Input: There are 6 input matrices of same dimensions 3 input matrices from normal tissues:
GeneA GeneB
GeneA 31 4
GeneB 5 8
GeneA GeneB
GeneA 5 14
GeneB 5 8
GeneA GeneB
GeneA 30 14
GeneB 45 7
3 input matrices from cancer tissues:
GeneA GeneB
GeneA 11 4
GeneB 5 18
GeneA GeneB
GeneA 7 14
GeneB 15 4
GeneA GeneB
GeneA 30 14
GeneB 45 7
output:
GeneA GeneB
GeneA t-test({31,5,30},{11,7,13}) t-test({4,14,14},{4,14,14})
GeneB t-test({5,5,45},{5,15,45}) t-test({8,8,7},{18,4,7})
Output matrix will have the p-values from the test
CodePudding user response：
The code that follows conducts the t-tests on data in a 3-dim array form. This makes it easier to loop through the data sets, extract the required vectors and run the tests.
From tabular data to arrays
normal <- mget(ls(pattern = "^normal"))
cancer <- mget(ls(pattern = "^cancer"))
anorm <- array(dim = c(dim(normal[[1]]), length(normal)))
acanc <- array(dim = c(dim(cancer[[1]]), length(cancer)))
for(i in seq_along(normal)) {
anorm[, , i] <- unlist(normal[[i]])
acanc[, , i] <- unlist(cancer[[i]])
}
The t-tests
Create a results list first, then run the tests, then extract the p-values into a data.frame.
t_test_list <- vector("list", length = prod(dim(anorm)[1:2]))
for(j in seq(dim(anorm)[2])) {
for(i in seq(dim(anorm)[1])) {
x <- anorm[i, j, ]
y <- acanc[i, j, ]
k <- i dim(anorm)[1]*(j - 1)
t_test_list[[k]] <- t.test(x, y)
}
}
t_test_pval <- normal[[1]]
t_test_pval[] <- sapply(t_test_list, `[[`, 'p.value')
t_test_pval
#> GeneA GeneB
#> GeneA 0.6176386 1.0000000
#> GeneB 0.8618120 0.6850214
Created on 2022-06-01 by the reprex package (v2.0.1)
Data
x<-' GeneA GeneB
GeneA 31 4
GeneB 5 8'
normal1 <- read.table(textConnection(x), header = TRUE)
x<-' GeneA GeneB
GeneA 5 14
GeneB 5 8 '
normal2 <- read.table(textConnection(x), header = TRUE)
x<-' GeneA GeneB
GeneA 30 14
GeneB 45 7 '
normal3 <- read.table(textConnection(x), header = TRUE)
x<-' GeneA GeneB
GeneA 11 4
GeneB 5 18 '
cancer1 <- read.table(textConnection(x), header = TRUE)
x<-' GeneA GeneB
GeneA 7 14
GeneB 15 4 '
cancer2 <- read.table(textConnection(x), header = TRUE)
x<-' GeneA GeneB
GeneA 30 14
GeneB 45 7 '
cancer3 <- read.table(textConnection(x), header = TRUE)
Created on 2022-06-01 by the reprex package (v2.0.1)
CodePudding user response：
Perhaps the neatest way to this is to bind both sets of matrices into 2 x 2 x 3 arrays, then use Map to get the t tests at each of the four combinations.
Let's say your matrices are called norm1, norm2, norm3 for the normal tissue and ca1, ca2 and ca3 for the cancer tissue. Then we can do:
library(abind)
norm <- abind::abind(norm1, norm2, norm3, along = 3)
canc <- abind::abind(ca1, ca2, ca3, along = 3)
pvals <- Map(function(i, j) {
t.test(norm[i, j, ], canc[i, j, ])$p.val
}, i = c(1:2, 1:2), j = c(1, 1, 2, 2))
matrix(unlist(pvals), 2, dimnames = dimnames(norm1))
#> GeneA GeneB
#> GeneA 0.6176386 1.0000000
#> GeneB 0.8618120 0.6850214
Created on 2022-06-01 by the reprex package (v2.0.1)
Reproducible data
norm1 <- structure(c(31L, 5L, 4L, 8L), dim = c(2L, 2L), dimnames = list(
c("GeneA", "GeneB"), c("GeneA", "GeneB")))
norm2 <- structure(c(5L, 5L, 14L, 8L), dim = c(2L, 2L), dimnames = list(
c("GeneA", "GeneB"), c("GeneA", "GeneB")))
norm3 <- structure(c(30L, 45L, 14L, 7L), dim = c(2L, 2L), dimnames = list(
c("GeneA", "GeneB"), c("GeneA", "GeneB")))
ca1 <- structure(c(11L, 5L, 4L, 18L), dim = c(2L, 2L), dimnames = list(
c("GeneA", "GeneB"), c("GeneA", "GeneB")))
ca2 <- structure(c(7L, 15L, 14L, 4L), dim = c(2L, 2L), dimnames = list(
c("GeneA", "GeneB"), c("GeneA", "GeneB")))
ca3 <- structure(c(30L, 45L, 14L, 7L), dim = c(2L, 2L), dimnames = list(
c("GeneA", "GeneB"), c("GeneA", "GeneB")))