基于一行标准的数据框列的唯一组合[关闭]

Question

我的data.frame 有 200 多列，并在下面包含了一个子集，包括与此问题相关的列：

>df
Variant   Pos     ID    DB.0.count    DB.1.count    sample1    sample2    sample3    sample4    sample5    sample6    sample7    sample8    sample9    sample10 
variant5  1234567 A     5             5             1/0        1/0        1/0        1/1        1/1        0/0        1/0        0/0        1/0        1/1
.         .       .     .             .             F1         F1         F1         F2         F2         F3         F4         F4         F4         F5

我想：

1. 将 samples1-sample10 列的所有可能组合，其中每个组合包含来自每个 F 数的一个样本，即每个组合包含 5 个样本，每个样本来自 F1、F2、F3， F4，F5。

所以在上面的例子中会有 18 种组合，例如：

第一个组合是 sample1, sample4, sample6, sample7, sample10

第二个组合是 sample1, sample4, sample6, sample8, sample10

第三个组合是 sample1, sample4, sample6, sample9, sample10

在阅读了相关帖子后，我玩过unique、duplicated 和distinct，但一无所获。

然后我想将每个唯一组合输出到新的data.frame，对样本中的每个变量执行计数并将结果输出到新列，并执行如下所示的 Fisher 精确测试并输出到新列，如下，下面的代码应该可以做到：（在这里学习的费雪代码：Fisher's exact test on values from large dataframe and bypassing errors）

df.combo.1$pop.0/0.count <- apply(df.combo.1[,6:10], 1, function(u) length(which(grepl("0/0",u))==TRUE) )    
df.combo.1$pop.1/0.count <- apply(df.combo.1[,6:10], 1, function(u) length(which(grepl("1/0",u))==TRUE) )  
df.combo.1$pop.1/1.count <- apply(df.combo.1[,6:10], 1, function(u) length(which(grepl("1/1",u))==TRUE) )  

df.combo.1$pop.0.count <- ( 2*(apply(df.combo.1[,6:10], 1, function(u) length(which(grepl("0/0",u))==TRUE) )) + apply(df.combo.1[,6:10], 1, function(u) length(which(grepl("0/1",u))==TRUE) ) )
df.combo.1$pop.1.count <- ( 2*(apply(df.combo.1[,6:10], 1, function(u) length(which(grepl("1/1",u))==TRUE) )) + apply(df.combo.1[,6:10], 1, function(u) length(which(grepl("0/1",u))==TRUE) ) )

res <- NULL
for (i in 1:nrow(df.combo.1)){
table <- matrix(c(df.combo.1[i, 4],    df.combo.1[i, 5], df.combo.1[i, 14], df.combo.1[i, 15]), ncol = 2, byrow = TRUE)
# if any NA occurs in your table save an error in p else run the fisher test
if(any(is.na(table))) p <- "error" else p <- fisher.test(table)$p.value
# save all p values in a vector
res <- c(res,p)
}
df.combo.1$fishers <- res 


>df.combo.1
Variant   Pos     ID    DB.0.count    DB.1.count    sample1    sample4    sample6    sample7    sample10   pop.0/0.count    pop.1/0.count    pop.1/1.count    pop.0.count    pop.1.count     fishers 
variant5  1234567 A     5             5             1/0        1/1        0/0        1/0        1/1        1                2                2                4              6               1.0000
.         .       .     .             .             F1         F2         F3         F4         F5

2. 最后，我想创建一个data.frame，其中列出了 Fisher 对每个独特组合的确切 p 值，如下所示：

>new.df
combo    fishers
1        1.0000
2        1.0000
3        1.0000
4        1.0000
etc

我认为整个练习可能需要某种 for 循环？

Answer 1

我想我已经掌握了你想要的东西。对于我认为您在第 1 部分中遇到的问题，我使用了 which 和 expand.grid 的组合来整理。

对于第 2 部分，一旦将数据排列在每个观察的 1 行上，cbind 就相当容易了。

看起来您每次观察使用 2 行（除非这只是一个格式化的东西），这使得它真的很难（但并非不可能，只是需要更多的杂耍）所以我将数据合并到一个行中。这应该是一个非常简单的转换，只需将每个“第二”行中的相应列附加到每个“第一”行，然后每隔第二行删除一次。

这可以更有效、更整洁地完成，但我认为这很有效，应该很容易扩展到其他情况。

问候， 乔什

# provided demo data
# Variant   Pos     ID    DB.0.count    DB.1.count    sample1    sample2    sample3    sample4    sample5    sample6    sample7    sample8    sample9    sample10 
# variant5  1234567 A     5             5             1/0        1/0        1/0        1/1        1/1        0/0        1/0        0/0        1/0        1/1
# .         .       .     .             .             F1         F1         F1         F2         F2         F3         F4         F4         F4         F5


# create data frame in long format
test.df <- as.data.frame(t(c("variant5",1234567,"A",5,5,"1/0","1/0","1/0","1/1","1/1","0/0","1/0","0/0","1/0","1/1","F1", "F1", "F1", "F2", "F2", "F3", "F4", "F4", "F4", "F5")))
# ensure as character format
test.df[] <- lapply(test.df, as.character)
# get postions of "F" data
F1.var <- which(test.df =="F1")
F2.var <- which(test.df =="F2")
F3.var <- which(test.df =="F3")
F4.var <- which(test.df =="F4")
F5.var <- which(test.df =="F5")
# get all combinations of the 5 F positions
Fcode.combinations <- expand.grid(F1.var,F2.var,F3.var,F4.var,F5.var)
# create results data frame
df.combo.1 <- as.data.frame(matrix(NA,ncol = 21, nrow = nrow(Fcode.combinations)))
# name variables
names(df.combo.1) <- c("Variant","Pos","ID","DB.0.count","DB.1.count",
                              "F1.sample.pos","F1.result",
                              "F2.sample.pos","F2.result",
                              "F3.sample.pos","F3.result",
                              "F4.sample.pos","F4.result",
                              "F5.sample.pos","F5.result",
                              "pop.0_0.count","pop.1_0.count","pop.1_1.count",
                              "pop.0.count","pop.1.count",
                              "fishers")
# copy in common data
df.combo.1[,1:5] <- test.df[,1:5]
# setup variables based on combination data
for(i in 1:nrow(Fcode.combinations)){
  df.combo.1[i,c(6,8,10,12,14)] <- Fcode.combinations[i,]
  # -10 to correct for the position of the results not the 'F type' data
  cycle.results <- as.numeric(Fcode.combinations[i,] -10)
  df.combo.1[i,c(7,9,11,13,15)] <- test.df[cycle.results]
}

# this is essentially your code with the column reference changed

df.combo.1$pop.0_0.count <- apply(df.combo.1[,c(7,9,11,13,15)], 1, function(u) length(which(grepl("0/0",u))==TRUE) )    
df.combo.1$pop.1_0.count <- apply(df.combo.1[,c(7,9,11,13,15)], 1, function(u) length(which(grepl("1/0",u))==TRUE) )  
df.combo.1$pop.1_1.count <- apply(df.combo.1[,c(7,9,11,13,15)], 1, function(u) length(which(grepl("1/1",u))==TRUE) )  

df.combo.1$pop.0.count <- ( 2*(apply(df.combo.1[,c(7,9,11,13,15)], 1, function(u) length(which(grepl("0/0",u))==TRUE) )) + apply(df.combo.1[,c(7,9,11,13,15)], 1, function(u) length(which(grepl("0/1",u))==TRUE) ) )
df.combo.1$pop.1.count <- ( 2*(apply(df.combo.1[,c(7,9,11,13,15)], 1, function(u) length(which(grepl("1/1",u))==TRUE) )) + apply(df.combo.1[,c(7,9,11,13,15)], 1, function(u) length(which(grepl("0/1",u))==TRUE) ) )

res <- NULL
for (i in 1:nrow(df.combo.1)){
  table <- matrix(as.numeric(c(df.combo.1[i, 4],    df.combo.1[i, 5], df.combo.1[i, 16], df.combo.1[i, 17])), ncol = 2, byrow = TRUE)
  # if any NA occurs in your table save an error in p else run the fisher test
  if(any(is.na(table))) p <- "error" else p <- fisher.test(table)$p.value
  # save all p values in a vector
  res <- c(res,p)
}
df.combo.1$fishers <- res 

# create results data
df.combo.1.results <- as.data.frame(cbind(1:nrow(df.combo.1),df.combo.1$fishers))
names(df.combo.1.results) <- c("combo","fishers")