R语言ggplot2做堆积柱形图展示密码子偏向性RSCU值（更新版）

之前有一篇推文记录过画图的代码
R语言ggplot2画堆积柱形图展示密码子偏向性的RSCU值，
R语言ggplot2画分组堆积柱形图展示密码子偏向性的RSCU值
那篇推文里需要借助python区算RSCU值，然后用R语言画图。R语言里有一个R包seqinr也可以计算RSCU值
计算RSCU值的代码

输入数据为多个cds序列
library(tidyverse)
seqinr::read.fasta(“calculateRSCUexampleCDS.fa”) %>%
unlist() %>%
seqinr::uco(index=”rscu”) %>%
as.data.frame() %>%
rownames_to_column() %>%
magrittr::set_colnames(c(“codon”,”rscu”))

构造作图数据

data.frame(
acids=c(“Isoleucine”,”Leucine”,”Valine”,”Phenylalanine”,
“Methionine”,”Cysteine”,”Alanine”,”Glycine”,
“Proline”,”Threonine”,”Serine”,
“Tyrosine”,”Tryptophan”,”Glutamine”,
“Asparagine”,”Histidine”,”Glutamic acid”,
“Aspartic acid”,”Lysine”,”Arginine”,”Stop codons”),
slc=c(“I”,”L”,”V”,”F”,”M”,”C”,”A”,”G”,”P”,”T”,
“S”,”Y”,”W”,”Q”,”N”,”H”,”E”,”D”,”K”,”R”,”Stop”),
codon=c(“ATT, ATC, ATA”,”CTT, CTC, CTA, CTG, TTA, TTG”,
“GTT, GTC, GTA, GTG”,”TTT, TTC”,
“ATG”,”TGT, TGC”,
“GCT, GCC, GCA, GCG”,”GGT, GGC, GGA, GGG”,
“CCT, CCC, CCA, CCG”,”ACT, ACC, ACA, ACG”,
“TCT, TCC, TCA, TCG, AGT, AGC”,
“TAT, TAC”,”TGG”,”CAA, CAG”,”AAT, AAC”,”CAT, CAC”,”GAA, GAG”,”GAT, GAC”,
“AAA, AAG”,”CGT, CGC, CGA, CGG, AGA, AGG”,
“TAA, TAG, TGA”)

) %>%
separate(codon,paste0(“col”,1:6),sep=”, “) %>%
pivot_longer(!c(acids,slc)) %>%
na.omit() %>%
select(-name) %>%
magrittr::set_colnames(c(“acids”,”abbr”,”codon”))-> codon.table

seqinr::read.fasta(“calculateRSCUexampleCDS.fa”) %>%
unlist() %>%
seqinr::uco(index=”rscu”) %>%
as.data.frame() %>%
rownames_to_column() %>%
magrittr::set_colnames(c(“codon”,”rscu”)) %>%
mutate(codon=str_to_upper(codon)) %>%
left_join(codon.table,by=c(“codon”=”codon”)) %>%
mutate(acids=str_sub(acids,1,3)) %>%
arrange(acids,rscu)%>%
group_by(acids) %>%
mutate(id=row_number()) %>%
dplyr::select(codon,acids,rscu,id) %>%
mutate(codon=factor(codon,level=codon),
id=-id) -> rscu.dat

rscu.dat

colnames(rscu.dat)<-paste0(“V”,1:4)
作图代码

ggplot(rscu.dat,aes(fill=as.character(V4),x=V2,y=V3))+
geom_bar(position = “stack”,stat=”identity”)+
theme_bw()+scale_y_continuous(expand=c(0,0),
limits = c(0,6.2))+
theme(legend.position = “none”)+labs(y=”RSCU”,x=””)+
theme(panel.grid = element_blank())+
scale_fill_manual(values = c(“#999999″,”#56b4e8″,”#d55e00”,
“#009f73″,”#cc79a7″,”#0072b1”))+
theme(plot.margin = unit(c(0.1,0.1,0,0.1),’mm’))-> rscu.p1

ggplot(rscu.dat,aes(x=V2,y=V4))+
geom_label(aes(label=V1,fill=as.character(V4)),
size=3)+
labs(x=””,y=””)+
theme_minimal()+
theme(legend.position = “none”,
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank())+
scale_fill_manual(values = c(“#999999″,”#56b4e8″,”#d55e00”,
“#009f73″,”#cc79a7″,”#0072b1”))+
theme(plot.margin = unit(c(0,0.1,0.1,0.1),’mm’))+
coord_cartesian(clip = “off”) -> rscu.p2

library(patchwork)

cairo_pdf(“RSCU_barplot.pdf”,
width = 9.4,height = 4)
rscu.p1+rscu.p2 + plot_layout(ncol=1,heights = c(2,1))
dev.off()

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