如何找到重叠坐标并提取重叠区域各自的 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
最佳答案
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
关于r - 找到重叠区域并提取各自的值，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/29648127/