如何使用data.table跨多个列(loci)按组有效地计算等位基因频率(比例)

Question

我有一个data.table的等位基因身份(行是个体,列是基因座),由一个单独的列分组.我想按组计算每个基因座的等位基因频率(比例).示例数据表:

    DT = data.table(Loc1=rep(c("G","T"),each=5), 
      Loc2=c("C","A"), Loc3=c("C","G","G","G",
      "C","G","G","G","G","G"), 
    Group=c(rep("G1",3),rep("G2",4),rep("G3",3)))
    for(i in 1:3)
        set(DT, sample(10,2), i, NA)
    > DT
        Loc1 Loc2 Loc3 Group
     1:    G   NA    C    G1
     2:    G    A    G    G1
     3:    G    C    G    G1
     4:   NA   NA   NA    G2
     5:    G    C   NA    G2
     6:    T    A    G    G2
     7:    T    C    G    G2
     8:    T    A    G    G3
     9:    T    C    G    G3
    10:   NA    A    G    G3

我的问题是,当我尝试按组进行计算时,只有组中存在的等位基因ID被识别,所以我很难找到可以告诉我例如所有3组中基因座1的G的比例的代码.举个简单的例子,计算每个基因座上第一个等位基因的总和(不是比例):

    > fun1<- function(x){sum(na.omit(x==unique(na.omit(x))[1]))}
    > DT[,lapply(.SD,fun1),by=Group,.SDcols=1:3]
       Group Loc1 Loc2 Loc3
    1:    G1    3    1    1
    2:    G2    1    2    2
    3:    G3    2    2    3

对于G1,结果是Loc1有3个G,但对于G3,它表示Loc1有2个T,而不是G的数量.在这种情况下,我想要两个G的数量.因此,关键问题是等位基因身份是由群体决定的,而不是整个群体.我尝试使用我想在计算中使用的等位基因身份创建一个单独的表,但无法弄清楚如何将其包含在fun1中,以便在上面的lapply中引用正确的单元格.等位基因表:

    > fun2<- function(x){sort(na.omit(unique(x)))}
    > allele.id<-data.table(DT[,lapply(.SD,fun2),.SDcols=1:3])
    > allele.id
       Loc1 Loc2 Loc3
    1:    G    A    C
    2:    T    C    G

Answer 1

将data.table转换为长格式可能是明智之举.这将使其更容易用于进一步的计算(或ggplot2例如制作可视化).随着melt功能data.table(其工作原理相同melt的功能reshape2包),你可以从广角到长格式转换:

DT2 <- melt(DT, id = "Group", variable.name = "loci")

如果要NA在熔解操作期间删除-values,可以添加na.rm = TRUE上述调用(na.rm = FALSE是默认行为).

然后你可以使计数和比例变量如下:

DT2 <- DT2[, .N, by = .(Group, loci, value)][, prop := N/sum(N), by = .(Group, loci)]

得出以下结果:

> DT2
    Group loci value N      prop
 1:    G1 Loc1     G 3 1.0000000
 2:    G2 Loc1    NA 1 0.2500000
 3:    G2 Loc1     G 1 0.2500000
 4:    G2 Loc1     T 2 0.5000000
 5:    G3 Loc1     T 2 0.6666667
 6:    G3 Loc1    NA 1 0.3333333
 7:    G1 Loc2    NA 1 0.3333333
 8:    G1 Loc2     A 1 0.3333333
 9:    G1 Loc2     C 1 0.3333333
10:    G2 Loc2    NA 1 0.2500000
11:    G2 Loc2     C 2 0.5000000
12:    G2 Loc2     A 1 0.2500000
13:    G3 Loc2     A 2 0.6666667
14:    G3 Loc2     C 1 0.3333333
15:    G1 Loc3     C 1 0.3333333
16:    G1 Loc3     G 2 0.6666667
17:    G2 Loc3    NA 2 0.5000000
18:    G2 Loc3     G 2 0.5000000
19:    G3 Loc3     G 3 1.0000000

我想要它以宽格式返回,你可以使用dcast多个变量:

DT3 <- dcast(DT2, Group + loci ~ value, value.var = c("N", "prop"), fill = 0)

这导致:

> DT3
   Group loci N_A N_C N_G N_T N_NA    prop_A    prop_C    prop_G    prop_T   prop_NA
1:    G1 Loc1   0   0   3   0    0 0.0000000 0.0000000 1.0000000 0.0000000 0.0000000
2:    G1 Loc2   1   1   0   0    1 0.3333333 0.3333333 0.0000000 0.0000000 0.3333333
3:    G1 Loc3   0   1   2   0    0 0.0000000 0.3333333 0.6666667 0.0000000 0.0000000
4:    G2 Loc1   0   0   1   2    1 0.0000000 0.0000000 0.2500000 0.5000000 0.2500000
5:    G2 Loc2   1   2   0   0    1 0.2500000 0.5000000 0.0000000 0.0000000 0.2500000
6:    G2 Loc3   0   0   2   0    2 0.0000000 0.0000000 0.5000000 0.0000000 0.5000000
7:    G3 Loc1   0   0   0   2    1 0.0000000 0.0000000 0.0000000 0.6666667 0.3333333
8:    G3 Loc2   2   1   0   0    0 0.6666667 0.3333333 0.0000000 0.0000000 0.0000000
9:    G3 Loc3   0   0   3   0    0 0.0000000 0.0000000 1.0000000 0.0000000 0.0000000

另外,直接的方法是使用melt和dcast在一个呼叫(这是@弗兰克的回答第一部分的简化版本):

DT2 <- dcast(melt(DT, id="Group"), Group + variable ~ value)

这使:

> DT2
   Group variable A C G T NA
1:    G1     Loc1 0 0 3 0  0
2:    G1     Loc2 1 1 0 0  1
3:    G1     Loc3 0 1 2 0  0
4:    G2     Loc1 0 0 1 2  1
5:    G2     Loc2 1 2 0 0  1
6:    G2     Loc3 0 0 2 0  2
7:    G3     Loc1 0 0 0 2  1
8:    G3     Loc2 2 1 0 0  0
9:    G3     Loc3 0 0 3 0  0

由于默认聚合函数dcast是length,您将自动获取每个值的计数.

使用数据:

DT <- structure(list(Loc1 = c("G", "G", "G", NA, "G", "T", "T", "T", "T", NA), 
                     Loc2 = c(NA, "A", "C", NA, "C", "A", "C", "A", "C", "A"), 
                     Loc3 = c("C", "G", "G", NA, NA, "G", "G", "G", "G", "G"), 
                     Group = c("G1", "G1", "G1", "G2", "G2", "G2", "G2", "G3", "G3", "G3")), 
                .Names = c("Loc1", "Loc2", "Loc3", "Group"), row.names = c(NA, -10L), class = c("data.table", "data.frame"))