使用facet_wrap和geom_bar错误分组

Question

我有一个看起来像的数据集：

Pair,Total readNUM,Uniquely mapped readNUM,Batch
CP3027_merged_trimmed,83750278,75237898,P160411
CP3028_merged_trimmed,94621036,86736510,P160411
CP3029_merged_trimmed,89051500,80999978,P160411
CP3030_merged_trimmed,100399436,89787060,P160411
CP3032_merged_trimmed,91591620,83432242,P160411
CP3036_merged_trimmed,81272998,73541686,P160411
CP3037_merged_trimmed,85289630,77513350,P160411
CP3058_merged_trimmed,85092730,78269348,P160411
CP3059_merged_trimmed,81696100,74981834,P160411
CP3060_merged_trimmed,88098518,79513000,P160411
CP3065_merged_trimmed,75924870,68052566,P160411
CP3066_merged_trimmed,89746438,79933004,P160411
CP3068_merged_trimmed,82041060,73183314,P160411
CP3074_merged_trimmed,82162078,74321554,P160411
CP3078_merged_trimmed,77500516,70835090,P160411
CP3185_merged_trimmed,99023950,90729150,P160411
CP3081_trimmed,88044290,76494036,P160475
CP3084_trimmed,88741718,79056712,P160475
CP3085_trimmed,81212190,71851198,P160475
CP3091_trimmed,82675822,72460250,P160475
CP3092_trimmed,96965168,86268756,P160475
CP3093_trimmed,68717952,60125000,P160475
CTL001_trimmed,74160410,63648530,P160475
CTL004_trimmed,100474172,85822840,P160475
CP1950_trimmed,162963640,136601638,SO41314
CP2160_trimmed,77991138,65584038,SO41314
CP2171_trimmed,89296686,75887918,SO41314
CP2204_trimmed,71691448,60311650,SO41314
CP2325_trimmed,95803886,80002310,SO41314
CP3133_trimmed,76307744,64964436,SO41314
CP3249_trimmed,78904062,67382812,SO41314
CP3541_trimmed,67020194,56703314,SO41314
CP0678_trimmed,19986550,18575050,SBSQ8092_1
CP2032_trimmed,21722580,20138926,SBSQ8092_1
CP2164_trimmed,23275750,21359668,SBSQ8092_1
CP2544_trimmed,22376982,20652410,SBSQ8092_1
CP2695_trimmed,22264402,20631472,SBSQ8092_1
CP3127_trimmed,33050232,29990758,SBSQ8092_2
CP3141_trimmed,24164170,21655048,SBSQ8092_2
CP2997_trimmed,96381034,91772686,NG-10002
L0218_001_trimmed,257181636,81639268,x
L0218_002_trimmed,263258410,31357342,x
L0218_003_trimmed,183642720,30657224,x

对于每个样品（Pair = col 1），我绘制了映射读数（第3列）上的读数总数（第2列），并通过实验（第4列）对条进行着色。 结果如图1所示：

样品未订购，难以比较属于同一实验（批次）的样品。 为了使图更具可读性，我使用facet_wrap进行了实验分组。

结果图（图2）具有正确的颜色，但样本未按facet_wrap（或facet_grid）放在正确的组中。

在其他帖子（“r facet_wrap not grouping properly with geom_point”stackover link）中，建议避免使用$来引用aes()中的变量。 然后我修改了代码（BamSummaryRaw$Pair -> Pair和BamSummaryRaw$Batch -> Batch），但问题仍然存在。

这是我使用的代码：

library(ggplot2);library(cowplot);library(grid)
library(gridExtra);library(reshape2)

BamSummaryRaw <- read.table('BamSummary_B38.csv',header=T,sep=',',quote='',check.names=F,stringsAsFactors=FALSE)

# convert # in millions
totReadsMill <- BamSummaryRaw$`Total readNUM`/1000000
totMappedMill <- BamSummaryRaw$`Uniquely mapped paired readNUM`/1000000

experiments <- BamSummaryRaw$Batch

# plots
gg.MAIN <- ggplot(BamSummaryRaw,aes(x=Pair,fill=experiments))

gg.reads <- gg.MAIN + geom_bar(aes(y=totReadsMill),fill='white',colour='black',stat='identity',width = 0.5,show.legend = T) +
   geom_bar(aes(y=totMappedMill),colour='black',stat='identity',width = 0.5,show.legend = T) +
   theme(axis.text.x = element_text(angle = 20, hjust = 1,size=5)) +
   labs(x='samples',y='# of reads [10^6]') +
   ylim(0,200)

#prova <- gg.reads + facet_grid(~BamSummaryRaw$Batch,scales='free_x', labeller = label_wrap_gen(multi_line=FALSE))
prova <- gg.reads + facet_wrap(~Batch,scales='free_x',nrow=1)

Answer 1

如果我理解正确你需要的是订购x轴。您可以使用scale_x_discrete()执行此操作。只需将样本矢量传递给函数，x轴就会按照它排序。

# Using OPs data
# Rename for easier manipulation
colnames(BamSummaryRaw) <- c("pair", "total", "mapped", "batch")

# Plot
library(ggplot2)
ggplot(BamSummaryRaw, aes(pair, fill = batch)) +
    # Number of total reads (M)
    geom_bar(aes(y = total / 1e6), fill = "white", color = "black",
             stat = "identity", position = "dodge", width = 0.5) +
    # Number of mapped reads (M)
    geom_bar(aes(y = mapped / 1e6), color = "black", stat = "identity", position = "dodge", width = 0.5) +
    # Order x-axis
    scale_x_discrete(limits = BamSummaryRaw$pair) +
    # Add labels
    labs(title = "Number of reads",
         subtile = "Total and Mapped / Grouped per batch",
         x = NULL,
         y = "Number of reads, M",
         fill = "Batch") +
    # Nicer theme
    theme_classic() +
    theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank(),
          legend.position = "bottom")

表示NGS统计数据的另一种方法是绘制映射读数的百分比：

ggplot(BamSummaryRaw, aes(pair, fill = batch)) +
    geom_bar(aes(y = mapped * 100 / total), color = "black", stat = "identity", position = "dodge", width = 0.5) +
    scale_x_discrete(limits = BamSummaryRaw$pair) +
    labs(title = "Percentage of mapped reads",
         subtile = "Total and Mapped / Grouped per batch",
         x = NULL,
         y = "Mapped, %",
         fill = "Batch") +
    theme_minimal() +
    scale_y_continuous(limits = c(0, 100)) +
    theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank(),
          legend.position = "bottom")