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s03.SV_summary_statistics.Rmd
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---
title: "SV summary statistics"
author: "Daoming Wang"
date: "2020/10/22"
output:
html_document:
theme: flatly
highlight: espresso
toc: true
toc_depth: 4
toc_float: true
word_document: default
pdf_document:
includes:
in_header: header.tex
keep_tex: yes
latex_engine: xelatex
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## 1 Preparation
### 1.1 Import
```{r 1.1, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
source("functions.R")
```
### 1.2 Inputs
Read input files.
```{r 1.2, echo=TRUE}
# SV
all_dsv <- read.table("01.cleanData/SV_all/20200801_LLD_300OB_deletionStructuralVariation_1437samples.tsv",check.names = F)
all_vsv <- read.table("01.cleanData/SV_all/20200801_LLD_300OB_variableStructuralVariation_1437samples.tsv",check.names = F)
info <- read.table("01.cleanData/SV_info/20200801_LLD_300OB_Informative_species_information.tsv",
sep = "\t", header = T, stringsAsFactors = F)
# abundance
all_abun_sv<-read.table("01.cleanData/mbio_all/LLD_300OB_SV_55species_abun_1437samples.tsv",check.names = F)
# Basic
all_basic <- read.table("01.cleanData/phen_all/20200801_LLD_basic_1437samples.tsv")
```
## 2 SV summary
### 2.1 Species number
Number of species with SVs
```{r 2.1, fig.width=3.5, fig.height=3.5}
if(!dir.exists("03.SV_summary_statistics")){dir.create("03.SV_summary_statistics")}
dsgv_species_n<-sum(info$Deletion_SVs_number>0)
vsgv_species_n<-sum(info$Variable_SVs_number>0)
total_species_n<-sum(info$Variable_SVs_number>0 | info$Deletion_SVs_number>0)
species_n<-data.frame(item = rep("Informative species number", 3),
categories = c("SVs","Deletion SVs", "Variable SVs"),
value = c(total_species_n,dsgv_species_n, vsgv_species_n))
species_n$categories <- factor(species_n$categories, levels = species_n$categories)
species_n$categories <- factor(species_n$categories,levels(species_n$categories)[c(2,3,1)])
p_species_n<-ggplot(species_n, aes(x=categories, y=value,label = value))+
geom_bar(aes(fill = categories),stat = 'identity')+
geom_text(position = position_stack(vjust = 0.5), color = "white")+
xlab(NULL)+
ylab("Number of Informative species")+
scale_y_continuous(expand = c(0,0))+
scale_fill_manual(name=NULL,
breaks=c("Deletion SVs", "Variable SVs", "SVs"),
labels=c("Deletion SVs", "Variable SVs", "SVs"),
values = mycolor3)+
theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.line.x = element_line(),
axis.line.y = element_line(),
legend.key = element_rect(fill = NA),
legend.position = "none",
panel.grid.major = element_line(colour = "gray90",linetype = "dashed",size = 0.25),
panel.grid.minor = element_line(colour = NA),
panel.background = element_rect(fill = NA))
pdf("03.SV_summary_statistics/20200801_LLD_300OB_Informative_species_number.pdf", height = 3.5, width = 3.5)
print(p_species_n)
dev.off()
print(p_species_n)
```
### 2.1 SV number
#### 2.1.1 SV total number
```{r 2.1.1, fig.width=6, fig.height=6}
dsgv_n<-sum(info$Deletion_SVs_number)
vsgv_n<-sum(info$Variable_SVs_number)
sgv_n<-dsgv_n+vsgv_n
sv_n<-data.frame(items = rep("SVs number", 2),
categories = c("Deletion SV", "Variable SV"),
value = c(dsgv_n, vsgv_n))
p_pie<-my_pie(sv_n, 'SVs number',mycol = rev(mycolor2_green_blue))
pdf("03.SV_summary_statistics/20200801_LLD_300OB_Total_SVs_number.pdf",height = 6, width = 6)
print(p_pie)
dev.off()
print(p_pie)
```
#### 2.1.2 SV number per species
```{r 2.1.2, fig.width=11, fig.height=6}
species_sgv_n_order<- info$Short_name[order(info$SVs_number, decreasing = T)]
species_sgv_n_long<-gather(info[,c(10,12,13)], "type", "number", c(2:3))
p_sv_species<-ggplot(species_sgv_n_long, aes(x=Short_name, y=number, group = type, fill = type))+
geom_bar(position = "stack",stat="identity")+
xlab(NULL)+
ylab("Number of SVs")+
scale_x_discrete(limits = species_sgv_n_order)+
scale_y_continuous(expand = c(0,0))+
scale_fill_manual(name=NULL,
breaks = c("Deletion_SVs_number", "Variable_SVs_number"),
labels = c("Deletion SVs ", "Variable SVs"),
values = mycolor2_green_blue)+
theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
plot.margin=unit(c(0,1,1,3),"cm"),
axis.line.x = element_line(),
axis.line.y = element_line(),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position="top",
legend.key = element_rect(fill = NA),
legend.text = element_text(size = 10),
panel.grid.major = element_line(colour = NA,linetype = "dashed",size = 0.25),
panel.grid.minor = element_line(colour = NA),
panel.background = element_rect(fill = NA))
pdf("03.SV_summary_statistics/20200801_LLD_300OB_SVs_number_species.pdf", height = 6, width = 11)
print(p_sv_species)
dev.off()
print(p_sv_species)
```
### 2.2 Sample size per species
```{r 2.2, fig.width=11, fig.height=6}
infor_sample_n_order<- info$Short_name[order(info$Total_samples_number, decreasing = T)]
infor_sample_n <- info[,c(10,15,16)]
infor_sample_n.long <- gather(infor_sample_n,'Cohort', 'Sample_size', c(2:3))
p_sample_n<-ggplot(infor_sample_n.long, aes(x=Short_name, y=Sample_size,group = Cohort))+
geom_bar(aes(fill = Cohort),position = "stack",stat="identity")+
xlab(NULL)+
ylab("Number of samples")+
scale_x_discrete(limits = infor_sample_n_order)+
scale_y_continuous(expand = c(0,0))+
scale_fill_manual(name=NULL,
breaks = c("LLD_sample_number", "X300OB_sample_number"),
labels = c("LLD ", "300OB"),
values = mycolor2_blue_yellow)+
theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
plot.margin=unit(c(2,1,1,3),"cm"),
axis.line.x = element_line(),
axis.line.y = element_line(),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position="top",
legend.key = element_rect(fill = NA),
legend.text = element_text(size = 10),
panel.grid.major = element_line(colour = NA,linetype = "dashed",size = 0.25),
panel.grid.minor = element_line(colour = NA),
panel.background = element_rect(fill = NA))
pdf("03.SV_summary_statistics/20200801_LLD_300OB_Samples_number_species.pdf", height = 6, width = 11)
print(p_sample_n)
dev.off()
print(p_sample_n)
```
### 2.3 SV number factors
Show relationships between SV numbers and genome size and sample size.
```{r 2.3, fig.width=4, fig.height=5}
species_sample_n<-info[,c(1,18,17,12:14)]
length_sv_n_cor<-cor.test(species_sample_n$Length/1000, species_sample_n$SVs_number)
text_r<-paste("r=",round(length_sv_n_cor$estimate,digits = 3),"\np=",format(length_sv_n_cor$p.value,digits = 3),sep = "")
p_scatter_pie<-ggplot()+
geom_smooth(data=species_sample_n ,aes(x=Length/10000, y=SVs_number),method = "lm", color = "white", alpha = 0.2,size = 0.5)+
geom_scatterpie(data=species_sample_n ,aes(x=Length/10000, y=SVs_number,r=Total_samples_number/50),
cols=c("Deletion_SVs_number","Variable_SVs_number"),color=NA, alpha = 0.75)+
coord_equal()+
annotate("text", -Inf, Inf, label = c(text_r),hjust = -0.1, vjust = 1)+
xlab("Genome size (10 kbp)")+
ylab("Number of SVs")+
scale_x_continuous(expand = c(0,0))+
scale_y_continuous(expand = c(0,0))+
scale_fill_manual(name = NULL,
breaks = c("Deletion_SVs_number", "Variable_SVs_number"),
labels = c("Deletion SVs ", "Variable SVs"),
values = mycolor2_green_blue)+
theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.line.x = element_line(),
axis.line.y = element_line(),
legend.position="top",
legend.key = element_rect(fill = NA),
panel.grid.major = element_line(colour = "gray90",linetype = "dashed",size = 0.25),
panel.grid.minor = element_line(colour = NA),
panel.background = element_rect(fill = NA))
pdf("03.SV_summary_statistics/20200801_LLD_300OB_Sample_n_SVs_n_scatter_pie.pdf", height = 5, width = 4)
print(p_scatter_pie)
dev.off()
print(p_scatter_pie)
```
### 3 Species abundance
```{r 3, fig.width = 3, fig.height = 3}
sv_species_total_abun<-data.frame(id = rownames(all_abun_sv),
Total_abundance=rowSums(all_abun_sv))
mean(sv_species_total_abun$Total_abundance,na.rm = T)
se(sv_species_total_abun$Total_abundance)
min(sv_species_total_abun$Total_abundance,na.rm = T)
max(sv_species_total_abun$Total_abundance,na.rm = T)
p_sv_abun_density<-ggplot(sv_species_total_abun,aes(x=Total_abundance), color = "#2EC4B6")+
geom_density(alpha = 0.2, color = "#2EC4B6", fill = "#2EC4B6", alpha = 0.5)+
geom_rug(color = "#2EC4B6",alpha = 0.5,length = unit(0.05, "npc"))+
geom_vline(xintercept = mean(sv_species_total_abun$Total_abundance,na.rm = T), linetype = "dashed",color = "#ee6352")+
ylab('Density')+
xlab('Abundance')+
scale_y_continuous(expand = c(0,0))+
theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.line.x = element_line(),
axis.line.y = element_line(),
legend.position = "none",
legend.key = element_rect(fill = NA),
panel.grid.major = element_line(colour = NA),
panel.grid.minor = element_line(colour = NA),
panel.background = element_rect(fill = NA))
pdf("03.SV_summary_statistics/sv_abun_density.pdf", width = 3, height = 2)
print(p_sv_abun_density)
dev.off()
print(p_sv_abun_density)
```
## 4 SV correlation
```{r 4, eval=FALSE}
vsv_corr <- rcorr(as.matrix(all_vsv))
vsv_corr.r<-vsv_corr$r
vsv_corr.p<-vsv_corr$P
vsv_corr.n<-vsv_corr$n
vsv_corr.r.edge<-melt(vsv_corr.r)
vsv_corr.p.edge<-melt(vsv_corr.p)
vsv_corr.n.edge<-melt(vsv_corr.n)
vsv_corr.edge<-cbind(vsv_corr.r.edge, vsv_corr.p.edge[,-c(1:2)], vsv_corr.n.edge[,-c(1:2)])
colnames(vsv_corr.edge)<-c("SV1", "SV2", "R", "P", "N")
save(vsv_corr.edge, file = "03.SV_summary_statistics/vsv_corr.edge.RData")
dsv_corr <- rcorr(as.matrix(all_dsv))
dsv_corr.r<-dsv_corr$r
dsv_corr.p<-dsv_corr$P
dsv_corr.n<-dsv_corr$n
dsv_corr.r.edge<-melt(dsv_corr.r)
dsv_corr.p.edge<-melt(dsv_corr.p)
dsv_corr.n.edge<-melt(dsv_corr.n)
dsv_corr.edge<-cbind(dsv_corr.r.edge, dsv_corr.p.edge[,-c(1:2)], dsv_corr.n.edge[,-c(1:2)])
colnames(dsv_corr.edge)<-c("SV1", "SV2", "R", "P", "N")
save(dsv_corr.edge, file = "03.SV_summary_statistics/dsv_corr.edge.RData")
all_vsv_M_plot.list<-list()
all_dsv_M_plot.list<-list()
for (i in c(1:nrow(info))){
# i<-1
file_name<-str_replace_all(info$organism[i], "\\/", "\\.")
spe_name<-str_replace_all(info$organism[i],"\\[","\\\\\\[") %>% str_replace_all("\\]","\\\\\\]")
vsv_i<-all_vsv[,grep(spe_name,colnames(all_vsv))]
dsv_i<-all_dsv[,grep(spe_name,colnames(all_dsv))]
if(info$organism[i]=="Bifidobacterium adolescentis"){
p_vsv_M_i<-ggplot() + theme_void()
}else{
p_vsv_M_i<- ggcorr(vsv_i,method = c("pairwise"), alpha = 0)+
ggtitle(info$Short_name[i])+
theme(legend.position = "none",
plot.title = element_text(size = 4))
}
p_dsv_M_i<- ggcorr(dsv_i,method = c("pairwise"), alpha = 0)+
ggtitle(info$Short_name[i])+
theme(legend.position = "none",
plot.title = element_text(size = 4))
all_vsv_M_plot.list[[i]] <- p_vsv_M_i
all_dsv_M_plot.list[[i]] <- p_dsv_M_i
}
pdf("03.SV_summary_statistics/vsv_correlation.pdf")
plot_grid(plotlist=all_vsv_M_plot.list)
dev.off()
pdf("03.SV_summary_statistics/dsv_correlation.pdf")
plot_grid(plotlist=all_dsv_M_plot.list)
dev.off()
```