Chapter 1 R

1.1 ggplot

1.1.1 geom_text

dftotal <- read.delim("data/geom_text.txt")
aobins <- c(0,0.0385,0.0759,0.113,0.151,0.188,0.225,0.262,0.3,0.45,0.6,0.7,0.8,0.9,1)
vector <-  c(1:length(aobins))[-length(aobins)]
xlabels <- NULL
for (i in vector) {
  print(paste(paste(aobins[i]*100,"%",sep = ""),paste(aobins[i+1]*100,"%",sep = ""),sep = "-") )
  label <- paste(paste(aobins[i]*100,"%",sep = ""),paste(aobins[i+1]*100,"%",sep = ""),sep = "-")
  xlabels[i] = label
}
## [1] "0%-3.85%"
## [1] "3.85%-7.59%"
## [1] "7.59%-11.3%"
## [1] "11.3%-15.1%"
## [1] "15.1%-18.8%"
## [1] "18.8%-22.5%"
## [1] "22.5%-26.2%"
## [1] "26.2%-30%"
## [1] "30%-45%"
## [1] "45%-60%"
## [1] "60%-70%"
## [1] "70%-80%"
## [1] "80%-90%"
## [1] "90%-100%"
dfori <- dftotal %>% filter(Region=="CDS",Variant_type=="SYNONYMOUS"|Variant_type=="NONSYNONYMOUS") %>% 
  mutate(Derived_SIFT = ifelse(is.na(Derived_SIFT),100,Derived_SIFT)) %>% 
  mutate(Gerp = ifelse(is.na(Gerp),-100,Gerp)) %>% 
  mutate(LoadGroup = ifelse(Variant_type=="NONSYNONYMOUS",ifelse(Derived_SIFT<=0.05&Gerp>=1,"Deleterious","Nonsynonymous"),"Synonymous")) %>%
  filter(!Ancestral == "NA") %>% 
  filter(Ancestral==Ref|Ancestral==Alt) %>% 
  mutate(IfRefisAnc = if_else(Ref == Ancestral,"Anc","Der")) 
  

dffre <- dfori %>% 
  filter(!is.na(DAF_ABD)) %>%  ## 注意要保持和分bin后一致,所以这里也要去除 DAF_ABD是NA的值。
  group_by(Sub,IfRefisAnc) %>% 
  count(LoadGroup) %>% 
  mutate(fre=n/sum(n)) %>% 
  arrange(Sub,desc(LoadGroup)) %>% 
  filter(Sub=="A",LoadGroup=="Deleterious") 


  ### data deal
df <- dfori %>%   
  filter(!is.na(DAF_ABD)) %>% 
  mutate(bin=cut(DAF_ABD,breaks=aobins)) %>%
  group_by(Sub,IfRefisAnc,bin) %>% 
  count(LoadGroup) %>% 
  mutate(fre=n/sum(n)) %>% 
  arrange(Sub,desc(bin)) %>%
  ### merge group
  mutate(group = paste(IfRefisAnc,"-",str_sub(LoadGroup,1,3),sep = "")) 

p <- df %>% 
  filter(Sub=="A") %>% 
  filter(LoadGroup=="Deleterious") %>% 
  ggplot()+
  geom_line(aes(x=bin,y=fre,group=group,color=group,linetype=group),color = "#d5311c")+
  scale_linetype_manual(values=c("solid","dotdash"))+
  labs(y="Fraction in each category",x="Derived allele frequency bins")+
  # scale_color_brewer(palette = "Set2")+
  scale_x_discrete(label = xlabels)+
  geom_hline(yintercept = dffre$fre[1],color ="#d5311c")+
  # geom_text(aes(0,dffre$fre[1],label = dffre$fre[1], vjust = -1))+
  geom_hline(yintercept = dffre$fre[2],color ="#d5311c",linetype="dashed")+
  # geom_text(aes(0,dffre$fre[2],label = dffre$fre[1], vjust = -1))+
  geom_text(data=dffre,aes(x=1,y=fre,label=round(fre,3)),nudge_y = .01)+
  facet_grid(Sub~.)+
  theme_classic()+
  theme(legend.position = "none")+
  theme(axis.text.x= element_text(angle=45, vjust=1,hjust = 1))+
  theme(plot.margin=unit(rep(1,4),'lines'),
          axis.line = element_line(size=0.4, colour = "black"),text = element_text(size = 10))


p

ggsave(p,filename = "~/Documents/test.pdf",height = 9/2.5,width = 16/2.5 )

1.2 Strings

mutate(qseqid = str_split_fixed(qseqid, fixed(":"), n=3)[,1]) %>% 

1.3 Plot section

1.3.1 heatmap

############################################################################################################################
### import library and file path
library(tidyverse)
library(pheatmap)
library(RColorBrewer)
infileS <- c("data/001_matrix_pheatmap.txt")
taxaDB <- c("data/001_taxa_InfoDB.txt")
df <- read.delim(infileS,stringsAsFactors = F)
dftaxa <- read.delim(taxaDB,stringsAsFactors = F)
dfinner <- inner_join(data.frame(Taxa=colnames(df)),dftaxa,by="Taxa")
df <- df %>%  select(1:6,all_of(dfinner$Taxa)) ## 选择有亚洲信息的和t

### as matrix
df_num = as.matrix(df[,-1:-6])
# df_num
# df_num[is.na(df_num)] <- 0  ##将缺失值设置为0
# df_num
### 为矩阵添加行名,以行号命名
# rownames(df_num) = paste("row",seq(1,dim(df)[1],1),sep = "")
rownames(df_num) = df$TriadsBlockID
#ncol(df_num)
#nrow(df_num)
### 标准化数据by column
df_num_scale = scale(df_num) ### z = (x — u) / s

### 先画一个最原始的图
# pheatmap(df_num_scale,main = "pheatmap default")
### 去除列上的树
# pheatmap(df_num_scale,cluster_cols = F,main = "pheatmap row cluster")
### 在行上也进行标准化,可以进行行与行之间的比较。注意这里是z score 标准化
# pheatmap(df_num_scale,scale = "row",cluster_cols = T,main = "pheatmap row cluster")

### 建立注释信息数据框,并添加行名
annotation_row = data.frame(rowannotation1 = factor(df$Chr))
# rownames(annotation_row) = paste("row",seq(1,dim(df)[1],1),sep = "") # name matching
rownames(annotation_row) = df$TriadsBlockID
#levels(annotation_row$rowannotation1)
### col annotation data frame
annotation_col=data.frame(colannotation=factor(dfinner$Group))
rownames(annotation_col) =  dfinner$Taxa ######## try

### 自定注释信息的颜色列表
colB <- brewer.pal(9,"Set3")[c(1:7)]
# colC <- c("#fd8582","#ffcbcc","#967bce","#cccdfe","#4bcdc6","#cdfffc","darkgray")
colC <- c("#9900ff","#7B241C","#dbb3ff","#A9A9A9","#fc6e6e","#FF9900","#82C782")
rwb <- colorRampPalette(colors = c("red", "white", "skyblue"))(50)

ann_colors <-  list(colannotation = c(Cultivar=colC[1],LR_Africa=colC[2],
                                                       LR_America=colC[3],LR_CSA=colC[4],
                                                       LR_EA=colC[5],LR_EU=colC[6],
                                                       LR_WA=colC[7]),
                    rowannotation1= c("1A"=colB[1],"2A"=colB[2],"3A"=colB[3],
                                      "4A"=colB[4],"5A"=colB[5],"6A"=colB[6],"7A"=colB[7]))
# ann_colors

### 自定义行名,注意:行名的顺序与原始表格的顺序一致,而不是聚类后的顺序; 也可以显示个别标签
#labels_row = df$TriadsBlockID

# labels_row = c("", "", "", "TP53", "", "", "her2", "", "", "", "", "", "", "", "") 
### start to plot !!
p <- pheatmap(
  df_num_scale,
  # df_num,
  # log10(df_num+1) , ## 可以是列标准化的矩阵,也可以是原始矩阵
  scale = "row",
  annotation_row = annotation_row, ## 注释数据框
  annotation_colors = ann_colors, ## 对应注释数据框的因子颜色
  annotation_col = annotation_col,
  annotation_names_row = F,
  annotation_names_col = F,
  main = "",
  cluster_rows = T,
  cluster_cols = T,
  # gaps_row=c(7,14,21,28,35,42),
  # gaps_col=c(3,6,9),
  # cellheight = 8,
  # cellwidth = 8,
  border=FALSE,
  # border_color = "red", border=TRUE,
  show_colnames = F,
  show_rownames = F,
  # labels_row = labels_row,
  # angle_col = "45",
  fontsize = 8, ### all text
  # display_numbers = TRUE,number_color = "blue",number_format = "%.1e" ## display num
  # treeheight_row = 30, treeheight_col = 50 ## tree height,fault 50
  # clustering_method = "average",
  # clustering_distance_rows = "correlation",
  # cutree_rows  = 7, ### Cut the heatmap to pieces
  # cutree_cols = 4, ### Cut the heatmap to pieces
  # gaps_row = c(4, 8),
  color = rwb
  # color = colorRampPalette(brewer.pal(8, "Blues"))(25)
)

#str(p, max.level = 2)
#ggsave(p,filename = "/Users/Aoyue/Documents/heatmap.pdf",height = 7,width = 7)
#ggsave(p,filename = "/Users/Aoyue/Documents/heatmap.png",height = 7,width = 7)
# df_cluster <- df_num[p$tree_row$order, p$tree_col$order]
# write.csv(df_cluster,file="/Users/Aoyue/Documents/value.csv",row.names =T)

1.5 Color

display.brewer.all()

1.6 Trick skills

1.6.1 Pointrange

  • point size: fatten
  • line size: size
# make test data
df <- data.frame(y=10, ymin=1, ymax=20, x=1)
# store ggplot object
p <- ggplot(data=df, aes(y=y, ymin=ymin, ymax=ymax, x=x)) 
p + geom_pointrange(fill='blue', color='grey', shape=21, fatten = 10, size = 2)+
  theme_minimal()

1.6.2 Join 最形象的教程

https://www.jianshu.com/p/1f4c7bfed3d4

1.6.3 dplyr trick - %in%

AoSubspecies <- c("EU","CL")
dflrcl <- dfall %>% filter(GroupBySubcontinent %in% AoSubspecies) %>% droplevels() 

1.6.4 %>% list -> unset

dfci <- dfeu %>% group_by(SlightlyOrStrongly) %>% summarise(ci=list(mean_cl_normal(T_notEqualVariances))) %>% unnest

1.6.5 custom function in summarise

extractABD <- function(chr){
      library(tidyverse)
      unionChr <- str_c(str_sub(chr[1],5,5),str_sub(chr[2],5,5),str_sub(chr[3],5,5))
      return(unionChr)
    }
    df_filter1 <- df
    df_filter2 <- df %>% 
      group_by(qseqid) %>% 
      summarise(n=n()) %>% 
      left_join(x=df_filter1,y=.,by="qseqid") %>% 
      filter(n==3) %>% 
      mutate(sub=substring(Chr,5,5)) %>% 
      arrange(sub) %>% 
      group_by(qseqid) %>% 
      summarise(unionChr = extractABD(Chr)) %>% 
      left_join(x=df_filter1,y=.,by="qseqid")

1.7 R publish

1.7.1 publish the book

bookdown::publish_book(render = 'local')

1.8 Function

1.8.1 identical : if the two dataframe is equal

identical(a1, a2)

all.equal(a1, a2) #给出哪一列的结果有差别,数值型变量给出均数的相对差异;字符型给出不一致的字符的数目。

match(a1, a2) #NA代表该列不一致 e.g. NA NA 3 (present the first and second column is not equal)

which(a1!=a2, arr.ind = TRUE)

1.8.2 count.fields 统计每行的字段数

`num.fields = count.fields(“combine_test.txt,” sep=“\t”)`\

`num.fields = count.fields(“combine_test.txt,” sep=“\t,”quote = "")`

1.8.3 Merge data and add group

aoaddGroup <- function(infileS,group){
  dfin <- read.delim(infileS)
  dfout <- dfin %>% mutate(DelGroup = group)
  return(dfout)
}

factor1 <- c("003_nonsynGERPandDerivedSIFT","004_nonsynDerivedSIFT","005_GERP","006_nonsynGERPandDerivedSIFT_correction","007_nonsynDerivedSIFT_correction","008_GERP_correction")

inputDir <- c("/Users/Aoyue/project/wheatVMapII/003_dataAnalysis/005_vcf/047_referenceEvaluation/001_delCount_filterLR_CL")
df <- NULL
for (i in factor1) {
  filename <- paste(i,"_additiveDeleterious_ANCbarleyVSsecalePasimony_vmap2_bychr_bySub_delVSsynonymous.txt",sep="")
  infileS <- file.path(inputDir,filename)
  dfo <- aoaddGroup(infileS,i)
  df <- rbind(dfo,df)
}

1.9 Multiple file deal with

inputDir <- c("")
fsList <- list.files(inputDir)
length(fsList)
dfout <- NULL
for (i in fsList) {
  filename <- str_replace(i,".txt","")
  df <- read.table(file.path(inputDir,i),header = T,sep="\t",row.names = NULL)
  df <- df %>% mutate(Subspecies = filename)
  dfout <- rbind(dfout,df)
}

inputDir <- c("")
outputDir <- c("data/001_popDepthBP/")
fsList <- list.files(inputDir)
length(fsList)
for (i in fsList) {
  df <- read.table(file.path(inputDir,i),header = T,sep = "\t",row.names = NULL)
  outfileS <- file.path(outputDir,str_replace(i,".txt.gz",".txt"))

  dfout <- df %>% select(1:16,21)
  write.table(dfout,file=outfileS,quote=F,col.name=T,row.names=F,sep = "\t")
  print(paste(nrow(dfout),"        ", i ,sep = "")) ### log
}