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data_wrangling.R
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# This script prepares the data to be used in plots.R and stats.R scripts
# Please do not run this script
# Author: Mustafa S. Pir
pkgList<-c("data.table","dplyr","tidyr","ggplot2","xlsx","readxl","RColorBrewer","forcats")
new.packages <- pkgList[!(pkgList %in% installed.packages()[,"Package"])]
if(length(new.packages)>0) install.packages(new.packages)
library(data.table)
library(dplyr)
library(tidyr)
library(ggplot2)
library(xlsx)
library(readxl)
library(RColorBrewer)
library(forcats)
options(warn=-1)
# data for stats
data_fhead<-fread("./files/dye_assay1.csv") %>%
select(1,2,4,6)
colnames(data_fhead)[2:4]<-c("Normal","Partial","Abnormal")
data_ftail<-fread("./files/dye_assay1.csv") %>%
select(1,3,5,7)
colnames(data_ftail)[2:4]<-c("Normal","Partial","Abnormal")
wdr31_dyf<-fread("./files/dye_assay2.csv")
wdr31_dyf_head<-fread("./files/dye_assay2.csv") %>%
select(1,2,4,6)
colnames(wdr31_dyf_head)[2:4]<-c("Normal","Partial","Abnormal")
wdr31_dyf_tail<-fread("./files/dye_assay2.csv") %>%
select(1,3,5,7)
colnames(wdr31_dyf_tail)[2:4]<-c("Normal","Partial","Abnormal")
morphology_stat<-read_xlsx("./files/AWB_Cilia_Morphology_Analysis.xlsx")
morphology1_stat<-morphology_stat[1:11,]
morphology2_stat<-morphology_stat[12:16,]
morphology3_stat<-morphology_stat[17:19,]
morphology4_stat<-morphology_stat[20:23,]
ift<-read.xlsx("./files/ift1.xlsx", sheetIndex = 1)
ift$Genotype[ift$Genotype == "wdr-31(tm10423);eldm-1"]<-"wdr-31(tm10423);elmd-1"
ift$Genotype[ift$Genotype == "wdr-31(tm10423);eldm-1;rpi-2 "]<-"wdr-31(tm10423);elmd-1;rpi-2"
ift$Genotype<-factor(ift$Genotype, levels = unique(ift$Genotype))
ift$Marker<-factor(ift$Marker, levels = unique(ift$Marker))
# Data for plots
data_head_plot<-fread("./files/dye_assay1.csv") %>%
select(1,2,4,6) %>%
mutate(Normal = 100*(`Head(Normal)` / (`Head(Normal)` + `Head(Partial)` + `Head(Abnormal)`))) %>%
mutate(Partial = 100*(`Head(Partial)` / (`Head(Normal)` + `Head(Partial)` + `Head(Abnormal)`))) %>%
mutate(Abnormal = 100*(`Head(Abnormal)` / (`Head(Normal)` + `Head(Partial)` + `Head(Abnormal)`))) %>%
pivot_longer(cols = 5:7,
names_to = "Phenotype",
values_to = "Count")
data_tail_plot<-fread("./files/dye_assay1.csv") %>%
select(1,3,5,7) %>%
mutate(Normal = 100*(`Tail(Normal)` / (`Tail(Normal)` + `Tail(Partial)` + `Tail(Abnormal)`))) %>%
mutate(Partial = 100*(`Tail(Partial)` / (`Tail(Normal)` + `Tail(Partial)` + `Tail(Abnormal)`))) %>%
mutate(Abnormal = 100*(`Tail(Abnormal)` / (`Tail(Normal)` + `Tail(Partial)` + `Tail(Abnormal)`))) %>%
pivot_longer(cols = 5:7,
names_to = "Phenotype",
values_to = "Count")
prpls<-brewer.pal(9, "Purples")
data_head_plot$Phenotype<-factor(data_head_plot$Phenotype, levels = c("Normal","Partial","Abnormal"))
data_head_plot$Genotype<-factor(data_head_plot$Genotype, levels = rev(unique(data_head_plot$Genotype)))
data_tail_plot$Phenotype<-factor(data_tail_plot$Phenotype, levels = c("Normal","Partial","Abnormal"))
data_tail_plot$Genotype<-factor(data_tail_plot$Genotype, levels = rev(unique(data_tail_plot$Genotype)))
#Table 2
data2_head_plot<-fread("./files/dye_assay2.csv") %>%
select(1,2,4,6) %>%
mutate(Normal = 100*(`Head(Normal)` / (`Head(Normal)` + `Head(Partial)` + `Head(Abnormal)`))) %>%
mutate(Partial = 100*(`Head(Partial)` / (`Head(Normal)` + `Head(Partial)` + `Head(Abnormal)`))) %>%
mutate(Abnormal = 100*(`Head(Abnormal)` / (`Head(Normal)` + `Head(Partial)` + `Head(Abnormal)`))) %>%
pivot_longer(cols = 5:7,
names_to = "Phenotype",
values_to = "Count")
data2_tail_plot<-fread("./files/dye_assay2.csv") %>%
select(1,3,5,7) %>%
mutate(Normal = 100*(`Tail(Normal)` / (`Tail(Normal)` + `Tail(Partial)` + `Tail(Abnormal)`))) %>%
mutate(Partial = 100*(`Tail(Partial)` / (`Tail(Normal)` + `Tail(Partial)` + `Tail(Abnormal)`))) %>%
mutate(Abnormal = 100*(`Tail(Abnormal)` / (`Tail(Normal)` + `Tail(Partial)` + `Tail(Abnormal)`))) %>%
pivot_longer(cols = 5:7,
names_to = "Phenotype",
values_to = "Count")
data2_head_plot$Phenotype<-factor(data2_head_plot$Phenotype, levels = c("Normal","Partial","Abnormal"))
data2_head_plot$Genotype<-factor(data2_head_plot$Genotype, levels = rev(unique(data2_head_plot$Genotype)))
data2_tail_plot$Phenotype<-factor(data2_tail_plot$Phenotype, levels = c("Normal","Partial","Abnormal"))
data2_tail_plot$Genotype<-factor(data2_tail_plot$Genotype, levels = rev(unique(data2_tail_plot$Genotype)))
# Morphology
morphology<-read_xlsx("./files/AWB_Cilia_Morphology_Analysis.xlsx")
morphology1<-morphology[1:11,]
morphology2<-morphology[12:14,]
morphology3<-morphology[18:20,]
morphology4<-morphology[21:24,]
morphology1<-morphology1 %>%
mutate(Normal = 100*(Normal1 / (Normal1 + Fan1 + `Extra Branch1` + `Backward projection1`))) %>%
mutate(Fan = 100*(Fan1 / (Normal1 + Fan1 + `Extra Branch1` + `Backward projection1`))) %>%
mutate(`Extra Branch` = 100*(`Extra Branch1` / (Normal1 + Fan1 + `Extra Branch1` + `Backward projection1`))) %>%
mutate(`Backward Projection` = 100*(`Backward projection1` / (Normal1 + Fan1 + `Extra Branch1` + `Backward projection1`))) %>%
pivot_longer(cols = 6:9,
names_to = "Phenotype",
values_to = "Count")
colnames(morphology1)[1]<-"Genotype"
morphology1$Phenotype<-factor(morphology1$Phenotype, levels = c("Normal","Fan","Extra Branch","Backward Projection"))
morphology1$Genotype<-factor(morphology1$Genotype, levels = rev(unique(morphology1$Genotype)))
morphology2<-morphology2 %>%
mutate(Normal = 100*(Normal1 / (Normal1 + Fan1 + `Extra Branch1` + `Backward projection1`))) %>%
mutate(Fan = 100*(Fan1 / (Normal1 + Fan1 + `Extra Branch1` + `Backward projection1`))) %>%
mutate(`Extra Branch` = 100*(`Extra Branch1` / (Normal1 + Fan1 + `Extra Branch1` + `Backward projection1`))) %>%
mutate(`Backward Projection` = 100*(`Backward projection1` / (Normal1 + Fan1 + `Extra Branch1` + `Backward projection1`))) %>%
pivot_longer(cols = 6:9,
names_to = "Phenotype",
values_to = "Count")
colnames(morphology2)[1]<-"Genotype"
morphology2$Phenotype<-factor(morphology2$Phenotype, levels = c("Normal","Fan","Extra Branch","Backward Projection"))
morphology2$Genotype<-factor(morphology2$Genotype, levels = rev(unique(morphology2$Genotype)))
# AWB length
length<-read.xlsx("./files/AWB_Cilia_Lenght_new.xlsx", sheetIndex = 1)
length<-length[1:57,1:23]
colnames(length)<-length[1,]
for (i in seq(2,22,2)){
colnames(length)[i+1]<-colnames(length)[i]
}
length<-length[-1,]
length_short<-select(length, seq(3,23,2))
length_short<-length_short[-1,]
length_long<-select(length, seq(2,22,2))
length_long<-length_long[-1,]
length_short<-sapply(length_short, as.numeric)
length_long<-sapply(length_long, as.numeric)
length_short1<-pivot_longer(as_tibble(length_short), cols = 1:11, names_to = "Genotype", values_to = "Length")
length_long1<-pivot_longer(as_tibble(length_long), cols = 1:11, names_to = "Genotype", values_to = "Length")
length_short1<-length_short1[!is.na(length_short1$Length),]
length_long1<-length_long1[!is.na(length_long1$Length),]
length_short1$Genotype<-factor(length_short1$Genotype, levels = rev(unique(length_short1$Genotype)))
length_long1$Genotype<-factor(length_long1$Genotype, levels = rev(unique(length_long1$Genotype)))
lengthx<-read.xlsx("./files/AWB_Cilia_Lenght_new.xlsx", sheetIndex = 2)
lengthx<-lengthx[1:52,1:7]
colnames(lengthx)<-lengthx[1,]
for (i in seq(2,6,2)){
colnames(lengthx)[i+1]<-colnames(lengthx)[i]
}
lengthx<-lengthx[-1,]
lengthx_short<-select(lengthx, seq(3,7,2))
lengthx_short<-lengthx_short[-1,]
lengthx_long<-select(lengthx, seq(2,6,2))
lengthx_long<-lengthx_long[-1,]
lengthx_short<-sapply(lengthx_short, as.numeric)
lengthx_long<-sapply(lengthx_long, as.numeric)
lengthx_short1<-pivot_longer(as_tibble(lengthx_short), cols = 1:3, names_to = "Genotype", values_to = "Length")
lengthx_long1<-pivot_longer(as_tibble(lengthx_long), cols = 1:3, names_to = "Genotype", values_to = "Length")
lengthx_short1<-lengthx_short1[!is.na(lengthx_short1$Length),]
lengthx_long1<-lengthx_long1[!is.na(lengthx_long1$Length),]
lengthx_short1$Genotype<-factor(lengthx_short1$Genotype, levels = unique(lengthx_short1$Genotype))
lengthx_long1$Genotype<-factor(lengthx_long1$Genotype, levels = unique(lengthx_long1$Genotype))
# ASER length
gcy_length<-read.xlsx("./files/gcy-5_and_srb-6p.xlsx", sheetIndex = 1, check.names = FALSE)
gcy_length<-gcy_length[1:86,2:9]
gcy_length<-sapply(gcy_length, as.numeric)
gcy_length1<-pivot_longer(as_tibble(gcy_length), cols = 1:8, names_to = "Genotype", values_to = "Length")
gcy_length1<-gcy_length1[!is.na(gcy_length1$Length),]
gcy_length1$Genotype<-factor(gcy_length1$Genotype, levels = unique(gcy_length1$Genotype))
gcy_length1$Genotype<-fct_rev(gcy_length1$Genotype)
# PHA/PHB length
srb6_length<-read.xlsx("./files/gcy-5_and_srb-6p.xlsx", sheetIndex = 2, check.names = FALSE)
srb6_length<-srb6_length[,2:9]
srb6_length<-sapply(srb6_length, as.numeric)
srb6_length1<-pivot_longer(as_tibble(srb6_length), cols = 1:8, names_to = "Genotype", values_to = "Length")
srb6_length1<-srb6_length1[!is.na(srb6_length1$Length),]
srb6_length1$Genotype<-factor(srb6_length1$Genotype, levels = unique(srb6_length1$Genotype))
srb6_length1$Genotype<-fct_rev(srb6_length1$Genotype)
# AWB length 2
awb_length<-read.xlsx("./files/awb_length.xlsx", sheetIndex = 1)
awb_length<-awb_length[1:57,1:23]
colnames(awb_length)<-awb_length[1,]
for (i in seq(2,22,2)){
colnames(awb_length)[i+1]<-colnames(awb_length)[i]
}
awb_length<-awb_length[-1,]
awb_length<-select(awb_length, seq(3,23,2))
awb_length<-awb_length[-1,]
awb_length<-sapply(awb_length, as.numeric)
awb_length<-awb_length[,c(-6,-10,-11)]
awb_length1<-pivot_longer(as_tibble(awb_length), cols = 1:8, names_to = "Genotype", values_to = "length")
awb_length1<-awb_length1[!is.na(awb_length1$length),]
awb_length1$Genotype<-factor(awb_length1$Genotype, levels = rev(unique(awb_length1$Genotype)))
# New IFT plots
ifts<-list()
for (i in 1:4){
iftx<-suppressMessages(read_xlsx("files/IFT_Velocity_IFT_frequency_edited.xlsx", sheet = i))
iftx<-iftx[-1,]
ift_long<-pivot_longer(iftx, everything(), names_to = "Genotype", values_to = "Speed1", values_drop_na = TRUE)
ifty<-suppressMessages(read_xlsx("files/IFT_Velocity_IFT_frequency_edited.xlsx", sheet = i, skip = 1))
ift_long_2<-pivot_longer(ifty, everything(), names_to = "Part", values_to = "Speed", values_drop_na = TRUE)
ift_all<-cbind(ift_long, ift_long_2)
ift_all<-ift_all %>% select(-Speed1)
ift_all[,1:2]<-as.data.frame(lapply(ift_all[,1:2], function(x) gsub("\\.\\.\\.[0-9]+", "", x)))
ifts[[i]]<-ift_all
}
# New IFT file
iftzn <- suppressMessages(read_xlsx("./files/IFT_particles_NEW_revision2.xlsx", sheet = 1))
iftzn <- iftzn[c(-1, -2), -1]
iftzn_long <- pivot_longer(iftzn, everything(), names_to = "Genotype2", values_to = "Speed1", values_drop_na = TRUE)
iftzn2 <- suppressMessages(read_xlsx("files/IFT_particles_NEW_revision2.xlsx", sheet = 1, skip = 1))
iftzn2 <- iftzn2[-1, -1]
iftzn_long_2 <- pivot_longer(iftzn2, everything(), names_to = "Genotype", values_to = "Speed2", values_drop_na = TRUE)
iftzn3 <- suppressMessages(read_xlsx("files/IFT_particles_NEW_revision2.xlsx", sheet = 1, skip = 2))
iftzn3 <- iftzn3[, -1]
iftzn_long_3 <- pivot_longer(iftzn3, everything(), names_to = "Part", values_to = "Speed", values_drop_na = TRUE)
iftzn_all <- cbind(iftzn_long, iftzn_long_2, iftzn_long_3)
iftzn_all <- iftzn_all %>% select(-Speed1, -Speed2)
iftzn_all[, 1:3] <- as.data.frame(lapply(iftzn_all[, 1:3], function(x) gsub("\\.\\.\\.[0-9]+", "", x)))
iftzn_all$Speed<-iftzn_all$Speed / (0.333 * 90)
iftzn_all_stat<-iftzn_all
colnames(iftzn_all_stat)<-c("Marker","Genotype","IFT","Number.of.Particles")
# Morphology 2
morphologyn<-read_xlsx("./files/AWB_cilia_morphology_NEW_revision.xlsx")
morphologyn1<-morphologyn %>%
mutate(Normal = 100*(Normal1 / (Normal1 + Fan1 + `Extra Branch1` + `Backward Projection1`))) %>%
mutate(Fan = 100*(Fan1 / (Normal1 + Fan1 + `Extra Branch1` + `Backward Projection1`))) %>%
mutate(`Extra Branch` = 100*(`Extra Branch1` / (Normal1 + Fan1 + `Extra Branch1` + `Backward Projection1`))) %>%
mutate(`Backward Projection` = 100*(`Backward Projection1` / (Normal1 + Fan1 + `Extra Branch1` + `Backward Projection1`))) %>%
pivot_longer(cols = 6:9,
names_to = "Phenotype",
values_to = "Count")
colnames(morphologyn1)[1]<-"Genotype"
morphologyn1$Phenotype<-factor(morphologyn1$Phenotype, levels = c("Normal","Fan","Extra Branch","Backward Projection"))
morphologyn1$Genotype<-factor(morphologyn1$Genotype, levels = rev(unique(morphologyn1$Genotype)))
morphologym<-read_xlsx("./files/AWB_cilia_morphology_NEW_revision.xlsx", sheet = 2)
morphologym1<-morphologym %>%
mutate(Normal = 100*(Normal1 / (Normal1 + Fan1 + `Extra Branch1` + `Backward Projection1`))) %>%
mutate(Fan = 100*(Fan1 / (Normal1 + Fan1 + `Extra Branch1` + `Backward Projection1`))) %>%
mutate(`Extra Branch` = 100*(`Extra Branch1` / (Normal1 + Fan1 + `Extra Branch1` + `Backward Projection1`))) %>%
mutate(`Backward Projection` = 100*(`Backward Projection1` / (Normal1 + Fan1 + `Extra Branch1` + `Backward Projection1`))) %>%
pivot_longer(cols = 6:9,
names_to = "Phenotype",
values_to = "Count")
colnames(morphologym1)[1]<-"Genotype"
morphologym1$Phenotype<-factor(morphologym1$Phenotype, levels = c("Normal","Fan","Extra Branch","Backward Projection"))
morphologym1$Genotype<-factor(morphologym1$Genotype, levels = rev(unique(morphologym1$Genotype)))
morphologyn_stat<-read_xlsx("./files/AWB_cilia_morphology_NEW_revision.xlsx", sheet = 1)
morphologym_stat<-read_xlsx("./files/AWB_cilia_morphology_NEW_revision.xlsx", sheet = 2)