
panel.grid.major.x = element_line(size = 0.1,
linetype = 'solid',
colour = "lightgrey"),
axis.ticks = element_blank(),
axis.text = element_text(size =8),
axis.title.y = element_text(angle = 0,
vjust = 0.55,
hjust = 0,
margin = margin(r=15)),
axis.text.x = element_text(angle = 50,
vjust = 1,
hjust = 1,
size = 11))
```
```{r}
## FILTERED BY ONLY PON MATCHES VS MISMATCHES
onlypon_df <- data.frame( matches = onlypon.matches$AF,
mismatches = c(onlypon.mismatches$AF,
rep(NA, nrow(onlypon.matches) - nrow(onlypon.mismatches))))
onlypon_df$mismatches <- as.numeric(onlypon_df$mismatches)
onlypon_df$matches <- as.numeric(onlypon_df$matches)
```
```{r}
#png(file="final_graphs/plot1.density.onlypon.png")
ggplot(onlypon_df) +
geom_density(aes(x = mismatches, fill = "Candidate Somatic Variants"), alpha = 0.5) +
geom_density(aes(x = matches, fill = "Normal SNPs"), alpha = 0.5) +
scale_x_continuous(limits = c(0, 1), breaks = seq(0, 1, by = 0.1)) +
coord_cartesian(ylim = c(0, 12)) +
labs(title = "Alternate Allele Frequency",
subtitle = "filtered only due to PoN",
x = "Frequency",
y = "Density") +
theme(panel.spacing = unit(1, "lines"),
panel.background = element_rect(fill = "white"),
panel.grid.major.y = element_line(size = 0.1,
linetype = "solid",
colour = "lightgrey"),
panel.grid.minor.y = element_line(size = 0.1,
linetype = "solid",
colour = "lightgrey"),
panel.grid.major.x = element_line(size = 0.1,
linetype = "solid",
colour = "lightgrey"),
axis.ticks = element_blank(),
axis.text.x = element_text(angle = 30,
vjust = 1,
hjust = 1,
size = 10),
axis.title.y = element_text(angle = 0,
vjust = 0.55,
hjust = 0,
margin = margin(r = 15)),
axis.text.y = element_text(size = 10)) +
guides(fill = guide_legend(title = NULL)) +
scale_fill_manual(values = c("red", "deepskyblue")) +
theme(legend.position = c(0.95, 0.95), legend.justification = c(1, 1))
```
```{r}
wilcox.test(as.numeric(onlypon.mismatches$AF),
as.numeric(onlypon.matches$AF))
mean(as.numeric(onlypon.mismatches$AF))
mean(as.numeric(onlypon.matches$AF))
```
```{r}
## FILTERED BY PON + OTHER REASONS MATCHES VS MISMATCHES
allpon_df <- data.frame( matches = allpon.matches$AF,
mismatches = c(allpon.mismatches$AF,
rep(NA, nrow(allpon.matches) - nrow(allpon.mismatches))))
allpon_df$mismatches <- as.numeric(allpon_df$mismatches)
allpon_df$matches <- as.numeric(allpon_df$matches)
```
```{r}
#png(file="final_graphs/plot2.density.allpon.png")
ggplot(allpon_df) +
geom_density(aes(x = mismatches, fill = "Candidate Somatic Variants"), alpha = 0.5) +
geom_density(aes(x = matches, fill = "Normal SNPs"), alpha = 0.5) +
scale_x_continuous(limits = c(0, 1), breaks = seq(0, 1, by = 0.1)) +
coord_cartesian(ylim = c(0, 12)) +
labs(title = "Alternate Allele Frequency",
subtitle = "filtered due to PoN + other reasons",
x = "Frequency",
y = "Density") +
theme(panel.spacing = unit(1, "lines"),
panel.background = element_rect(fill = "white"),
panel.grid.major.y = element_line(size = 0.1,
linetype = "solid",
colour = "lightgrey"),
panel.grid.minor.y = element_line(size = 0.1,
linetype = "solid",
colour = "lightgrey"),
panel.grid.major.x = element_line(size = 0.1,
linetype = "solid",
colour = "lightgrey"),
axis.ticks = element_blank(),
axis.text.x = element_text(angle = 30,
vjust = 1,
hjust = 1,
size = 10),
axis.title.y = element_text(angle = 0,
vjust = 0.55,
hjust = 0,
margin = margin(r = 15)),
axis.text.y = element_text(size = 10)) +
guides(fill = guide_legend(title = NULL)) +
scale_fill_manual(values = c("red", "deepskyblue")) +
theme(legend.position = c(0.95, 0.95), legend.justification = c(1, 1))
```
```{r}
wilcox.test(as.numeric(allpon.mismatches$AF),
as.numeric(allpon.matches$AF))
mean(as.numeric(allpon.mismatches$AF))
mean(as.numeric(allpon.matches$AF))
```
```{r}
## FILTERED BY ONLY PON MATCHES VS MISMATCHES
onlypon.rs_df <- data.frame( matches = onlypon.matches.rsids$AF,
mismatches = c(onlypon.mismatches.rsids$AF,
rep(NA, nrow(onlypon.matches.rsids) - nrow(onlypon.mismatches.rsids))))
onlypon.rs_df$mismatches <- as.numeric(onlypon.rs_df$mismatches)
onlypon.rs_df$matches <- as.numeric(onlypon.rs_df$matches)
```
```{r}
#png(file="final_graphs/plot3.density.onlypon.rsid.png")
ggplot(onlypon.rs_df) +
geom_density(aes(x = mismatches, fill = "Candidate Somatic Variants"), alpha = 0.5) +
geom_density(aes(x = matches, fill = "Normal SNPs"), alpha = 0.5) +
scale_x_continuous(limits = c(0, 1), breaks = seq(0, 1, by = 0.1)) +
coord_cartesian(ylim = c(0, 12)) +
labs(title = "Alternate Allele Frequency",
subtitle = "filtered only due to PoN (rs IDs)",
x = "Frequency",
y = "Density") +
theme(panel.spacing = unit(1, "lines"),
panel.background = element_rect(fill = "white"),
panel.grid.major.y = element_line(size = 0.1,
linetype = "solid",
colour = "lightgrey"),
panel.grid.minor.y = element_line(size = 0.1,
linetype = "solid",
colour = "lightgrey"),
panel.grid.major.x = element_line(size = 0.1,
linetype = "solid",
colour = "lightgrey"),
axis.ticks = element_blank(),
axis.text.x = element_text(angle = 30,
vjust = 1,
hjust = 1,
size = 10),
axis.title.y = element_text(angle = 0,
vjust = 0.55,
hjust = 0,
margin = margin(r = 15)),
axis.text.y = element_text(size = 10)) +
guides(fill = guide_legend(title = NULL)) +
scale_fill_manual(values = c("red", "deepskyblue")) +
theme(legend.position = c(0.95, 0.95), legend.justification = c(1, 1))
```
```{r}
wilcox.test(as.numeric(onlypon.mismatches.rsids$AF),
as.numeric(onlypon.matches.rsids$AF))
mean(as.numeric(onlypon.mismatches.rsids$AF))
mean(as.numeric(onlypon.matches.rsids$AF))
```
```{r}
## FILTERED BY PON + OTHER REASONS MATCHES VS MISMATCHES
allpon_df <- data.frame( matches = allpon.matches.rsids$AF,
mismatches = c(allpon.mismatches.rsids$AF,
rep(NA, nrow(allpon.matches.rsids) - nrow(allpon.mismatches.rsids))))
allpon_df$mismatches <- as.numeric(allpon_df$mismatches)
allpon_df$matches <- as.numeric(allpon_df$matches)
```
```{r}
#png(file="final_graphs/plot4.density.allpon.rsid.png")
ggplot(allpon_df) +
geom_density(aes(x = mismatches, fill = "Candidate Somatic Variants"), alpha = 0.5) +
geom_density(aes(x = matches, fill = "Normal SNPs"), alpha = 0.5) +
scale_x_continuous(limits = c(0, 1), breaks = seq(0, 1, by = 0.1)) +
coord_cartesian(ylim = c(0, 12)) +
labs(title = "Alternate Allele Frequency",
subtitle = "filtered due to PoN + other filters (rs IDs)",
x = "Frequency",
y = "Density") +
theme(panel.spacing = unit(1, "lines"),
panel.background = element_rect(fill = "white"),
panel.grid.major.y = element_line(size = 0.1,
linetype = "solid",
colour = "lightgrey"),
panel.grid.minor.y = element_line(size = 0.1,
linetype = "solid",
colour = "lightgrey"),
panel.grid.major.x = element_line(size = 0.1,
linetype = "solid",
colour = "lightgrey"),
axis.ticks = element_blank(),
axis.text.x = element_text(angle = 30,
vjust = 1,
hjust = 1,
size = 10),
axis.title.y = element_text(angle = 0,
vjust = 0.55,
hjust = 0,
margin = margin(r = 15)),
axis.text.y = element_text(size = 10)) +
guides(fill = guide_legend(title = NULL)) +
scale_fill_manual(values = c("red", "deepskyblue")) +
theme(legend.position = c(0.95, 0.95), legend.justification = c(1, 1))
```
```{r}
wilcox.test(as.numeric(allpon.mismatches.rsids$AF),
as.numeric(allpon.matches.rsids$AF))
mean(as.numeric(allpon.mismatches.rsids$AF))
mean(as.numeric(allpon.matches.rsids$AF))
```
```{r}
## RECURRENT VARIANTS
#print(onlypon.mismatches.rsids)
recurrent_variants.rsids <- duplicated(onlypon.mismatches.rsids$rs.ID) |
duplicated(onlypon.mismatches.rsids$rs.ID, fromLast = TRUE)
#print(recurrent_variants.rsids)
recurrent_variants.rsids.df <- onlypon.mismatches.rsids[recurrent_variants.rsids, , drop = FALSE]
print(recurrent_variants.rsids.df)
onlypon_recurrent.rsids <- unique(recurrent_variants.rsids.df$rs.ID)
print(onlypon_recurrent.rsids)
total <- recurrent_variants.rsids.df$rs.ID
#print(total)
rsid_counts <- table(total)
occurences_onlypon <- data.frame(rsID = names(rsid_counts),