methylKit 包中 getCorrelation 函数怎么

今天有一个小伙伴遇到就是不知道为啥 getCorrelation 函数输出的结果都不能储存到变量中，我也尝试但是费解。
但是本质就是 先计算每个位点中每个样品的甲基化值，然后通过 cor() 函数来计算相关性。

> test <- getCorrelation(methylBase.obj,method="pearson",plot=FALSE)
          test1     test2     ctrl1     ctrl2
test1 1.0000000 0.9252530 0.8767865 0.8737509
test2 0.9252530 1.0000000 0.8791864 0.8801669
ctrl1 0.8767865 0.8791864 1.0000000 0.9465369
ctrl2 0.8737509 0.8801669 0.9465369 1.0000000

查看 test 是空的，不论我我怎么转换。。

然后我就开启了查看源码模式

• https://rdrr.io/bioc/methylKit/src/R/methylDBFunctions.R
  

先聊点无关的，我们可以看到如果不指定 nrow 参数多少行，默认是用前 200 万行来计算相关性，如果指定，就按照你指定多少行来计算。那么问题来了，结果明明是由 cor() 函数来计算得到的相关性结果，怎么就不能保存到一个变量中呢？我也不知道。
然后我就拆开来运行了

• 首先将几个相关函数 copy 出来

headTabix：用来将数据转换成 data.frame 、data.table、GRanges 文件格式的函数
fread.gzipped：用来读取文件的函数，调用 data.table 包中的 fread 函数来进行

headTabix <- function(tbxFile, nrow = 10,
                      return.type = c("data.table","data.frame","GRanges") ){
  
  if(nrow < 1e6) {
    if( class(tbxFile) != "TabixFile" ){
      tbxFile <- TabixFile(tbxFile)
      open(tbxFile)
    }
    df <- getTabixByChunk( tbxFile,chunk.size=nrow,return.type)
  } 
  else {
    returnDt = if(return.type[1] == "data.table") TRUE else FALSE 
    df <- fread.gzipped(tbxFile,nrow = nrow, stringsAsFactors = TRUE, data.table = returnDt)
    
    if(return.type[1] == "GRanges"){
      return( GRanges(seqnames=as.character(df$V1),
                      ranges=IRanges(start=df$V2, end=df$V3),
                      strand=df$V4, df[,5:ncol(df)]) )
    } 
  }
  
  return(df)
  
}

fread.gzipped <- function(filepath, ..., runShell=TRUE){
  
  # check if file is gzipped (either gz or bgz)
  if (R.utils::isGzipped(filepath, method = "content")){
    
    if( runShell && ( .Platform$OS.type == "unix" ) ) {
      # being on unix we can run shell comands to uncompress the file
      if(!file.exists(filepath) ) 
        # to protect against exploits
        stop("No such file: ", filepath)
      # run the command
      ext = if( tools::file_ext(filepath) == "bgz") "bgz" else "gz"
      tmpFile <- paste0(tempdir(),"/",gsub(ext,"",basename(filepath)))
      if(!file.exists(tmpFile)) {
        system2("gunzip",args = c("-c",shQuote(filepath)), stdout = tmpFile)
      }
      filepath <- tmpFile
      # on.exit(unlink( tmpFile ), add = TRUE)
      
    } else {
      # on windows we have to decompress first ... 
      ext = if( tools::file_ext(filepath) == "bgz") "bgz" else "gz"
      tmpFile <- R.utils::gunzip(filepath,temporary = TRUE, overwrite = TRUE,
                                 remove = FALSE, ext = ext, FUN = gzfile)
      filepath <- tmpFile
      # on.exit(unlink( tmpFile ), add = TRUE)
    }
    
  }
  ## finally we read in the uncompressed file  
  df <- data.table::fread(file = filepath,...)
  
  
  return(df)
}

• 好了，函数 copy 出来了，就进行分布操作

其实本质就是先计算每个位点中每个样品的甲基化值，然后通过 cor() 函数来计算相关性。

mydbpath <- methylKit::getDBPath(makeMethylDB(methylBase.obj,"my/path"))
# [1] "my/path\\methylBase_58b42b3d58d3.txt.bgz"

data = headTabix(mydbpath, nrow = 2e6, return.type = "data.frame")
# V1      V2      V3 V4 V5 V6 V7  V8  V9 V10 V11 V12 V13 V14 V15 V16
# 1 chr21 9853296 9853296  + 17 10  7 333 268  65  18  16   2 395 341  54
# 2 chr21 9853326 9853326  + 17 12  5 329 249  79  16  14   2 379 284  95
# 3 chr21 9860126 9860126  + 39 38  1  83  78   5  83  83   0  41  40   1
# 4 chr21 9906604 9906604  + 68 42 26 111  97  14  23  18   5  37  33   4
# 5 chr21 9906616 9906616  + 68 52 16 111 104   7  23  14   9  37  27  10
# 6 chr21 9906619 9906619  + 68 59  9 111 109   2  22  18   4  37  29   8

# 数据中代表样品 C 的数目的列
methylBase.obj@numCs.index
# [1]  6  9 12 15

# 数据中代表样品 T 的数目的列
methylBase.obj@numTs.index
# [1]  7 10 13 16

# meth = C/(C + T)
meth.mat = data[, methylBase.obj@numCs.index]/
  ( data[,methylBase.obj@numCs.index] + data[,methylBase.obj@numTs.index] )
#       V6        V9       V12       V15
# 1 0.5882353 0.8048048 0.8888889 0.8632911
# 2 0.7058824 0.7591463 0.8750000 0.7493404
# 3 0.9743590 0.9397590 1.0000000 0.9756098
# 4 0.6176471 0.8738739 0.7826087 0.8918919
# 5 0.7647059 0.9369369 0.6086957 0.7297297
# 6 0.8676471 0.9819820 0.8181818 0.7837838

# 样品名称
methylBase.obj@sample.ids
# [1] "test1" "test2" "ctrl1" "ctrl2"

names(meth.mat) = methylBase.obj@sample.ids
# test1     test2     ctrl1     ctrl2
# 1 0.5882353 0.8048048 0.8888889 0.8632911
# 2 0.7058824 0.7591463 0.8750000 0.7493404
# 3 0.9743590 0.9397590 1.0000000 0.9756098
# 4 0.6176471 0.8738739 0.7826087 0.8918919
# 5 0.7647059 0.9369369 0.6086957 0.7297297
# 6 0.8676471 0.9819820 0.8181818 0.7837838

a <- cor(meth.mat, method = "pearson")
 a
#           test1     test2     ctrl1     ctrl2
# test1 1.0000000 0.9252530 0.8767865 0.8737509
# test2 0.9252530 1.0000000 0.8791864 0.8801669
# ctrl1 0.8767865 0.8791864 1.0000000 0.9465369
# ctrl2 0.8737509 0.8801669 0.9465369 1.0000000


class(a)
# [1] "matrix"