遍历数据框输出图列表

时间:2020-02-06 14:00:42

标签: r list dataframe bioinformatics seurat

我尝试绘制函数图或我的前20个基因。这就是为什么我创建了一个数据帧列表,这些数据帧存在或不同的列包含值和名称的原因。 数据框中的这些列之一是基因列。我的代码将前20个基因转换为功能图。但是现在我在一些现有的数据帧中遇到了问题 少于20个基因。这导致我的代码中止。

因为每页最多要有5个功能图,所以我不能只定义一个计数器。

谢谢您的输入。

我的列表或数据框的示例 listGroups

group1_2: 'data.frame': 68 obs. of 7 variables:
    ..$ p_val: num [1:68] 1.15 1.43 ...
    ..$ score: num [1:68] 15.5 27.14 ...
    ..$ gene: Factor w/ 68 levels "BRA1", "NED",...: 41 52 ...
group2_3: 'data.frame': 3 obs. of 7 variables:
    ..$ p_val: num [1:3] 1.15 1.43 ...
    ..$ score: num [1:3] 15.5 27.14 ...
    ..$ gene: Factor w/ 3 levels "BCL12", "DEF1",...: 41 52 ...

代码

groupNames <- c("cluster1_2","cluster2_3","cluster3_4","cluster4_5","cluster5_6")
for (i in 1:length(listGroups)) {
  Grouplist <- listGroups[[i]]
  genesList <- Grouplist['gene']
  lengths(geneList)
  print(groupNames[i])
  # Make Featureplots for top20 DE genes per cluster_group
  pdf(file=paste0(sampleFolder,"/Featureplots_cluster_",groupNames[i],"_",sampleName,".pdf"))
  print(FeaturePlot(object = seuratObj, features = c(as.character(genesList[1:5,]))))
  print(FeaturePlot(object = seuratObj, features = c(as.character(genesList[6:10,]))))
  print(FeaturePlot(object = seuratObj, features = c(as.character(genesList[11:15,]))))
  print(FeaturePlot(object = seuratObj, features = c(as.character(genesList[16:20,]))))
  dev.off()
}

3 个答案:

答案 0 :(得分:2)

对于每个基因列表,您都可以选择一个这样的基因作图(如PDF所示,该图在更大的情况下看起来很好):

  • 使用combine=FALSE并将要绘制的要素数量限制为rownames(pbmc_small)[1 : min(20, nrow(pbmc_small))]之类,以免发生错误
  • 然后导出单个图的列表(允许主题绘制)并使用cowplot::plot_grid将图绘制为pdf
  • 您可以导出为pdf(而不是在函数(plot(out))中进行绘图(可以将文件名作为第二个参数传递给函数)。
library(Seurat)
genelist <- list(
    l1 = sample(rownames(pbmc_small), 23),
    l2 = sample(rownames(pbmc_small), 14),
    l3 = sample(rownames(pbmc_small), 4))

plotFeatures <- function(x){
    p <- FeaturePlot(object = pbmc_small, 
        features = x[1 : min(20, length(x))], 
        combine = FALSE, label.size = 2)
    out <- cowplot::plot_grid(plotlist = p, ncol = 5, nrow = 4)
    plot(out)
}
lapply(genelist, plotFeatures)

答案 1 :(得分:1)

未经测试,类似的东西应该可以工作。而不是为每个5个基因调用5次print,而是根据基因数量在循环中 n 次调用它。如果我们有10个基因, forloop 将打印两次,如果有20个,则我们调用print 4次,等等:

groupNames <- c("cluster1_2","cluster2_3","cluster3_4","cluster4_5","cluster5_6")

for (i in 1:length(listGroups)) {
  Grouplist <- listGroups[[i]]
  genesList <- Grouplist['gene']
  #lengths(geneList)
  print(groupNames[i])
  # Make Featureplots for top20 DE genes per cluster_group

  # make chunks of 5 each. 
  myChunks <- split(genesList, ceiling(seq_along(genesList)/5))

  pdf(file=paste0(sampleFolder,"/Featureplots_cluster_",groupNames[i],"_",sampleName,".pdf"))

  # loop through genes plotting 5 genes each time.
  for(x %in% seq(myChunks) ){
    print(FeaturePlot(object = seuratObj, features = myChunks[[ x ]]))
  }

  dev.off()
}

答案 2 :(得分:0)

感谢zx8754和user12728748的输入。我发现了两个解决问题的方法。

        for (i in 1:length(listGroups)) {
      Grouplist <- listGroups[[1]]
      genesList <- Grouplist['gene']
      print(groupNames[1])

    ## Solution 1
    # Here all genes are printed. I didn't find a way yet to limited to 20

      # make chunks of 5 each. 
      myChunks <- split(genesList,ceiling(seq(lengths(genesList))/5))
      # Make Featureplots for top20 DE genes per cluster_group
      pdf(file=paste0(sampleFolderAggr,"results/Featureplots_",groupNames[i],"_",sampleNameAggr,".pdf"))
      # loop through genes plotting 5 genes each time.
      for(x in 1:min(5, length(myChunks) ){
        # Create a list of 5 genes
        my5Genes <- as.list(myChunks[[x]])

        print(FeaturePlot(object = seuratObj, features = c(as.character(my5Genes$gene))))
      }
      dev.off()

    ## Solution 2

    pdf(file=paste0(sampleFolderAggr,"results/Featureplots_",groupNames[i],"_",sampleNameAggr,".pdf"))

    plotFeatures <- function(x){
        p <- FeaturePlot(object = seuratObj, features = c(as.character(x[1: min(20, lengths(x)),])), combine = FALSE, label.size = 2)
        out <- cowplot::plot_grid(plotlist = p, ncol = 5, nrow = 4)
        # Make Featureplots for top20 DE genes per cluster_group
        plot(out)
        }
      lapply(genelist, plotFeatures)
      dev.off()
    }