I have a set of data x which consists of 12 columns and 167 rows. The first column is compound Id for each row. I want to run a t.test for 3 column as one group and the other 3 groups as the second group, separately for each row. My code is as below but it does not work.
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)))
My intention was doing a t.test for each metabolite (compound) between AC groups and SC groups, these are two group of cells.
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
The t.test is used to compare two data sets. Collecting two data sets each from three different columns of a matrix can be done like this:
data_a = c(x[,2:4])
data_b = c(x[,4:8])
These two data sets can be evaluated using t.test at this point:
t.test(data_a, data_b)
Collecting the data from three columns each for two different compounds for a given row (amino acid) we modify and add a loop:
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))
}
With this fake data:
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
You can do the following to extract (for example) the p-values:
library(zoo)
rollapply(t(df[, -1]), function(x) t.test(x)$p.value, width = 3, by = 3)
[,1] [,2] [,3] [,4]
[1,] 0.6308340 0.5702970 0.5783582 0.6468241
[2,] 0.2511564 0.8327439 0.1617192 0.2005518
[3,] 0.9026407 0.4309623 0.4156030 0.6441069
[4,] 0.3878145 0.4909217 0.6239915 0.2747601
x$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")])
Another alternative is using a package.
Your data:
df <- rbind(c(27612820, 22338050, 15359640, 19741350, 18726880, 18510800, 10914980, 12071660, 16036180, 16890860, 16066960, 16364300),
c(7067206, 7172234, 5933320, 136272600, 131596800, 134717600, 6102838, 7186256, 6770344, 140127100, 155341300, 151748000),
c(3151398, 2141378, 1240904, 11522180, 8907711, 9842342, 1677299, 2265826, 2942991, 11690360, 12552660, 12102620)
)
df <- data.frame(df)
rownames(df) <- c("alanine", "arginine", "asparagine")
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")
Then to run a t-test on every row between AC and SC groups:
library(matrixTests)
> row_t_welch(df[,c("AC-1", "AC-2", "AC-3")], df[,c("SC-1", "SC-2", "SC-3")])
obs.x obs.y obs.tot mean.x mean.y mean.diff var.x var.y stderr df statistic pvalue conf.low conf.high alternative mean.null conf.level
alanine 3 3 6 21770170 13007607 8762563.3 37776970798900 7213669482133 3872580.5 2.736945 2.26271945 0.1171389 -4259692 21784819 two.sided 0 0.95
arginine 3 3 6 6724253 6686479 37774.0 471939373529 298723602417 506840.9 3.807645 0.07452832 0.9443398 -1397926 1473474 two.sided 0 0.95
asparagine 3 3 6 2177893 2295372 -117478.7 913496858185 401148784303 661978.3 3.472571 -0.17746605 0.8690016 -2070931 1835973 two.sided 0 0.95
来源：https://stackoverflow.com/questions/28119894/doing-t-test-for-columns-for-each-row-in-data-set