对数据集中每行的列执行t.test

Question

我有一组数据x，由12列和167行组成。第一列是每行的复合Id。我想为3列运行一个t.test作为一个组，其他3个组作为第二个组，分别为每行。我的代码如下所示，但它不能工作。

for (i in 1:nrow(x)) {
 function(i)c(compound=i,
    t.test(x[2:4],x[8:10],
      x[x$compound==i, ],
      alternative='two.sided',conf.level=0.95)
    )
}
print(c(compound=i,t.test(x[2:4],x[8:10],x[x$compound==i,],
    alternative='two.sided',conf.level=0.95)))

我的目的是对AC组和SC组之间的每个代谢物(化合物)进行t.test，这是两组细胞。

compound    AC-1     AC-2     AC-3     AM-1      AM-2      AM-3      SC-1     SC-2     SC-3     SM-1      SM-2      SM-3
alanine     27612820 22338050 15359640 19741350  18726880  18510800  10914980 12071660 16036180 16890860  16066960  16364300
arginine    7067206  7172234  5933320  136272600 131596800 134717600 6102838  7186256  6770344  140127100 155341300 151748000
asparagine  3151398  2141378  1240904  11522180  8907711   9842342   1677299  2265826  2942991  11690360  12552660  12102620                        

Answer 1

t.test用于比较两个数据集。分别从矩阵的三个不同列收集两个数据集可以像这样完成：

data_a = c(x[,2:4])
data_b = c(x[,4:8])

此时可以使用t.test对这两个数据集进行评估：

t.test(data_a, data_b)

从三列收集数据，每列对应给定行(氨基酸)的两种不同化合物，我们修改并添加一个循环：

x <- matrix(rnorm(24, mean=0, sd=1), 4, ncol=6)
x
           [,1]       [,2]        [,3]      [,4]       [,5]       [,6]
[1,] -0.4810307  0.3996071  0.90663635 0.7487048  0.5787846  2.0231681
[2,] -2.0454921 -0.1225105 -1.04447522 0.9325333 -1.7782776  0.6856150
[3,] -0.3099937  1.2079548 -0.03835271 0.2751349  1.0111554 -0.4862846
[4,] -0.2834953  0.1930481 -0.57968344 0.1204925 -0.5015843  0.3690397

for(i in 1:nrow(x)){
data_a = c(x[i, 1:3])
data_b = c(x[i, 4:6])
print(t.test(data_a, data_b))
}

Answer 2

x$stat <- sapply(1:nrow(x), function(i) t.test(as.numeric(as.character(unlist(x[i,2:4]))), as.numeric(as.character(unlist(x[i,8:10]))))[c("p.value")])

Answer 3

有了这个假数据：

df <- data.frame(compound = c("alanine ", "arginine", "asparagine", "aspartate"))
df <- matrix(rnorm(12*4), ncol = 12)
colnames(df) <- c("AC-1", "AC-2", "AC-3", "AM-1", "AM-2", "AM-3", "SC-1", "SC-2", "SC-3", "SM-1", "SM-2", "SM-3")
df <- data.frame(compound = c("alanine ", "arginine", "asparagine", "aspartate"), df)
 df
    compound        AC.1        AC.2       AC.3       AM.1       AM.2        AM.3       SC.1       SC.2       SC.3         SM.1
1   alanine   1.18362683 -2.03779314 -0.7217692 -1.7569264 -0.8381042  0.06866567  0.2327702 -1.1558879  1.2077454  0.437707310
2   arginine -0.19610110  0.05361113  0.6478384 -0.1768597  0.5905398 -0.67945600 -0.2221109  1.4032349  0.2387620  0.598236199
3 asparagine  0.02540509  0.47880021 -0.1395198  0.8394257  1.9046667  0.31175358 -0.5626059  0.3596091 -1.0963363 -1.004673116
4  aspartate -1.36397906  0.91380826  2.0630076 -0.6817453 -0.2713498 -2.01074098  1.4619707 -0.7257269  0.2851122 -0.007027878
         SM.2        SM.3
1 -0.08419146  0.14275728
2 -1.44965718 -0.64314509
3  0.37673942 -0.07245741
4  0.52794136  1.62305413

您可以执行以下操作来提取(例如)p值：

library(zoo)
rollapply(t(df[, -1]), function(x) t.test(x)$p.value, width = 3, by = 3)
          [,1]      [,2]      [,3]      [,4]
[1,] 0.6308340 0.5702970 0.5783582 0.6468241
[2,] 0.2511564 0.8327439 0.1617192 0.2005518
[3,] 0.9026407 0.4309623 0.4156030 0.6441069
[4,] 0.3878145 0.4909217 0.6239915 0.2747601