查找重叠区域并提取相应的值

Question

您如何找到重叠坐标并提取重叠区域的相应 seg.mean 值？

data1
      Rl       pValue     chr  start    end     CNA
      2        2.594433   6 129740000 129780000 gain
      2        3.941399   6 130080000 130380000 gain
      1        1.992114  10  80900000  81100000 gain
      1        7.175750  16  44780000  44920000 gain

数据2

ID     chrom   loc.start   loc.end   num.mark  seg.mean
8410     6     129750000  129760000      8430   0.0039
8410     10    80907000   81000000        5   -1.7738
8410     16    44790000   44910000       12    0.0110

数据输出

  Rl       pValue     chr  start    end        CNA    seg.mean
  2        2.594433   6 129750000   129760000  gain   0.0039
  1        1.992114  10  80907000   81000000   gain   -1.7738  
  1        7.175750  16  44790000   44910000   gain   0.0110

Answer 1

正如@Roland 正确建议的那样，这是一个可能的data.table::foverlaps 解决方案

library(data.table)
setDT(data1) ; setDT(data2) # Convert data sets to data.table objects
setnames(data2, c("loc.start", "loc.end"), c("start", "end")) # Rename columns so they will match in both sets
setkey(data2, start, end) # key the smaller data so foverlaps will work
foverlaps(data1, data2, nomatch = 0L)[, 1:5 := NULL][] # run foverlaps and remove the unnecessary columns
#    seg.mean Rl   pValue chr   i.start     i.end  CNA
# 1:   0.0039  2 2.594433   6 129740000 129780000 gain
# 2:  -1.7738  1 1.992114  10  80900000  81100000 gain
# 3:   0.0110  1 7.175750  16  44780000  44920000 gain

或者

indx <- foverlaps(data1, data2, nomatch = 0L, which = TRUE) # run foverlaps in order to find indexes using `which`
data1[indx$xid][, seg.mean := data2[indx$yid]$seg.mean][] # update matches
#    Rl   pValue chr     start       end  CNA seg.mean
# 1:  2 2.594433   6 129740000 129780000 gain   0.0039
# 2:  1 1.992114  10  80900000  81100000 gain  -1.7738
# 3:  1 7.175750  16  44780000  44920000 gain   0.0110

Answer 2

当我们处理基因组数据时，将数据保存为 Granges 对象更容易，然后我们可以使用 - mergeByOverlaps(g1,g2) 来自 GenomicRanges 包，请参见下面的示例：

library("GenomicRanges")

#data
x1 <- read.table(text="Rl       pValue     chr  start    end     CNA
      2        2.594433   6 129740000 129780000 gain
      2        3.941399   6 130080000 130380000 gain
      1        1.992114  10  80900000  81100000 gain
      1        7.175750  16  44780000  44920000 gain",header=TRUE)

x2 <- read.table(text="ID     chrom   loc.start   loc.end   num.mark  seg.mean
8410     6     129750000  129760000      8430   0.0039
8410     10    80907000   81000000        5   -1.7738
8410     16    44790000   44910000       12    0.0110",header=TRUE)

g1 <-  GRanges(seqnames=paste0("chr",x1$chr),
               IRanges(start=x1$start,
                       end=x1$end),
               CNA=x1$CNA,
               Rl=x1$Rl)


g2 <-  GRanges(seqnames=paste0("chr",x2$chrom),
               IRanges(start=x2$loc.start,
                       end=x2$loc.end),
               ID=x2$ID,
               num.mark=x2$num.mark,
               seq.mean=x2$seg.mean)

mergeByOverlaps(g1,g2)
# DataFrame with 3 rows and 7 columns
#                               g1      CNA        Rl                             g2        ID  num.mark  seq.mean
#                        <GRanges> <factor> <integer>                      <GRanges> <integer> <integer> <numeric>
# 1  chr6:*:[129740000, 129780000]     gain         2  chr6:*:[129750000, 129760000]      8410      8430    0.0039
# 2 chr10:*:[ 80900000,  81100000]     gain         1 chr10:*:[ 80907000,  81000000]      8410         5   -1.7738
# 3 chr16:*:[ 44780000,  44920000]     gain         1 chr16:*:[ 44790000,  44910000]      8410        12    0.0110

---

编辑：添加sessionInfo()输出：

R version 3.2.0 (2015-04-16)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=English_United Kingdom.1252  LC_CTYPE=English_United Kingdom.1252    LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C                            LC_TIME=English_United Kingdom.1252    

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] GenomicRanges_1.20.3 GenomeInfoDb_1.4.0   IRanges_2.2.1        S4Vectors_0.6.0      BiocGenerics_0.14.0 
[6] BiocInstaller_1.18.1

loaded via a namespace (and not attached):
[1] XVector_0.8.0 tools_3.2.0