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ggplot2绘制突变全景图
💡专注R语言在🩺生物医学中的使用
这篇是生信技能树的一个学徒作业：小队列的肿瘤外显子临床预后意义
主要学习的图是这几个：
读取数据
附件下载地址：https://ehoonline.biomedcentral.com/articles/10.1186/s40164-021-00200-x
s2 <- data.table::fread("./s240164_2021_200_MOESM2_ESM.csv",skip = 2,header = T)
加载R包：
suppressMessages(library(tidyverse))
## Warning: package 'tidyverse' was built under R version 4.2.1
改个名字方便使用：
names(s2)[3] <- "gene_symbol"
names(s2)
## [1] "SampleID" "PatientID" "gene_symbol" "cHGVS" "pHGVS"
## [6] "Function" "Transcript" "ExIn_ID" "Cosmic ID" "Vary Type"
## [11] "caseAF"
Fig2a
Fig2a其实就是突变全景图的右边条形图部分，但是作者给分开展示了。只要计算某个基因在多少个样本中突变了，再除以53即可得到纵坐标mutation percentage！
# 把这几个基因挑出来
mutationper <- s2 |>
dplyr::filter(gene_symbol %in% c("TET2","RHOA","PCLO",
"DNMT3A","IDH2","PIEZO1",
"TP53","RELN","FAT3",
"CHD3")) |>
group_by(PatientID,gene_symbol) |>
summarise(n=n()) |>
mutate(n = case_when(n > 3 ~ "3",
T ~ as.character(n)
)) |>
ungroup() |>
group_by(gene_symbol,n) |>
summarise(nsub=n(),per=nsub/53) |>
mutate(gene_symbol = factor(gene_symbol,
levels = c("TET2","RHOA","PCLO",
"DNMT3A","IDH2","PIEZO1",
"TP53","RELN","FAT3",
"CHD3")),
n = factor(n,levels = c("3","2","1"))
)
## `summarise()` has grouped output by 'PatientID'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'gene_symbol'. You can override
## using the `.groups` argument.
mutationper
## # A tibble: 20 × 4
## # Groups: gene_symbol [10]
## gene_symbol n nsub per
## <fct> <fct> <int> <dbl>
## 1 CHD3 1 6 0.113
## 2 CHD3 2 1 0.0189
## 3 CHD3 3 1 0.0189
## 4 DNMT3A 1 9 0.170
## 5 DNMT3A 2 1 0.0189
## 6 FAT3 1 6 0.113
## 7 FAT3 2 2 0.0377
## 8 IDH2 1 8 0.151
## 9 IDH2 2 1 0.0189
## 10 PCLO 1 9 0.170
## 11 PCLO 2 3 0.0566
## 12 PIEZO1 1 8 0.151
## 13 PIEZO1 2 1 0.0189
## 14 RELN 1 7 0.132
## 15 RELN 2 1 0.0189
## 16 RHOA 1 23 0.434
## 17 TET2 1 7 0.132
## 18 TET2 2 17 0.321
## 19 TET2 3 10 0.189
## 20 TP53 1 8 0.151
画图即可：
mutationper |>
ggplot(aes(gene_symbol,per))+
geom_bar(stat = "identity",aes(fill=n))+
scale_x_discrete(name=NULL)+
scale_y_continuous(name="Mutation Percentage %",expand = c(0,0),
limits = c(0,0.8),breaks = c(0,0.2,0.4,0.6,0.8),
labels = c(0,20,40,60,80)
)+
scale_fill_discrete(name=NULL,labels=c("≥ 3 mutations",
"2 mutations","1 mutation"
))+
theme_classic()+
theme(axis.text.x = element_text(angle = 45,color = "black",size = 12,hjust = 1),
axis.text.y = element_text(color = "black",size = 12),
axis.title.y = element_text(color = "black",size = 14),
legend.position = c(0.8,0.6),
axis.line = element_line(color = "black",size = 1.1),
axis.ticks = element_line(color = "black",size = 1.1)
)
Fig1
最开始想用complexheatmap画，但是发现是长数据，可以直接用ggplot2画。
# 预处理数据
heat_df <- s2
# 挑选展示的基因
genes <- c("CHD3","APC","TP53","PALB2","FANCA","TET2","DNMT3A","IDH2","ARID1A","ARID1B","MLL3","TYK2","STAT3","LRRK2","MAP2K1","PCLO","PIEZO1","FAT3","CSMD1","NSD1","MKI67","WDR90","MGA","CPS1","SPEN","ATP10B","ANKRD11","RELN","PLCG1","ALK","FLT4","RHOA","NOTCH1","NOTCH4")
# 挑选数据
aa <- heat_df |>
select(gene_symbol,PatientID,Function) |>
filter(gene_symbol %in% genes)
# 变成因子方便排序
aa$gene_symbol <- factor(aa$gene_symbol,levels = genes)
# 类型修改
aa[aa=="splice-3"] <- "splicing"
aa[aa=="splice-5"] <- "splicing"
画热图部分：
p1 <- ggplot(aa, aes(factor(PatientID),fct_rev(gene_symbol)))+
geom_tile(aes(fill=Function))+
labs(x=NULL,y=NULL)+
theme(axis.text.x = element_blank(),
axis.ticks = element_blank()
)
p1
画条形图部分：
up.df <- aa |> count(PatientID,Function)
p2 <- ggplot(up.df, aes(factor(PatientID),n))+
geom_bar(stat = "identity", aes(fill=Function))+
scale_y_continuous(name = NULL,expand = c(0,0))+
scale_x_discrete(name=NULL)+
theme_classic()+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
legend.position = "none"
)
p2
拼图：
library(aplot)
p3 <- p2 |> insert_bottom(p1,height = 5)
p3
由于纵坐标变成了因子，没有突变的样本会直接移除，所以和原图还是有差别的。
fig2c
可以用trackviewer画，但是我还不会。。只能偷个懒用maftools画。
这个附件也不像常见的几个软件的结果，应该是作者修改过的，所以也没办法直接用技能树的方法转换为maf。
s2 <- data.table::fread("./s240164_2021_200_MOESM2_ESM.csv",skip = 2,header = T)
names(s2)
## [1] "SampleID" "PatientID" "Gene Symbol" "cHGVS" "pHGVS"
## [6] "Function" "Transcript" "ExIn_ID" "Cosmic ID" "Vary Type"
## [11] "caseAF"
改名字，变成maftools需要的列名：
names(s2)[1] <- "Tumor_Sample_Barcode"
names(s2)[3] <- "Hugo_Symbol"
names(s2)[6] <- "Variant_Classification"
names(s2)[10] <- "Variant_Type"
增加几列maftools需要的列名，内容随便填即可：
s2$Chromosome <- c(rep(paste0("chr",1:22),38),paste0("chr",1:20))
s2$Start_Position <- 1
s2$End_Position <- 2
s2$Reference_Allele <- 3
s2$Tumor_Seq_Allele2 <- 4
还要修改Variant_Classification的内容，不然maftools会报错。
table(s2$Variant_Classification)
##
## cds-del cds-ins frameshift missense ncRNA
## 24 5 43 712 2
## nonsense span splice-3 splice-5 stop-loss
## 45 4 6 13 1
## stop-retained
## 1
这几个类型转换可能有问题，网络上没找到合适的信息.
s3 <- s2 |>
mutate(Variant_Type = ifelse(Variant_Type == "SNV","SNP","DEL"),
Variant_Classification =
case_when(Variant_Classification == "cds-del" ~ "In_Frame_Del",
Variant_Classification == "cds-ins" ~ "In_Frame_Ins",
Variant_Classification == "frameshift" ~ "Frame_Shift_Ins",
Variant_Classification == "missense" ~ "Missense_Mutation",
Variant_Classification == "ncRNA" ~ "RNA",
Variant_Classification == "nonsense" ~ "Nonsense_Mutation",
Variant_Classification == "span" ~ "Intron",
Variant_Classification %in% c("splice-3","splice-5") ~ "Splice_Site",
Variant_Classification %in% c("stop-loss","stop-retained") ~ "3'Flank"
)
)
library(maftools)
ptclMaf <- read.maf(s3)
## -Validating
## --Removed 108 duplicated variants
## -Silent variants: 8
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 0.050s elapsed (0.030s cpu)
由于类型转换问题，比例差异很大。
oncoplot(ptclMaf,top = 30)
lollipopPlot(ptclMaf, gene = "TET2",
AACol = "pHGVS"
)
## Removed 4 mutations for which AA position was not available
lollipopPlot(ptclMaf, gene = "TP53", AACol = "pHGVS")
## 8 transcripts available. Use arguments refSeqID or proteinID to manually specify tx name.
## HGNC refseq.ID protein.ID aa.length
## 1: TP53 NM_000546 NP_000537 393
## 2: TP53 NM_001126112 NP_001119584 393
## 3: TP53 NM_001126118 NP_001119590 354
## 4: TP53 NM_001126115 NP_001119587 261
## 5: TP53 NM_001126113 NP_001119585 346
## 6: TP53 NM_001126117 NP_001119589 214
## 7: TP53 NM_001126114 NP_001119586 341
## 8: TP53 NM_001126116 NP_001119588 209
## Using longer transcript NM_000546 for now.
## Removed 1 mutations for which AA position was not available
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