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如何计算R中多个单元格之间的距离?

Julian · 技术社区 · 5 年前

我有多个不同的表型和每个细胞的xy坐标。计算同一张幻灯片中每个细胞之间距离的最简单方法是什么?我的数据集有10万个以上的单元格,所以我正在努力找出最有效的方法。

数据帧的一个例子是:

Xposition <- c(1,6,4,7,9,4,8,6,4)

Yposition <- c(6,3,2,6,3,6,1,3,7)

Phenotype <- c("A", "A", "B", "C", "C", "A", "A", "B", "B")

SlideID <- c(111,111,111,111,111,112,112,112,112)

df <- data.frame(Xposition, Yposition, Phenotype, SlideID)

我在寻找一种可以给我一个数据帧的东西,其中的输出是这样的:

CellType1 <- c("A", "A", "A", "A", "A", "A", "A", "B", "B", "C", "A", "A", "A", "A", "A", "B")

Celltype2 <- c("A", "B", "C", "C", "B", "C", "C", "C", "C", "C", "A", "B", "B", "B", "B", "B")

Distance <- c("5.83", "5", "6", "8.54", "2.23", "3.16", "3", "5", "5.09", "3.6", "6.4", "3.6", "1", "2.82", "7.21", "4.47")

SlideID <- c("111", "111", "111", "111", "111", "111", "111", "111", "111", "111", "112", "112", "112", "112", "112", "112")

distancedf <- data.frame(CellType1, Celltype2, Distance, SlideID)

谢谢你的帮助!

0 回复 | 直到 5 年前

r2evans 5 年前

我认为这里有模棱两可的余地,但是。。。

res <- as.data.frame.table(as.matrix(dist(df[,1:2])))
res$Var2 <- df$Phenotype[res$Var2]
res$SlideID <- df$SlideID[res$Var1]
res$Var1 <- df$Phenotype[res$Var1]
head(res)
#   Var1 Var2     Freq SlideID
# 1    A    A 0.000000     111
# 2    A    A 5.830952     111
# 3    B    A 5.000000     111
# 4    C    A 6.000000     111
# 5    C    A 8.544004     111
# 6    A    A 3.000000     112

由此,您应该能够过滤掉 0 这很容易,但我想把它放在这里,以展示实际发生的事情。实际上 as.data.frame.table(...) 是怎么回事

dist(df[,1:2])
#          1        2        3        4        5        6        7        8
# 2 5.830952                                                               
# 3 5.000000 2.236068                                                      
# 4 6.000000 3.162278 5.000000                                             
# 5 8.544004 3.000000 5.099020 3.605551                                    
# 6 3.000000 3.605551 4.000000 3.000000 5.830952                           
# 7 8.602325 2.828427 4.123106 5.099020 2.236068 6.403124                  
# 8 5.830952 0.000000 2.236068 3.162278 3.000000 3.605551 2.828427         
# 9 3.162278 4.472136 5.000000 3.162278 6.403124 1.000000 7.211103 4.472136

通过这是:

as.matrix(dist(df[,1:2]))
#          1        2        3        4        5        6        7        8        9
# 1 0.000000 5.830952 5.000000 6.000000 8.544004 3.000000 8.602325 5.830952 3.162278
# 2 5.830952 0.000000 2.236068 3.162278 3.000000 3.605551 2.828427 0.000000 4.472136
# 3 5.000000 2.236068 0.000000 5.000000 5.099020 4.000000 4.123106 2.236068 5.000000
# 4 6.000000 3.162278 5.000000 0.000000 3.605551 3.000000 5.099020 3.162278 3.162278
# 5 8.544004 3.000000 5.099020 3.605551 0.000000 5.830952 2.236068 3.000000 6.403124
# 6 3.000000 3.605551 4.000000 3.000000 5.830952 0.000000 6.403124 3.605551 1.000000
# 7 8.602325 2.828427 4.123106 5.099020 2.236068 6.403124 0.000000 2.828427 7.211103
# 8 5.830952 0.000000 2.236068 3.162278 3.000000 3.605551 2.828427 0.000000 4.472136
# 9 3.162278 4.472136 5.000000 3.162278 6.403124 1.000000 7.211103 4.472136 0.000000

归根结底

head(as.data.frame.table(as.matrix(dist(df[,1:2]))))
#   Var1 Var2     Freq
# 1    1    1 0.000000
# 2    2    1 5.830952
# 3    3    1 5.000000
# 4    4    1 6.000000
# 5    5    1 8.544004
# 6    6    1 3.000000

还有 0.000 s是距离矩阵的对角线(在 dist(...) ).

每 SlideID :

lapply(split(df, df$SlideID), function(x) { 
  res <- as.data.frame.table(as.matrix(dist(x[,1:2])))
  res$Var2 <- x$Phenotype[res$Var2]
  res$SlideID <- x$SlideID[res$Var1]
  res$Var1 <- x$Phenotype[res$Var1]
  res
})
# $`111`
#    Var1 Var2     Freq SlideID
# 1     A    A 0.000000     111
# 2     A    A 5.830952     111
# 3     B    A 5.000000     111
# 4     C    A 6.000000     111
# 5     C    A 8.544004     111
# 6     A    A 5.830952     111
# 7     A    A 0.000000     111
# 8     B    A 2.236068     111
# 9     C    A 3.162278     111
# 10    C    A 3.000000     111
# 11    A    B 5.000000     111
# 12    A    B 2.236068     111
# 13    B    B 0.000000     111
# 14    C    B 5.000000     111
# 15    C    B 5.099020     111
# 16    A    C 6.000000     111
# 17    A    C 3.162278     111
# 18    B    C 5.000000     111
# 19    C    C 0.000000     111
# 20    C    C 3.605551     111
# 21    A    C 8.544004     111
# 22    A    C 3.000000     111
# 23    B    C 5.099020     111
# 24    C    C 3.605551     111
# 25    C    C 0.000000     111
# $`112`
#    Var1 Var2     Freq SlideID
# 1     A    A 0.000000     112
# 2     A    A 6.403124     112
# 3     B    A 3.605551     112
# 4     B    A 1.000000     112
# 5     A    A 6.403124     112
# 6     A    A 0.000000     112
# 7     B    A 2.828427     112
# 8     B    A 7.211103     112
# 9     A    B 3.605551     112
# 10    A    B 2.828427     112
# 11    B    B 0.000000     112
# 12    B    B 4.472136     112
# 13    A    B 1.000000     112
# 14    A    B 7.211103     112
# 15    B    B 4.472136     112
# 16    B    B 0.000000     112