-
Notifications
You must be signed in to change notification settings - Fork 0
/
PeakMorph_CE_vs_Amp.R
129 lines (107 loc) · 5.99 KB
/
PeakMorph_CE_vs_Amp.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
library(tidyverse)
library(ggforce)
#### Replicate of the PeakMorph_2111 script but for samples where CE and Amp calls are in disagreement ####
### Function to make length_dist files tidy ###
length_dist_tidy <- function(x){
BKT_ID <- deparse(substitute(x))
output <- x %>%
mutate(uSat_locus = str_replace_all(Microsatellite, "-", "_"), .keep = "unused") %>%
select(-sum) %>%
pivot_longer(-c(uSat_locus, scores), names_to = "Length", values_to = "Read_count") %>%
add_column(SampleID = BKT_ID) %>%
mutate(SampleID = str_remove_all(SampleID, "BKT_")) %>%
mutate(Length = as.numeric(Length))
print(output)
}
### Read in 12 BKT ### These are the 12 fish with the greatest number of CE vs Amp disagreements
BKT_17_03053 <- read_delim("X:/2201_BKT_msat_conversion/Sfon_2201-001_paired_uSat_output/Sfon_2201-001_length_distribution/Genotype_17-03053.txt")
BKT_17_03009 <- read_delim("X:/2201_BKT_msat_conversion/Sfon_2201-001_paired_uSat_output/Sfon_2201-001_length_distribution/Genotype_17-03009.txt")
BKT_17_03011 <- read_delim("X:/2201_BKT_msat_conversion/Sfon_2201-001_paired_uSat_output/Sfon_2201-001_length_distribution/Genotype_17-03011.txt")
BKT_17_03051 <- read_delim("X:/2201_BKT_msat_conversion/Sfon_2201-001_paired_uSat_output/Sfon_2201-001_length_distribution/Genotype_17-03051.txt")
BKT_17_03052 <- read_delim("X:/2201_BKT_msat_conversion/Sfon_2201-001_paired_uSat_output/Sfon_2201-001_length_distribution/Genotype_17-03052.txt")
BKT_17_03010 <- read_delim("X:/2201_BKT_msat_conversion/Sfon_2201-001_paired_uSat_output/Sfon_2201-001_length_distribution/Genotype_17-03010.txt")
BKT_17_03015 <- read_delim("X:/2201_BKT_msat_conversion/Sfon_2201-001_paired_uSat_output/Sfon_2201-001_length_distribution/Genotype_17-03015.txt")
BKT_17_03016 <- read_delim("X:/2201_BKT_msat_conversion/Sfon_2201-001_paired_uSat_output/Sfon_2201-001_length_distribution/Genotype_17-03016.txt")
BKT_17_03058 <- read_delim("X:/2201_BKT_msat_conversion/Sfon_2201-001_paired_uSat_output/Sfon_2201-001_length_distribution/Genotype_17-03058.txt")
BKT_17_03057 <- read_delim("X:/2201_BKT_msat_conversion/Sfon_2201-001_paired_uSat_output/Sfon_2201-001_length_distribution/Genotype_17-03057.txt")
BKT_17_03012 <- read_delim("X:/2201_BKT_msat_conversion/Sfon_2201-001_paired_uSat_output/Sfon_2201-001_length_distribution/Genotype_17-03012.txt")
BKT_17_03054 <- read_delim("X:/2201_BKT_msat_conversion/Sfon_2201-001_paired_uSat_output/Sfon_2201-001_length_distribution/Genotype_17-03054.txt")
### Run each fish's length_dist file through my tidying function while joining them ###
All_LD_data <- bind_rows(length_dist_tidy(BKT_17_03053),
length_dist_tidy(BKT_17_03009),
length_dist_tidy(BKT_17_03011),
length_dist_tidy(BKT_17_03051),
length_dist_tidy(BKT_17_03052),
length_dist_tidy(BKT_17_03010),
length_dist_tidy(BKT_17_03015),
length_dist_tidy(BKT_17_03016),
length_dist_tidy(BKT_17_03058),
length_dist_tidy(BKT_17_03057),
length_dist_tidy(BKT_17_03012),
length_dist_tidy(BKT_17_03054)) %>%
filter(uSat_locus == "L_SFOC113" |
uSat_locus == "L_SFOC24" |
uSat_locus == "L_SFOC28" |
uSat_locus == "L_SFOC88" |
uSat_locus == "SFOC86" |
uSat_locus == "SFO_18" |
uSat_locus == "SfoC38" |
uSat_locus == "SfoD75" |
uSat_locus == "SfoD91")
All_LD_data %>%
summarize(n_loci = n_distinct(uSat_locus))
All_LD_data %>%
#filter(Read_count > 0) %>%
group_by(uSat_locus, SampleID) %>%
#slice_max(Read_count, n = 10) %>%
filter(Read_count >= max(Read_count)*0.25) %>%
ungroup() %>%
summarize(x = n_distinct(uSat_locus))
### Make peak morphology plots, one locus and 12 BKT per page ### Saves directly as pdf
pdf("X:/2205_BKT_feral_broodstock_ID/Thometz_scripts/PeakMorph_CE_vs_Amp.pdf", paper = "a4r", width = 11, height = 9)
ProgressBar <- txtProgressBar(min = 0, max = 9, style = 3)
for(i in 1:9){
print(All_LD_data %>%
group_by(uSat_locus, SampleID) %>%
#slice_max(Read_count, n = 8) %>% ### This seems to determine run time (4 ~ 40min, 10 ~ 1.5hrs)
filter(Read_count >= max(Read_count)*0.05) %>%
ggplot(aes(x = Length, y = Read_count)) +
geom_col(fill = "seagreen") +
geom_text(aes(label = Length),
hjust = 0.5,
vjust = 1.5,
colour = "black") +
ylab("Read count") +
xlab("Allele length") +
scale_y_continuous(expand = c(0,0)) +
#scale_x_continuous(breaks = c(0:200), expand = c(0,0)) +
theme_classic() +
facet_wrap_paginate(facets = vars(as.factor(uSat_locus), as.factor(SampleID), as.factor(scores)),
nrow = 3,
ncol = 4,
page = i,
scales = "free"))
Sys.sleep(0.1)
setTxtProgressBar(ProgressBar, i)
}
close(ProgressBar)
dev.off()
### Single page of peak morphs for testing plot changes ###
All_LD_data %>%
#filter(Read_count > 0) %>%
group_by(uSat_locus, SampleID) %>%
#slice_max(Read_count, n = 10) %>%
filter(Read_count >= max(Read_count)*0.05) %>%
ggplot(aes(x = Length, y = Read_count)) +
geom_col(fill = "seagreen") +
geom_text(aes(label = Length), hjust = 0.5, vjust = 1.5, colour = "black") +
ylab("Read count") +
xlab("Allele length") +
scale_y_continuous(expand = c(0,0)) +
#scale_x_continuous(breaks = c(0:200), expand = c(0,0)) +
theme_classic() +
facet_wrap_paginate(facets = vars(as.factor(uSat_locus), as.factor(SampleID), as.factor(scores)),
nrow = 3,
ncol = 4,
page = 49,
scales = "free")