论文

Removing unwanted variation from large-scale RNA sequencing data with PRPS

https://www.nature.com/articles/s41587-022-01440-w#data-availability

数据链接

https://zenodo.org/record/6459560#.Y2D2NHZBzid

https://zenodo.org/record/6392171#.Y2D2SXZBzid

代码链接

https://github.com/RMolania/TCGA_PanCancer_UnwantedVariation

今天推文重复的图没有出现在论文中，是论文中提供的代码里的一个图

image.png

但是没有能够重复出来论文中用到的作图数据，所以这里用R语言自带的鸢尾花数据集来演示

首先是论文中提供的两个自定义函数，一个是用来做主成分分析的pca,

.pca <- function(data, is.log) {
  if (is.log)
    data <- data
  else
    data <- log2(data + 1)
  svd <- base::svd(scale(
    x = t(data),
    center = TRUE,
    scale = FALSE
  ))
  percent <- svd$d ^ 2 / sum(svd$d ^ 2) * 100
  percent <-
    sapply(seq_along(percent),
           function(i) {
             round(percent[i], 1)
           })
  return(list(
    sing.val = svd,
    variation = percent))
}

一个是用来作图展示结果的
用到了ggplot2 ggpubr 和 cowplot 包

.scatter.density.pc <- function(
  pcs, 
  pc.var, 
  group.name, 
  group, 
  color, 
  strokeSize, 
  pointSize, 
  strokeColor,
  alpha,
  title
){
  pair.pcs <- utils::combn(ncol(pcs), 2)
  pList <- list()
  for(i in 1:ncol(pair.pcs)){
    if(i == 1){
      x <- pair.pcs[1,i]
      y <- pair.pcs[2,i]
      p <- ggplot(mapping = aes(
        x = pcs[,x], 
        y = pcs[,y], 
        fill = group)) +
        xlab(paste0('PC', x, ' (', pc.var[x], '%)')) +
        ylab(paste0('PC', y, ' (', pc.var[y], '%)')) +
        geom_point(
          aes(fill = group), 
          pch = 21, 
          color = strokeColor, 
          stroke = strokeSize, 
          size = pointSize,
          alpha = alpha) +
        scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) +
        scale_y_continuous(breaks = scales::pretty_breaks(n = 5)) +
        ggtitle(title) +
        theme(
          legend.position = "right",
          panel.background = element_blank(), 
          axis.line = element_line(colour = "black", size = 1.1),
          legend.background = element_blank(),
          legend.text = element_text(size = 12),
          legend.title = element_text(size = 14),
          legend.key = element_blank(),
          axis.text.x = element_text(size = 10),
          axis.text.y = element_text(size = 10),
          axis.title.x = element_text(size = 14),
          axis.title.y = element_text(size = 14)) +
        guides(fill = guide_legend(override.aes = list(size = 4))) + 
        scale_fill_manual(name = group.name, values = color)
      
      le <- ggpubr::get_legend(p)
    }else{
      x <- pair.pcs[1,i]
      y <- pair.pcs[2,i]
      p <- ggplot(mapping = aes(
        x = pcs[,x], 
        y = pcs[,y], 
        fill = group)) +
        xlab(paste0('PC', x, ' (',pc.var[x],  '%)')) +
        ylab(paste0('PC', y, ' (',pc.var[y], '%)')) +
        geom_point(
          aes(fill = group), 
          pch = 21, 
          color = strokeColor, 
          stroke = strokeSize,
          size = pointSize,
          alpha = alpha) +
        scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) +
        scale_y_continuous(breaks = scales::pretty_breaks(n = 5)) +
        theme(
          panel.background = element_blank(), 
          axis.line = element_line(colour = "black", size = 1.1),
          legend.position = "none",
          axis.text.x = element_text(size = 10),
          axis.text.y = element_text(size = 10),
          axis.title.x = element_text(size = 14),
          axis.title.y = element_text(size = 14)) +
        scale_fill_manual(values = color, name = group.name)
    }
    p <- p + theme(legend.position = "none")
    xdens <- cowplot::axis_canvas(p, axis = "x")+
      geom_density(
        mapping = aes(
          x = pcs[,x], 
          fill = group),
        alpha = 0.7, 
        size = 0.2
      ) +
      theme(legend.position = "none") +
      scale_fill_manual(values = color)
    
    ydens <- cowplot::axis_canvas(
      p, 
      axis = "y", 
      coord_flip = TRUE) +
      geom_density(
        mapping = aes(
          x = pcs[,y],
          fill = group),
        alpha = 0.7,
        size = 0.2) +
      theme(legend.position = "none") +
      scale_fill_manual(name = group.name, values = color) +
      coord_flip()
    
    p1 <- insert_xaxis_grob(
      p,
      xdens,
      grid::unit(.2, "null"),
      position = "top"
    )
    p2 <- insert_yaxis_grob(
      p1,
      ydens,
      grid::unit(.2, "null"),
      position = "right"
    )
    pList[[i]] <- ggdraw(p2)
  }
  pList[[i+1]] <- le
  return(pList)
}

这两个自定义函数在函数名前都加了一个点，暂时不知道加这个点和不加有什么区别，将这两个函数放到一个文件里

source("pca_and_ggplot2.R")

library(ggplot2)
library(ggpubr)
library(cowplot)

pca.ncg<-.pca(data = iris[,1:4],
              is.log = FALSE)
.scatter.density.pc(pcs = pca.ncg$sing.val$v[, 1:3],
                    pc.var = pca.ncg$variation,
                    strokeColor = 'gray30',
                    strokeSize = .2,
                    pointSize = 2,
                    alpha = .6,
                    title = "A",
                    group.name = "B",
                    group=iris$Species,
                    color=c("red","blue","green")) -> p

do.call(
  gridExtra::grid.arrange,
  c(p,ncol=4))

image.png

这里自定义的pca结果可视化函数参数还挺多的，这里就不逐个介绍了，争取抽时间录制成视频介绍，敬请期待

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