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REV_meta_statistics.Rmd
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REV_meta_statistics.Rmd
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---
title: "Analysis of genotype-phenotype associations in BBS"
author: "Veronika Niederlova"
date: '`r format(Sys.time(), "%d %m, %Y")`'
output:
html_document: default
---
```{r setup, include=FALSE}
library(kableExtra)
library(dplyr)
library(readr)
library(FSA)
library(knitr)
library(ggplot2)
library(cowplot)
library(here)
knitr::opts_chunk$set(echo = FALSE, warning=FALSE, message=FALSE)
options(knitr.table.format = "html")
## import of data
update8forR <- read_delim(here("data/update8forR.csv"),
";", escape_double = FALSE, col_types = cols(CI = col_integer(),
DD = col_integer(), HEART = col_integer(),
LIV = col_integer(), OBE = col_integer(),
PD = col_integer(), RD = col_integer(),
REN = col_integer(), REP = col_integer(),
agegroup = col_factor(levels = c("age1",
"age2", "age3", "age4")), allwow = col_factor(levels = c("yes",
"no")), canonwobbs1 = col_factor(levels = c("yes",
"no")), gene = col_factor(levels = c("BBS1",
"BBS2", "BBS3", "BBS4", "BBS5",
"BBS6", "BBS7", "BBS8", "BBS9",
"BBS10", "BBS11", "BBS12", "BBS13",
"BBS14", "BBS16", "BBS17", "BBS18",
"BBS19", "BBS20", "BBS21")),
ggroup = col_factor(levels = c("bbs3",
"other", "bbsome", "chaps")),
muttype = col_factor(levels = c("MSMSandMScLOF",
"cLOF")), sex = col_factor(levels = c("F",
"M")), si = col_number(), year = col_number(), sisubset = col_factor(levels = c("yes",
"no")), m390r = col_factor(levels = c("yes",
"no"))), trim_ws = TRUE)
## functions
summary_analysis <- function(subset_x) {
# number of patients
df_x = data.frame(count = nrow(subset_x))
# sex
df_x$sex = sum(is.na(subset_x$sex)==FALSE)
df_x$male = sum(subset_x$sex=="M", na.rm = TRUE)
df_x$female = sum(subset_x$sex=="F", na.rm = TRUE)
df_x$per_male = df_x$male/df_x$sex*100
df_x$per_female = df_x$female/df_x$sex*100
# age
df_x$age = sum(is.na(subset_x$agegroup)==FALSE)
df_x$age1 = sum(subset_x$agegroup=="age1", na.rm = TRUE)
df_x$age2 = sum(subset_x$agegroup=="age2", na.rm = TRUE)
df_x$age3 = sum(subset_x$agegroup=="age3", na.rm = TRUE)
df_x$age4 = sum(subset_x$agegroup=="age4", na.rm = TRUE)
df_x$per_age1 = df_x$age1/df_x$age*100
df_x$per_age2 = df_x$age2/df_x$age*100
df_x$per_age3 = df_x$age3/df_x$age*100
df_x$per_age4 = df_x$age4/df_x$age*100
# mutations
df_x$mut = sum(is.na(subset_x$muttype)==FALSE)
df_x$cLOF = sum(subset_x$muttype=="cLOF", na.rm = TRUE)
df_x$MS = sum(subset_x$muttype=="MSMSandMScLOF", na.rm = TRUE)
df_x$per_cLOF = df_x$cLOF/df_x$mut*100
df_x$per_MS = df_x$MS/df_x$mut*100
colnames(df_x) <- c("Number of patients",
"Sex information available",
"Number of male patients",
"Number of female patients",
"% of male",
"% of female",
"Age information available",
"Number of age 0-9 years",
"Number of age 10-19 years",
"Number of age 20-39 years",
"Number of age 40+ years",
"% of age 0-9 years",
"% of age 10-19 years",
"% of age 20-39 years",
"% of age 40+ years",
"Mutation type avaiable",
"Number of cLOF",
"Number of missense",
"% of cLOF mutations",
"% of missense mutations")
df_x
}
phenotype_analysis <- function(df) {
df_x <- data.frame( count = sum(is.na(df)==FALSE),
yes = sum(df, na.rm = TRUE),
no = sum(df=="0", na.rm = TRUE),
na = sum(is.na(df)))
# penetrance of symptoms in group
df_x$percent = round(df_x$yes/df_x$count*100,digits=2)
df_x
}
phenotypes <- c("RD","OBE","PD","CI", "REP","REN","HEART", "LIV","DD")
one_vs_all_other <- function(subset_one,subset_all,phenotype_number,sidak_values_vector) {
df<- data.frame(data1 = deparse(substitute(subset_one)),
data2 = deparse(substitute(subset_all)),
phenotype = phenotypes[phenotype_number-6],
p_val = fisher.test(matrix( c(
phenotype_analysis(subset_one[phenotype_number])$no,
(phenotype_analysis(subset_all[phenotype_number])$no-phenotype_analysis(subset_one[phenotype_number])$no),
phenotype_analysis(subset_one[phenotype_number])$yes,
(phenotype_analysis(subset_all[phenotype_number])$yes-phenotype_analysis(subset_one[phenotype_number])$yes)),
byrow = FALSE, ncol = 2),workspace = 2e8)$p.value)
is_sidak <- function(i,sidak_values_vector) {
if(i<sidak_values_vector[5]){
symbol <- "****"
} else { if (i<sidak_values_vector[4]){
symbol <- "***"
} else { if (i<sidak_values_vector[3]){
symbol <- "**"
} else { if (i<sidak_values_vector[2]){
symbol <- "*"
} else { if (i<sidak_values_vector[1]){
symbol <- "#"
} else { symbol <- ""}
}
}
}
}
symbol
}
df$sidak <- is_sidak(df$p_val,sidak_values_vector)
df
}
plot_histogram <- function(df, group_name, label_y){
label_y_no <- theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_blank(),
axis.title.x = element_text(size = 11),
axis.text.y = element_text(color = "black", size = 11),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(3,3,3,16),"pt"))
label_y_yes <- theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_text(size = 11),
axis.text.y = element_text(color = "black", size = 11),
axis.title.y = element_text(vjust = 2.5, size = 11),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.margin=unit(c(3,3,3,3),"pt"))
if(label_y==T){label_y <- label_y_yes}else{label_y <- label_y_no}
qplot(df, geom="histogram",
binwidth = 0.1,
ylab = paste(group_name, "\n","n = ",length(df)),
xlab = "Syndromic score",
fill=I("grey"),
col=I("darkgrey"),
alpha=I(.2)
) +
coord_flip() +
label_y +
scale_x_continuous(breaks=c(0.2,0.4,0.6,0.8,1), limits = c(0,1.2)) +
geom_vline(aes(xintercept = mean(df)),col='black',size=1.5, linetype = "dashed") +
scale_y_continuous(expand = c(0, 0))
}
plot_bars<- function(vector_groups,vector_group_names,phenotype_n){
for(i in c(1:length(vector_groups))){
if(i==1){df <- data.frame()
table <- c()}
pa <- phenotype_analysis(get(vector_groups[i])[,phenotype_n])
df <- rbind(df, data.frame(penetrance = pa$percent,
group = vector_group_names[i],
lower = qbeta(0.025,pa$yes + 1, pa$no + 1),
upper = qbeta(0.975, pa$yes + 1, pa$no + 1) ))
table <- c(table,pa$yes,pa$no)
}
p_val <- fisher.test(matrix(table, ncol = length(vector_groups), byrow = FALSE), workspace = 2e8)$p.value
df_plot <- df
p<- ggplot(data = df_plot, aes(x = factor(group,levels = vector_group_names), y = penetrance)) +
geom_bar(stat="identity", colour="black", fill="grey") + labs(title=phenotypes[phenotype_n-6]) + xlab(paste("p-value = ",format(p_val,digits = 4))) +
geom_errorbar(aes(ymin = lower*100, ymax = upper*100), width = .2) +
theme(axis.text = element_text(size = 10),
axis.title.x = element_text(size = 9),
axis.title.y = element_text(size = 10),
axis.line.x = element_blank(),
axis.text.x = element_text(angle = -45, hjust = 0, vjust = .3),
plot.title = element_text(hjust = 0.5)
) +
scale_y_continuous(breaks = c(0,20,40,60,80,100), limits = c(0,100))
p
}
plot_bars_wo_pval <- function(vector_groups,vector_group_names,phenotype_n){
for(i in c(1:length(vector_groups))){
if(i==1){df <- data.frame()
table <- c()}
pa <- phenotype_analysis(get(vector_groups[i])[,phenotype_n])
df <- rbind(df, data.frame(penetrance = pa$percent,
group = vector_group_names[i],
lower = qbeta(0.025,pa$yes + 1, pa$no + 1),
upper = qbeta(0.975, pa$yes + 1, pa$no + 1) ))
table <- c(table,pa$yes,pa$no)
}
df_plot <- df
p<- ggplot(data = df_plot, aes(x = factor(group,levels = vector_group_names), y = penetrance)) +
geom_bar(stat="identity", colour="black", fill="grey") + labs(title=phenotypes[phenotype_n-6]) +
geom_errorbar(aes(ymin = lower*100, ymax = upper*100), width = .2) +
theme(axis.text = element_text(size = 10),
axis.title.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = 10),
axis.line.x = element_blank(),
plot.title = element_text(hjust = 0.5, size = 10)
) +
scale_y_continuous(breaks = c(0,20,40,60,80,100), limits = c(0,100))
p
}
number_of_patients <- function(phenotype_n,vector_groups){
group_numbers <- c()
for(j in c(1:length(vector_groups))){
pa <- phenotype_analysis(get(vector_groups[j])[,phenotype_n])
group_numbers <- c(group_numbers, pa$count)
}
return(paste(phenotypes[phenotype_n-6],"-", paste(group_numbers,collapse=", ")))
}
calculate_sidak <- function(sidak_n){
df <- data.frame(orig_p = c(0.1,0.05,0.01,0.001,0.0001)) %>%
mutate(sidak_p = 1-((1-orig_p)^(1/sidak_n)))
return(df$sidak_p)
}
sidak_table <- function(sidak_n){
df_sidak <- data.frame(
symbol = c("#","*","**","***","****"), # symbols used for p-value significance visualization
orig_p = paste0("< ",c(0.1,0.05,0.01,0.001,0.0001)),
sidak_p = paste0("< ",format(calculate_sidak(sidak_n),digits = 3)))
names(df_sidak) <- c("Symbol","Orig. p-value","Sidak p-value")
kable(df_sidak, format= "html") %>%
kable_styling(full_width = F, font_size = 9,bootstrap_options = c("striped", "hover", "condensed", "responsive"))
}
```
## Summary of the data and descriptive analysis
The data file contains the following variables:
```{r}
df <- data.frame(variables=colnames(update8forR),description = c(
"Source study that can be find in the manuscript.",
"The ID or another information that can be used for identification of the record within the study.",
"BBS gene that contain causative mutation.",
"Sex of patient, blanks mean not available.",
"Age groups according to age, blanks mean not available.",
"Specific age, if available.",
"Retinal dystrophy, 1 = present, 0 = not present, blank = not available",
"Obesity, 1 = present, 0 = not present, blank = not available",
"Polydactyly, 1 = present, 0 = not present, blank = not available",
"Cognitive impairment, 1 = present, 0 = not present, blank = not available",
"Reproductive system anomalies, 1 = present, 0 = not present, blank = not available",
"Renal anomalies, 1 = present, 0 = not present, blank = not available",
"Heart anomalies, 1 = present, 0 = not present, blank = not available",
"Liver anomalies, 1 = present, 0 = not present, blank = not available",
"Developmental delay, 1 = present, 0 = not present, blank = not available",
"Type of mutation, MSMSandMScLOF meaning missense mutation in at least one allele, cLOF meaning assumed complete loss of function mutations in both alleles",
"Severity score subset, yes if information about RD, OBE, PD, CI and REN is stated, no if any of these contain blanks.",
"Group of genes according to their function, bbs3 for mutations in BBS3, bbsome for mutations in BBS1, BBS2, BBS4, BBS5, BBS7, BBS8, BBS9 and BBS18, chaps for mutations in BBS6,BBS10 and BBS12, other for mutations in BBS11, BBS13, BBS14, BBS16, BBS17, BBS19, BBS20 and BBS21.",
"Yes for canonical BBS genes BBS1-10, BBS12 and BBS18, no for BBS11, BBS13, BBS14, BBS16, BBS17, BBS19, BBS20 and BBS21.",
"Yes for canonical BBS genes other than BBS1 (BBS2-10, BBS12 and BBS18), no for BBS1, BBS11, BBS13, BBS14, BBS16, BBS17, BBS19, BBS20 and BBS21.",
"syndromic score calculated as the sum of RD, OBE, PD, CI and REN divided by 5.",
"Age groups similar to variable age group renamed as follows: age1 = 0-9, age2 = 10-19, age3 = 20-39, age4 = 40+.",
"Year of publication",
"Indicates if the particular mutation is BBS1 M390R/M390R"))
kable(df, format = "html") %>%
kable_styling(full_width = F, font_size = 9,bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```
Now let's calculate the penetrance of mutations in particular genes and particular phenotypes within the whole dataset. These are used in `Figure S2A` and `Figure S2E`.
```{r fig.width = 9, fig.height = 4.5, fig.align = "center", out.width = '60%'}
## penetrance of mutations in BBS genes
df <- data.frame(
gene = c("BBS1","BBS2","BBS4","BBS5","BBS7","BBS8","BBS9","BBS10","BBS6","BBS12","BBS3","other"),
count = c(
nrow(subset(update8forR,gene=="BBS1")),
nrow(subset(update8forR,gene=="BBS2")),
nrow(subset(update8forR,gene=="BBS4")),
nrow(subset(update8forR,gene=="BBS5")),
nrow(subset(update8forR,gene=="BBS7")),
nrow(subset(update8forR,gene=="BBS8")),
nrow(subset(update8forR,gene=="BBS9")),
nrow(subset(update8forR,gene=="BBS10")),
nrow(subset(update8forR,gene=="BBS6")),
nrow(subset(update8forR,gene=="BBS12")),
nrow(subset(update8forR,gene=="BBS3")),
nrow(subset(update8forR,
gene=="BBS11"|
gene=="BBS13"|
gene=="BBS14"|
gene=="BBS16"|
gene=="BBS17"|
gene=="BBS18"|
gene=="BBS19"|
gene=="BBS20"|
gene=="BBS21")))
)
df$gene = factor(df$gene,levels = c("BBS1","BBS2","BBS4","BBS5","BBS7","BBS8","BBS9","BBS10","BBS6","BBS12","BBS3","other"))
df$percentage = round(df$count/nrow(update8forR)*100,digits = 2)
kable(df, format = "html") %>%
kable_styling(full_width = F, font_size = 9,bootstrap_options = c("striped", "hover", "condensed", "responsive"))
bp<- ggplot(df, aes(x="", y=count, fill=gene)) +
geom_bar(width = 2, stat = "identity",color='black') + scale_fill_manual(values=c("#e5edf8ff","#e5edf8ff","#e5edf8ff","#e5edf8ff","#e5edf8ff","#e5edf8ff","#e5edf8ff", "#f2e984ff","#f2e984ff","#f2e984ff", "#facfcfff", "grey"))
y.breaks <- cumsum(df$count)
y.breaks[1] = 70
y.breaks[2] = 155
y.breaks[3] = 215
y.breaks[4] = 275
y.breaks[5] = 365
y.breaks[6] = 450
y.breaks[7] = 475
y.breaks[8] = 500
y.breaks[9] = 530
y.breaks[10] = 575
y.breaks[11] = 635
y.breaks[12] = 790
pie2 <- bp + coord_polar("y", start = pi/3, direction = -1) +
theme(axis.ticks=element_blank(),
axis.line = element_blank(),
axis.title=element_blank(),
axis.text.y=element_blank(),
axis.text.x=element_text(color='black')) +
scale_y_continuous(
breaks=y.breaks,
labels=rev(df$gene))
## penetrance of phenotypes
penetrance <- c( phenotype_analysis(update8forR$RD)$percent,
phenotype_analysis(update8forR$OBE)$percent,
phenotype_analysis(update8forR$DD)$percent,
phenotype_analysis(update8forR$PD)$percent,
phenotype_analysis(update8forR$CI)$percent,
phenotype_analysis(update8forR$REP)$percent,
phenotype_analysis(update8forR$REN)$percent,
phenotype_analysis(update8forR$LIV)$percent,
phenotype_analysis(update8forR$HEART)$percent
)
## test of significance
penetrance_table = matrix(c(
phenotype_analysis(update8forR$RD)$no,
phenotype_analysis(update8forR$OBE)$no,
phenotype_analysis(update8forR$PD)$no,
phenotype_analysis(update8forR$CI)$no,
phenotype_analysis(update8forR$REP)$no,
phenotype_analysis(update8forR$REN)$no,
phenotype_analysis(update8forR$HEART)$no,
phenotype_analysis(update8forR$LIV)$no,
phenotype_analysis(update8forR$DD)$no,
phenotype_analysis(update8forR$RD)$yes,
phenotype_analysis(update8forR$OBE)$yes,
phenotype_analysis(update8forR$PD)$yes,
phenotype_analysis(update8forR$CI)$yes,
phenotype_analysis(update8forR$REP)$yes,
phenotype_analysis(update8forR$REN)$yes,
phenotype_analysis(update8forR$HEART)$yes,
phenotype_analysis(update8forR$LIV)$yes,
phenotype_analysis(update8forR$DD)$yes
), ncol = 9, byrow = TRUE)
chisq.test(penetrance_table)
chisq.test(penetrance_table)$p.value
pie2
```
```{r fig.width = 6, fig.align = "center", out.width = '40%'}
barplot(penetrance, ylim = c(0,100), ylab = "Penetrance of symptoms (%)", las = 1, names.arg = c("RD","OBE","DD","PD","CI","REP","REN","LIV","HRT"), main = "Penetrance of BBS symptoms",
xlab = paste("chisq.test p-value = ", format(chisq.test(penetrance_table)$p.value,digits = 4)))
```
Now, let's analyze the whole dataset in terms of sex, age and mutation types. This analysis will also be applied to subsets of patients grouped according to the functional groups of BBS genes in which the patients have their causative mutations. These information are provided in Figures S2B-D.
Summary analysis in the whole dataset for `Figure S2B`:
```{r}
subset_bbsome <- subset(update8forR,update8forR$ggroup=="bbsome")
subset_bbs3 <- subset(update8forR,update8forR$gene=="BBS3")
subset_chaperonins <- subset(update8forR,update8forR$ggroup=="chaps")
subset_other <- subset(update8forR,update8forR$allwow=="no")
## Summary analysis in the whole dataset
whole_dataset_analysis <- as.data.frame(round(t(rbind(
all = summary_analysis(update8forR),
bbsome = summary_analysis(subset_bbsome),
bbs3 = summary_analysis(subset_bbs3),
chaperonins = summary_analysis(subset_chaperonins),
other = summary_analysis(subset_other)
))),digits=1)
kable(whole_dataset_analysis) %>%
kable_styling(full_width = F, font_size = 9,bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```
Summary analysis in the bbsome group for `Figure S2C`:
```{r}
subset_bbs1 <- subset(update8forR,update8forR$gene=="BBS1")
subset_bbs2 <- subset(update8forR,update8forR$gene=="BBS2")
subset_bbs4 <- subset(update8forR,update8forR$gene=="BBS4")
subset_bbs5 <- subset(update8forR,update8forR$gene=="BBS5")
subset_bbs7 <- subset(update8forR,update8forR$gene=="BBS7")
subset_bbs8 <- subset(update8forR,update8forR$gene=="BBS8")
subset_bbs9 <- subset(update8forR,update8forR$gene=="BBS9")
subset_bbs18 <- subset(update8forR,update8forR$gene=="BBS18")
## Summary analysis in the whole dataset
bbsome_analysis <- as.data.frame(round(t(rbind(
bbsome = summary_analysis(subset_bbsome),
bbs1 = summary_analysis(subset_bbs1),
bbs2 = summary_analysis(subset_bbs2),
bbs4 = summary_analysis(subset_bbs4),
bbs5 = summary_analysis(subset_bbs5),
bbs7 = summary_analysis(subset_bbs7),
bbs8 = summary_analysis(subset_bbs8),
bbs9 = summary_analysis(subset_bbs9),
bbs18 = summary_analysis(subset_bbs18)
))),digits=1)
kable(bbsome_analysis, format = "html") %>%
kable_styling(full_width = F, font_size = 9,bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```
Summary analysis in the chaperonins group for `Figure S2D`:
```{r}
subset_bbs6 <- subset(update8forR,update8forR$gene=="BBS6")
subset_bbs10 <- subset(update8forR,update8forR$gene=="BBS10")
subset_bbs12 <- subset(update8forR,update8forR$gene=="BBS12")
## Summary analysis in the whole dataset
chaperonin_analysis <- as.data.frame(round(t(rbind(
all_chaperonins = summary_analysis(subset_chaperonins),
bbs6 = summary_analysis(subset_bbs6),
bbs10 = summary_analysis(subset_bbs10),
bbs12 = summary_analysis(subset_bbs12)
))),digits=1)
kable(chaperonin_analysis, format = "html") %>%
kable_styling(full_width = F, font_size = 9,bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```
## Syndromic score
For the analyses in Figure 1 we use the syndromic score, which is defined in patients with reported presence/absence of the five primary phenotypic criteria (RD,OBE,PD,CI,REN). Patients with blanks in any of these categories are not used for this statistics. Let's define the syndromic score subset called 'set and analyze the penetrance of the symptoms and penetrance of mutations in BBS genes in this 'set, as used in `Figure 1B` and `Figure 1C`.
```{r fig.width = 9, fig.height=4.5, fig.align = "center", out.width = '60%'}
subset_sscore <- subset(update8forR, sisubset=="yes")
df <- data.frame(
gene = c("BBS1","BBS2","BBS4","BBS5","BBS7","BBS8","BBS9","BBS10","BBS6","BBS12","BBS3","other"),
count = c(
nrow(subset(subset_sscore,gene=="BBS1")),
nrow(subset(subset_sscore,gene=="BBS2")),
nrow(subset(subset_sscore,gene=="BBS4")),
nrow(subset(subset_sscore,gene=="BBS5")),
nrow(subset(subset_sscore,gene=="BBS7")),
nrow(subset(subset_sscore,gene=="BBS8")),
nrow(subset(subset_sscore,gene=="BBS9")),
nrow(subset(subset_sscore,gene=="BBS10")),
nrow(subset(subset_sscore,gene=="BBS6")),
nrow(subset(subset_sscore,gene=="BBS12")),
nrow(subset(subset_sscore,gene=="BBS3")),
nrow(subset(subset_sscore,
gene=="BBS11"|
gene=="BBS13"|
gene=="BBS14"|
gene=="BBS16"|
gene=="BBS17"|
gene=="BBS18"|
gene=="BBS19"|
gene=="BBS20"|
gene=="BBS21")))
)
df$gene = factor(df$gene,levels = c("BBS1","BBS2","BBS4","BBS5","BBS7","BBS8","BBS9","BBS10","BBS6","BBS12","BBS3","other"))
df$percentage = round(df$count/nrow(subset_sscore)*100,digits = 2)
kable(df, format = "html") %>%
kable_styling(full_width = F, font_size = 9,bootstrap_options = c("striped", "hover", "condensed", "responsive"))
bp<- ggplot(df, aes(x="", y=count, fill=gene)) +
geom_bar(width = 2, stat = "identity",color='black') + scale_fill_manual(values=c("#e5edf8ff","#e5edf8ff","#e5edf8ff","#e5edf8ff","#e5edf8ff","#e5edf8ff","#e5edf8ff", "#f2e984ff","#f2e984ff","#f2e984ff", "#facfcfff", "grey")) + scale_color_manual(values = c("darkblue", "darkblue","darkblue","darkblue","darkblue","darkblue","darkblue","orange","orange","orange","darkred","black"))
y.breaks <- cumsum(df$count)
y.breaks[1] = 10
y.breaks[2] = 40
y.breaks[3] = 70
y.breaks[4] = 95
y.breaks[5] = 150
y.breaks[6] = 195
y.breaks[7] = 205
y.breaks[8] = 225
y.breaks[9] = 245
y.breaks[10] = 265
y.breaks[11] = 290
y.breaks[12] = 370
pie2 <- bp + coord_polar("y", start = pi/3, direction = -1) +
theme(axis.ticks=element_blank(),
axis.line = element_blank(),
axis.title=element_blank(),
axis.text.y=element_blank(),
axis.text.x=element_text(color='black')) +
scale_y_continuous(
breaks=y.breaks,
labels=rev(df$gene))
## penetrance of phenotypes
penetrance <- c( phenotype_analysis(subset_sscore$RD)$percent,
phenotype_analysis(subset_sscore$OBE)$percent,
phenotype_analysis(subset_sscore$PD)$percent,
phenotype_analysis(subset_sscore$CI)$percent,
phenotype_analysis(subset_sscore$REN)$percent
)
## test of significance
penetrance_table = matrix(c(
phenotype_analysis(subset_sscore$RD)$no,
phenotype_analysis(subset_sscore$OBE)$no,
phenotype_analysis(subset_sscore$PD)$no,
phenotype_analysis(subset_sscore$CI)$no,
phenotype_analysis(subset_sscore$REN)$no,
phenotype_analysis(subset_sscore$RD)$yes,
phenotype_analysis(subset_sscore$OBE)$yes,
phenotype_analysis(subset_sscore$PD)$yes,
phenotype_analysis(subset_sscore$CI)$yes,
phenotype_analysis(subset_sscore$REN)$yes
), ncol = 5, byrow = TRUE)
chisq.test(penetrance_table)
chisq.test(penetrance_table)$p.value
pie2
```
```{r fig.width = 6, fig.align = "center", out.width = '40%'}
barplot(penetrance, ylim = c(0,100), ylab = "Penetrance of symptoms (%)", names.arg = c("RD","OBE","PD","CI","REN"), main = "Penetrance of BBS symptoms", las = 1,
xlab = paste("chisq.test p-value = ",format(chisq.test(penetrance_table)$p.value,digits = 4)))
```
Summary analysis of the syndromic score in the 'set follows. This is used in `Figure S3B`.
```{r fig.width=1.5, fig.height=2.75}
subset_sscore_bbsome <- subset(subset_sscore,subset_sscore$ggroup=="bbsome")
subset_sscore_bbs3 <- subset(subset_sscore,subset_sscore$gene=="BBS3")
subset_sscore_chaperonins <- subset(subset_sscore,subset_sscore$ggroup=="chaps")
subset_sscore_other <- subset(subset_sscore,subset_sscore$allwow=="no")
sscore_dataset_analysis <- as.data.frame(round(t(rbind(
set = summary_analysis(subset_sscore),
set_bbsome = summary_analysis(subset_sscore_bbsome),
set_bbs3 = summary_analysis(subset_sscore_bbs3),
set_chaperonins = summary_analysis(subset_sscore_chaperonins),
set_other = summary_analysis(subset_sscore_other)
))),digits=1)
kable(sscore_dataset_analysis, format = "html") %>%
kable_styling(full_width = F, font_size = 9,bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```
Histogram plots showing the distribution of syndromic score for `Figure 1D` and `Figure S3A`.
```{r fig.width=1.5, fig.height=2.75}
plot_histogram(subset_sscore$si, "All data", T)
subset_sscore_male <- subset(subset_sscore,subset_sscore$sex=="M")
subset_sscore_female <- subset(subset_sscore,subset_sscore$sex=="F")
plot_histogram(subset_sscore_male$si, "Male", F)
plot_histogram(subset_sscore_female$si, "Female", F)
```
### Syndromic score in patient groups divided according to gene functions
Let's investigate the severity score between groups of BBS genes based on their functions.
```{r fig.width=4, fig.height=3, fig.align = "center"}
## test of functional groups
boxplot(subset_sscore$si~factor(subset_sscore$ggroup,levels = c("bbs3","bbsome","chaps","other")), las = 2, ylab = "Syndromic score", xlab = "")
kruskal.test(subset_sscore$si~subset_sscore$ggroup)
FSA::dunnTest(subset_sscore$si~subset_sscore$ggroup)
```
Histogram plots showing the distribution of syndromic score in different gene groups for `Figure 1E`.
```{r fig.width=1.5,fig.height=2.75}
plot_histogram(subset_sscore_bbsome$si, "BBSome",T)
plot_histogram(subset_sscore_bbs3$si, "BBS3",F)
plot_histogram(subset_sscore_chaperonins$si, "Chaperonins",F)
plot_histogram(subset_sscore_other$si, "Non-canonical",F)
```
### Syndromic score in patients with different Chaperonin genes mutated
Summary of 'set patients with mutations in chaperonin-encoding BBS genes `Figure S3D`:
```{r}
subset_sscore_bbs6 <- subset(subset_sscore,gene=="BBS6")
subset_sscore_bbs10 <- subset(subset_sscore,gene=="BBS10")
subset_sscore_bbs12 <- subset(subset_sscore,gene=="BBS12")
sscore_chaperonin_analysis <- as.data.frame(round(t(rbind(
all_chaperonins = summary_analysis(subset_sscore_chaperonins),
set_bbs6 = summary_analysis(subset_sscore_bbs6),
set_bbs10 = summary_analysis(subset_sscore_bbs10),
set_bbs12 = summary_analysis(subset_sscore_bbs12)
))),digits=1)
kable(sscore_chaperonin_analysis) %>%
kable_styling(full_width = F, font_size = 9,bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```
Testing if there are differences in the syndromic score between patients with mutations in chpaeronin-encoding genes `Figure 1F`.
```{r fig.width=4,fig.height=3, fig.align = "center"}
## test of chaperonins
boxplot(subset_chaperonins$si~droplevels(subset_chaperonins$gene), las = 1, ylab = "Syndromic score", xlab = "")
kruskal.test(subset_chaperonins$si~droplevels(subset_chaperonins$gene))
```
```{r fig.width=1.5,fig.height=2.75}
plot_histogram(subset_sscore_bbs6$si,"BBS6",T)
plot_histogram(subset_sscore_bbs10$si,"BBS10",F)
plot_histogram(subset_sscore_bbs12$si,"BBS12",F)
```
### Syndromic score in patients with different BBSome genes mutated
Summary analysis of the bbsome group of 'set patients `Figure S3C`:
```{r}
subset_sscore_bbs1 <- subset(subset_sscore,subset_sscore$gene=="BBS1")
subset_sscore_bbs2 <- subset(subset_sscore,subset_sscore$gene=="BBS2")
subset_sscore_bbs4 <- subset(subset_sscore,subset_sscore$gene=="BBS4")
subset_sscore_bbs5 <- subset(subset_sscore,subset_sscore$gene=="BBS5")
subset_sscore_bbs7 <- subset(subset_sscore,subset_sscore$gene=="BBS7")
subset_sscore_bbs8 <- subset(subset_sscore,subset_sscore$gene=="BBS8")
subset_sscore_bbs9 <- subset(subset_sscore,subset_sscore$gene=="BBS9")
subset_sscore_bbs18 <- subset(subset_sscore,subset_sscore$gene=="BBS18")
sscore_bbsome_analysis <- as.data.frame(round(t(rbind(
set_bbsome = summary_analysis(subset_sscore_bbsome),
set_bbs1 = summary_analysis(subset_sscore_bbs1),
set_bbs2 = summary_analysis(subset_sscore_bbs2),
set_bbs4 = summary_analysis(subset_sscore_bbs4),
set_bbs5 = summary_analysis(subset_sscore_bbs5),
set_bbs7 = summary_analysis(subset_sscore_bbs7),
set_bbs8 = summary_analysis(subset_sscore_bbs8),
set_bbs9 = summary_analysis(subset_sscore_bbs9),
set_bbs18 = summary_analysis(subset_sscore_bbs18)
))),digits=1)
kable(sscore_bbsome_analysis) %>%
kable_styling(full_width = F, font_size = 9,bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```
Testing if there are differences in the syndromic score between groups of 'set patients with mutations in BBSome genes.
```{r fig.width=6.5, fig.height=3, fig.align = "center"}
## test of bbsome
subset_sscore_bbsome_wobbs18 = droplevels(subset(subset_sscore_bbsome,subset_sscore_bbsome$gene!="BBS18"))
boxplot(subset_sscore_bbsome_wobbs18$si~subset_sscore_bbsome_wobbs18$gene, las = 1, ylab = "Syndromic score", xlab = "")
kruskal.test(subset_sscore_bbsome_wobbs18$si~subset_sscore_bbsome_wobbs18$gene)
FSA::dunnTest(subset_sscore_bbsome_wobbs18$si~subset_sscore_bbsome_wobbs18$gene)
```
```{r fig.width=1.2, fig.height=2.5}
plot_histogram(subset_sscore_bbs1$si,"BBS1",T)
plot_histogram(subset_sscore_bbs2$si,"BBS2",F)
plot_histogram(subset_sscore_bbs4$si,"BBS4",F)
plot_histogram(subset_sscore_bbs5$si,"BBS5",F)
plot_histogram(subset_sscore_bbs7$si,"BBS7",F)
plot_histogram(subset_sscore_bbs8$si,"BBS8",F)
plot_histogram(subset_sscore_bbs9$si,"BBS9",F)
```
# Syndromic score in patients with cLOF vs MS mutations
Below, we will test if the syndromic score differs between patients with mutations leading to complete loss of function of the protein (such as frameshifts, splicing mutations, large deletions etc.) and patients having at least one missense mutation (either a substitution or a small deletion of one to three amino acids).
```{r fig.width= 4, fig.align = "center", out.width='35%'}
subset_sscore_MS <- subset(subset_sscore,subset_sscore$muttype=="MSMSandMScLOF")
subset_sscore_cLOF <- subset(subset_sscore,subset_sscore$muttype=="cLOF")
boxplot(subset_sscore$si~subset_sscore$muttype, las = 1, ylab = "Syndromic score", xlab = "")
wilcox.test(subset_sscore$si~subset_sscore$muttype)
```
In addition, we will test if the proportion of patients with syndromic score of 1 differs between cLOF and missense patients. This will be tested using Fisher test on number of patients with syndromic score 1 or syndromic score other than one in both groups.
```{r fig.width = 1.5, fig.height=2.75}
subset_sscore_MS_si1 <- subset(subset_sscore_MS,subset_sscore_MS$si==1)
subset_sscore_cLOF_si1 <- subset(subset_sscore_cLOF,subset_sscore_cLOF$si==1)
fisher.test(matrix(c(nrow(subset_sscore_MS_si1),
nrow(subset_sscore_MS)-nrow(subset_sscore_MS_si1),
nrow(subset_sscore_cLOF_si1),
nrow(subset_sscore_cLOF)-nrow(subset_sscore_cLOF_si1)), ncol = 2), workspace = 2e8)
plot_histogram(subset_sscore_MS$si, "missense", TRUE)
plot_histogram(subset_sscore_cLOF$si, "cLOF",F)
```
## Syndromic score in cLOF patients
### Patient groups divided according to gene functions
Let's investigate the severity score between groups of BBS genes based on their functions in the patients with cLOF mutations.
```{r fig.width=1.5,fig.height=2.75}
subset_sscore_cLOF_bbsome <- subset(subset_sscore_bbsome,subset_sscore_bbsome$muttype=="cLOF")
subset_sscore_cLOF_bbs3 <- subset(subset_sscore_bbs3,subset_sscore_bbs3$muttype=="cLOF")
subset_sscore_cLOF_chaperonins <- subset(subset_sscore_chaperonins,subset_sscore_chaperonins$muttype=="cLOF")
subset_sscore_cLOF_other <- subset(subset_sscore_other,subset_sscore_other$muttype=="cLOF")
plot_histogram(subset_sscore_cLOF_bbsome$si, "BBSome",T)
plot_histogram(subset_sscore_cLOF_bbs3$si, "BBS3",F)
plot_histogram(subset_sscore_cLOF_chaperonins$si, "Chaperonins",F)
plot_histogram(subset_sscore_cLOF_other$si, "Non-canonical",F)
kruskal.test(subset_sscore_cLOF$si~subset_sscore_cLOF$ggroup)
FSA::dunnTest(subset_sscore_cLOF$si~subset_sscore_cLOF$ggroup)
```
### Patient group within BBSome
```{r fig.width=6.5, fig.height=3, fig.align = "center"}
## test of bbsome
subset_sscore_cLOF_bbsome_wobbs18 = subset(subset_sscore_bbsome_wobbs18,subset_sscore_bbsome_wobbs18$muttype=="cLOF")
boxplot(subset_sscore_cLOF_bbsome_wobbs18$si~subset_sscore_cLOF_bbsome_wobbs18$gene, las = 1, ylab = "Syndromic score", xlab = "")
kruskal.test(subset_sscore_cLOF_bbsome_wobbs18$si~subset_sscore_cLOF_bbsome_wobbs18$gene)
FSA::dunnTest(subset_sscore_cLOF_bbsome_wobbs18$si~subset_sscore_cLOF_bbsome_wobbs18$gene)
```
```{r fig.height=2.5, fig.width=1.2, warning=FALSE}
subset_sscore_cLOF_bbs1 <- subset(subset_sscore_bbs1,subset_sscore_bbs1$muttype=="cLOF")
subset_sscore_cLOF_bbs2 <- subset(subset_sscore_bbs2,subset_sscore_bbs2$muttype=="cLOF")
subset_sscore_cLOF_bbs4 <- subset(subset_sscore_bbs4,subset_sscore_bbs4$muttype=="cLOF")
subset_sscore_cLOF_bbs5 <- subset(subset_sscore_bbs5,subset_sscore_bbs5$muttype=="cLOF")
subset_sscore_cLOF_bbs7 <- subset(subset_sscore_bbs7,subset_sscore_bbs7$muttype=="cLOF")
subset_sscore_cLOF_bbs8 <- subset(subset_sscore_bbs8,subset_sscore_bbs8$muttype=="cLOF")
subset_sscore_cLOF_bbs9 <- subset(subset_sscore_bbs9,subset_sscore_bbs9$muttype=="cLOF")
plot_histogram(subset_sscore_cLOF_bbs1$si,"BBS1",T)
plot_histogram(subset_sscore_cLOF_bbs2$si,"BBS2",F)
plot_histogram(subset_sscore_cLOF_bbs4$si,"BBS4",F)
plot_histogram(subset_sscore_cLOF_bbs5$si,"BBS5",F)
plot_histogram(subset_sscore_cLOF_bbs7$si,"BBS7",F)
plot_histogram(subset_sscore_cLOF_bbs8$si,"BBS8",F)
plot_histogram(subset_sscore_cLOF_bbs9$si,"BBS9",F)
```
### Missense and cLOF mutations in patient groups
Here, we test if the severity score differs between patients with missense or cLOF mutations in distinct groups of patients. These figures are used for `Figure S4`.
```{r fig.height=2.5, fig.width=1.2, warning=FALSE}
subset_sscore_MS_bbs1 <- subset(subset_sscore_bbs1,subset_sscore_bbs1$muttype=="MSMSandMScLOF")
subset_sscore_cLOF_canonwobbs1 <- subset(subset_sscore,subset_sscore$muttype=="cLOF"&subset_sscore$canonwobbs1=="yes")
subset_sscore_MS_canonwobbs1 <- subset(subset_sscore,subset_sscore$muttype=="MSMSandMScLOF"&subset_sscore$canonwobbs1=="yes")
subset_sscore_cLOF_bbsome <- subset(subset_sscore_bbsome,subset_sscore_bbsome$muttype=="cLOF")
subset_sscore_MS_bbsome <- subset(subset_sscore_bbsome,subset_sscore_bbsome$muttype=="MSMSandMScLOF")
plot_histogram(subset_sscore_MS_canonwobbs1$si,"Canon wo BBS1\n Missense",T)
plot_histogram(subset_sscore_cLOF_canonwobbs1$si,"Canon wo BBS1\n cLOF",F)
plot_histogram(subset_sscore_MS_bbs1$si,"BBS1\n Missense",F)
plot_histogram(subset_sscore_cLOF_bbs1$si,"BBS1\n cLOF",F)
plot_histogram(subset_sscore_cLOF_bbsome$si,"BBSome\n cLOF",F)
plot_histogram(subset_sscore_MS_bbsome$si,"BBSome \n Missense",F)
wilcox.test(subset_sscore_cLOF_canonwobbs1$si,subset_sscore_MS_canonwobbs1$si)
fisher_severity_ones <- function(subsetMS,subsetcLOF){
print(fisher.test(matrix(c(
nrow(subset(subsetMS,subsetMS$si==1)),
nrow(subsetMS)-nrow(subset(subsetMS,subsetMS$si==1)),
nrow(subset(subsetcLOF,subsetcLOF$si==1)),
nrow(subsetcLOF)-nrow(subset(subsetcLOF,subsetcLOF$si==1))
), ncol = 2)))
}
fisher_severity_ones(subset_sscore_cLOF_canonwobbs1, subset_sscore_MS_canonwobbs1)
wilcox.test(subset_sscore_cLOF_bbs1$si,subset_sscore_MS_bbs1$si)
fisher_severity_ones(subset_sscore_cLOF_bbs1, subset_sscore_MS_bbs1)
wilcox.test(subset_sscore_cLOF_bbsome$si,subset_sscore_MS_bbsome$si)
fisher_severity_ones(subset_sscore_cLOF_bbsome, subset_sscore_MS_bbsome)
```
Below, we visualize the percentage of cLOF mutations in BBSome patients with syndromic score = 1 according to mutations in different BBS genes.
```{r fig.width=4, fig.height=4, fig.align = "center", out.width="35%"}
cLOF_sscore <- function(gene1){
total_cLOF <- nrow(subset(subset_sscore,subset_sscore$gene==gene1&subset_sscore$muttype=="cLOF"))
total_MS <- nrow(subset(subset_sscore,subset_sscore$gene==gene1&subset_sscore$muttype=="MSMSandMScLOF"))
cLOF_si1 <- nrow(subset(subset_sscore,subset_sscore$gene==gene1&subset_sscore$muttype=="cLOF"&subset_sscore$si==1))
MS_si1 <- nrow(subset(subset_sscore,subset_sscore$gene==gene1&subset_sscore$muttype=="MSMSandMScLOF"&subset_sscore$si==1))
per_cLOF <- cLOF_si1/total_cLOF*100
per_MS <- MS_si1/total_MS*100
return(round(data.frame(per_MS,per_cLOF), digits = 2))
}
df <- rbind(
BBS1 = cLOF_sscore("BBS1"),
BBS2 = cLOF_sscore("BBS2"),
BBS4 = cLOF_sscore("BBS4"),
BBS5 = cLOF_sscore("BBS5"),
BBS7 = cLOF_sscore("BBS7"),
BBS8 = cLOF_sscore("BBS8"),
BBS9 = cLOF_sscore("BBS9")
)
kable(df)
ggplot(df, aes(x = per_MS, y = per_cLOF)) + geom_point(size=2, shape=19) + scale_x_continuous(breaks = c(0,20,40,60), limits = c(0,60)) +
scale_y_continuous(breaks = c(0,20,40,60), limits = c(0,60)) +
geom_abline(intercept = 0, slope = 1) +
geom_text(aes(label=row.names(df)),hjust=-.3, vjust=0)
```
# Analysis of phenotypes
For this analysis, all available data were used, i.e. not restricted to the 'set. Statistical significance of penetrance of phenotypes was tested using Fisher test.
### Phenotypes in gene groups:
First, let's test if any differences in the penetrance of phenotypes exist between gene groups in each phenotype.
```{r}
for(i in c(7:15)) { if(i==7){df <- data.frame()}
fisher.test(matrix( c(
phenotype_analysis(subset_bbsome[i])$no,
phenotype_analysis(subset_bbs3[i])$no,
phenotype_analysis(subset_chaperonins[i])$no,
phenotype_analysis(subset_bbsome[i])$yes,
phenotype_analysis(subset_bbs3[i])$yes,
phenotype_analysis(subset_chaperonins[i])$yes),byrow = TRUE, ncol = 3))
ggroups_pval <- rbind(df,data.frame(phenotype = phenotypes[i-6],
p_val = fisher.test(matrix( c(
phenotype_analysis(subset_bbsome[i])$no,
phenotype_analysis(subset_bbs3[i])$no,
phenotype_analysis(subset_chaperonins[i])$no,
phenotype_analysis(subset_bbsome[i])$yes,
phenotype_analysis(subset_bbs3[i])$yes,
phenotype_analysis(subset_chaperonins[i])$yes),byrow = TRUE, ncol = 3))$p.value))
df
}
ggroups_pval <- df
kable(ggroups_pval, format = "html") %>%
kable_styling(full_width = F, font_size = 9,bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```
On the 5% level, differences were shown in PD, CI, REP, REN and HEART.
Let'have a look at the differences between individual groups to reveal which differences account for the overall difference. As there are 3 groups, the p-value on which we consider the difference being statistical significance will be lowered according to Sidak correction for multiple comparisons.
```{r}
sidak_table(3)
```
```{r}
subset_allwow <- subset(update8forR,update8forR$allwow=="yes"|
update8forR$gene=="BBS18")
sidak3 <- calculate_sidak(3)
kable(rbind(
one_vs_all_other(subset_bbsome,subset_allwow,9,sidak3),
one_vs_all_other(subset_bbs3,subset_allwow,9,sidak3),
one_vs_all_other(subset_chaperonins,subset_allwow,9,sidak3),
one_vs_all_other(subset_bbsome,subset_allwow,10,sidak3),
one_vs_all_other(subset_bbs3,subset_allwow,10,sidak3),
one_vs_all_other(subset_chaperonins,subset_allwow,10,sidak3),
one_vs_all_other(subset_bbsome,subset_allwow,11,sidak3),
one_vs_all_other(subset_bbs3,subset_allwow,11,sidak3),
one_vs_all_other(subset_chaperonins,subset_allwow,11,sidak3),
one_vs_all_other(subset_bbsome,subset_allwow,12,sidak3),
one_vs_all_other(subset_bbs3,subset_allwow,12,sidak3),
one_vs_all_other(subset_chaperonins,subset_allwow,12,sidak3),
one_vs_all_other(subset_bbsome,subset_allwow,13,sidak3),
one_vs_all_other(subset_bbs3,subset_allwow,13,sidak3),
one_vs_all_other(subset_chaperonins,subset_allwow,13,sidak3))) %>%
kable_styling(full_width = F, font_size = 9,bootstrap_options = c("striped", "hover", "condensed", "responsive"))