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[跟着NC学作图]--散点图

[跟着NC学作图]--散点图

作者: 小杜的生信筆記 | 来源:发表于2022-11-27 16:34 被阅读0次

    本期内容为[跟着NC学作图]--散点图

    文章题目:



    感兴趣的可以自己去看!



    代码部分:
    1. 导入数据,文章中的代码是比较复杂的,那么我自己就使用画图前的数据。
    > nb_Omicron_Delta
    sample  nb_Delta  nb_Omicron  ratio_Delta  ratio_Omicron  ID  proto  ext  Delta  Coinf
    22PlCoInf-MIDV2xEMAGx0x100_S373  0  47  0  1  22PlCoInf-MIDV2xEMAGx0x100  MIDNIGHT V2  EMAG  0  FALSE
    22PlCoInf-MIDV2xEMAGx100x0_S384  25  0  1  0  22PlCoInf-MIDV2xEMAGx100x0  MIDNIGHT V2  EMAG  100  FALSE
    22PlCoInf-MIDV2xEMAGx10x90_S374  24  47  0.96  1  22PlCoInf-MIDV2xEMAGx10x90  MIDNIGHT V2  EMAG  10  TRUE
    22PlCoInf-MIDV2xEMAGx20x80_S375  25  46  1  0.978723404  22PlCoInf-MIDV2xEMAGx20x80  MIDNIGHT V2  EMAG  20  TRUE
    22PlCoInf-MIDV2xEMAGx30x70_S376  25  44  1  0.936170213  22PlCoInf-MIDV2xEMAGx30x70  MIDNIGHT V2  EMAG  30  TRUE
    22PlCoInf-MIDV2xEMAGx40x60_S377  25  44  1  0.936170213  22PlCoInf-MIDV2xEMAGx40x60  MIDNIGHT V2  EMAG  40  TRUE
    
    sample  nb_Delta  nb_Omicron  ratio_Delta  ratio_Omicron  ID  proto  ext  Delta  Coinf  median_Delta  IQR_Delta  median_Omicron  IQR_Omicron
    22PlCoInf-MIDV2xEMAGx0x100_S373  0  47  0  1  22PlCoInf-MIDV2xEMAGx0x100  MIDNIGHT V2  EMAG  0  FALSE  0  0  99  1.5
    22PlCoInf-MIDV2xEMAGx100x0_S384  25  0  1  0  22PlCoInf-MIDV2xEMAGx100x0  MIDNIGHT V2  EMAG  100  FALSE  99  0  0  0
    22PlCoInf-MIDV2xEMAGx10x90_S374  24  47  0.96  1  22PlCoInf-MIDV2xEMAGx10x90  MIDNIGHT V2  EMAG  10  TRUE  33  11.5  64  10
    22PlCoInf-MIDV2xEMAGx20x80_S375  25  46  1  0.978723404  22PlCoInf-MIDV2xEMAGx20x80  MIDNIGHT V2  EMAG  20  TRUE  53  12  43.5  18
    22PlCoInf-MIDV2xEMAGx30x70_S376  25  44  1  0.936170213  22PlCoInf-MIDV2xEMAGx30x70  MIDNIGHT V2  EMAG  30  TRUE  67  13  29.5  13.25
    22PlCoInf-MIDV2xEMAGx40x60_S377  25  44  1  0.936170213  22PlCoInf-MIDV2xEMAGx40x60  MIDNIGHT V2  EMAG  40  TRUE  70  10  26  10.5
    22PlCoInf-MIDV2xEMAGx50x50_S378  25  46  1  0.978723404  22PlCoInf-MIDV2xEMAGx50x50  MIDNIGHT V2  EMAG  50  TRUE  78  5  18.5  8.75
    
    1. Plot A
    nb_Omicron_Delta$Delta = as.numeric(nb_Omicron_Delta$Delta)
    p1 <- ggplot(data=nb_Omicron_Delta, aes(x=ratio_Omicron, y=ratio_Delta ,colour=Delta)) 
    
    p1 <-  p1 +
        geom_point(aes(colour=Delta,shape=Coinf),alpha=0.9) +
        facet_grid(.~proto) + 
        ylab("Delta-specific mutations detection rate") + xlab("Omicron-specific mutations detection rate") +
        scale_color_gradient2(low = "steelblue1", mid = "cyan4", high = "tomato",
          breaks=c(0,10,20,30,40,50,60,70,80,90,100), midpoint = 50,
          name = "Delta:Omicron", labels = c("0:100","10:90","20:80", "30:70","40:60","50:50","60:40","70:30", "80:20","90:10","100:0")) +
        scale_shape(name = "Experimental\nCoinfection") +
        geom_vline(xintercept = 0.25,linetype =  "dotted", alpha = 0.5, inherit.aes = FALSE) +
        geom_hline(yintercept = 0.90,linetype =  "dotted", alpha = 0.5, inherit.aes = FALSE) +
        theme_bw() 
    
    1. Plot B
    p2 <- ggplot(data=table_MAF, aes(x=Delta, y=median_Delta)) 
    
    p2 <-  p2 +
        geom_smooth(color="grey", fill="grey", linetype="blank") +
        geom_point(aes(colour=Delta,shape=Coinf)) +
        facet_grid(.~proto) + 
        ylab("Measured Frequency (%)") + xlab("Expected Frequency (%)") +
        geom_abline(intercept = 0, slope = 1,linetype =  "dotted", alpha = 0.5, inherit.aes = FALSE) +
        scale_color_gradient2(low = "steelblue1", mid = "cyan4", high = "tomato",
          breaks=c(0,10,20,30,40,50,60,70,80,90,100), midpoint = 50,
          name = "Delta:Omicron", labels = c("0:100","10:90","20:80", "30:70","40:60","50:50","60:40","70:30", "80:20","90:10","100:0")) +
        scale_shape(name = "Experimental\nCoinfection") +
        theme_bw()
    
    1. Plot C
    
    plot_AF_fig1C <- function(vcf_file,annot_file, ncol=11) {
      require(data.table)
      library(ggplot2)
    
      annot=annot_file
    
      ##########################
      ### annotate vcf
      ##########################
      vcf=vcf_file
      
      vcf$VOC <- sapply(vcf$nt_mut, function(x) ifelse(is.element(x,annot$nt_mut),annot$var[x==annot$nt_mut],NA)) ## For each variant, determine whether it is Delta- or OMICRON-specific
    
      vcf$proto = sapply(vcf$ID , function(x) strsplit(x,"x")[[1]][1])
      vcf$proto = gsub("MID","MIDNIGHT ",vcf$proto)
      vcf$proto = gsub("V4","ARTIC V4",vcf$proto)
      vcf$proto = gsub("V41","V4.1",vcf$proto)
      vcf$proto = gsub("MIDNIGHT $","MIDNIGHT V1",vcf$proto)
      vcf$proto = factor(vcf$proto,levels=c("MIDNIGHT V1","MIDNIGHT V2","ARTIC V4","ARTIC V4.1"))
    
      ########## plot AF bars of DELTA-specific and/or OMICRON-specific mutations
      vcfDO = vcf[!is.na(vcf$VOC),]
    
      # Plot
      p <- ggplot(data=vcfDO, aes(x=nt_pos, y=af, group = sample )) 
    
      p <- p +
        geom_bar(aes(fill = VOC), stat="identity") + 
        geom_point(data=vcfDO[vcfDO$af>50,], aes(x=nt_pos, y=af, group = sample,color = VOC),size=1,alpha=0.8)+
        facet_wrap(.~proto, ncol=ncol) + 
        ylab("Mutation frequency (%)") + xlab("Nucleotide position") +
        scale_color_manual(values=c("tomato","darkgrey","steelblue1"),name = "Mutations in\nconsensus", labels = c("Delta-specific", "shared", "Omicron-specific")) +
        scale_fill_manual(values=c("tomato","darkgrey","steelblue1"),name = "Mutations", labels = c("Delta-specific", "shared", "Omicron-specific")) +
        geom_hline(yintercept = 50,linetype =  "dotted", alpha = 0.5, inherit.aes = FALSE) +
        theme_bw() + theme( axis.text.x  = element_text(angle=45, size=8,hjust =1, vjust=1)) +
        theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
    
      return(p)
      
    }
    
    1. 合并图
    
    plot_grid(p1, p2,p3, ncol = 1,labels = c("A","B","C"), align = 'v',axis = "rl",rel_heights=c(1,1,1.1))
    ggsave("Fig1.pdf",width = 10 ,height=9 )
    ggsave("Fig1.png",width = 10 ,height=9)
    

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