Multiple ttests - r

Question

I want to perform multiple ttests on data in the following format
first column is "id"
with values (for example) 1,1,1,2,2,2
second column is "ratios"
with values 0.2, 0.18, 0.3, 1.5, 1.4, 1.6
for each instance of "id" I want to test all ratio values against all the ratio values in the dataframe
Right now I have this
data <- read.delim("clipboard", stringsAsFactors=FALSE) ##data to test
dist <- as.numeric(readClipboard()) ##distribution to test against
data$Ratio.Mean.H.L <- NA
data$p.value <- NA
for (i in 1:nrow(data))
if (nrow(data) > 1)
{
#welsh t-test
t.test.result <- t.test(data$ratio[i],dist,
alternative = "two.sided",
mu = 0,
paired = FALSE,
var.equal = FALSE,
conf.level = 0.95)
#writes data into the data.frame
data$p.value[i] <- t.test.result$p.value
}
write.table(data, file="C:/R_Temp/t-test.txt", sep = "\t")
I know this does not work, for one I am not sure I am only testing rows that share the same "id". I am also manually entering the distribution to test against, which is all entries in the "ratio" column.
How do I do this correct? and add multiple testing correction (bonferroni)?

Answer 1

I suspect that MattParker's comment is going to be the biggest thing here: you are comparing a single number with a vector, and t.test will complain about that. Since you suggested that you want to perform tests per grouping variable (id), so in base R you probably want to use a function like by (or split). (There are great methods within dplyr and data.table as well.)
Using mtcars as sample data, I'll try to mimic your data:
dat <- mtcars[c("cyl", "mpg")]
colnames(dat) <- c("id", "ratio")
It isn't clear what you mean to use for dist, so I'll use the naïve
dist <- 1:10
Now you can do:
by(dat$ratio, dat$id, function(x) t.test(x, dist, paired = FALSE)$p.value)
# dat$id: 4
# [1] 2.660716e-10
# ------------------------------------------------------------
# dat$id: 6
# [1] 4.826322e-09
# ------------------------------------------------------------
# dat$id: 8
# [1] 2.367184e-07
If you want/need to deal with more than just ratio at a time, you can alternatively do this:
by(dat, dat$id, function(x) t.test(x$ratio, dist, paired = FALSE)$p.value)
# dat$id: 4
# [1] 2.660716e-10
# ------------------------------------------------------------
# dat$id: 6
# [1] 4.826322e-09
# ------------------------------------------------------------
# dat$id: 8
# [1] 2.367184e-07
The results from the call to by are a class "by", which is really just a repackaged list with some extra attributes:
res <- by(dat, dat$id, function(x) t.test(x$ratio, dist, paired = FALSE)$p.value)
class(res)
# [1] "by"
str(attributes(res))
# List of 4
# $ dim : int 3
# $ dimnames:List of 1
# ..$ dat$id: chr [1:3] "4" "6" "8"
# $ call : language by.data.frame(data = dat, INDICES = dat$id, FUN = function(x) t.test(x$ratio, dist, paired = FALSE)$p.value)
# $ class : chr "by"
So you can expand/access it however you would a list:
res[[1]]
# [1] 2.660716e-10
as.numeric(res)
# [1] 2.660716e-10 4.826322e-09 2.367184e-07
names(res)
# [1] "4" "6" "8"
(Realize that the different levels of dat$id are the integers 4, 6, and 8, so the names should correspond to your $id.)
Edit:
If you want the results in a data.frame, two options come to mind:
Repeat the p-value for each and every row, resulting in a lot of duplication. I discourage this method for several reasons; if you need it at some point, I suggest using option 2 and then merge.
Produce a data.frame with as many rows as unique id. Something like:
do.call(rbind.data.frame,
by(dat, dat$id, function(x) list(id=x$id[1], pv=t.test(x, dist, paired=F)$p.value)))
# id pv
# 4 4 1.319941e-03
# 6 6 2.877065e-03
# 8 8 6.670216e-05

Answer 2

OK, Sorry for the poorly defined question. I got help elsewhere and will post the script that worked for those that are interested. I want to calculate p-values for ratio changes in a proteomics experiment. To do this I make individual t-tests for all the ratio measurements for any given protein or PTM site.These measurements are compared to the median of all measurments (mu in the t.test function), or to the entire distribution of measurements. In one column I have "id"s which are unique for each entry, in the other column I have "values" (ratios). I will make t-tests comparing all "values" that occur with any given unique "id". for ease of use I paste the table into the script, rather than calling it from a file (it saves me a step).
data <- read.delim("clipboard", stringsAsFactors=FALSE) ##data to test(two columns "id" and "value") Log-transfrom ratios!!
summary(data)
med <- median(data$value)
# function for the id-grouped t-test
calc_id_ttest <- function(d) #col1: id, col2:values
{
colnames(d) <- c("id", "value") # reassign the column names
# calculate the number of values for each id
res_N <- as.data.frame(tapply(d$value, d$id, length))
colnames(res_N) <- "N"
res_N$id <- row.names(res_N)
# calculate the number of values for each id
res_med <- as.data.frame(tapply(d$value, d$id, median))
colnames(res_med) <- "med"
res_med$id <- row.names(res_med)
# calculate the pvalues
res_pval <- as.data.frame(tapply(d$value, d$id, function(x)
{
if(length(x) < 3)
{ # t test requires at least 3 samples
NA
}
else
{
t.test(x, mu=med)$p.value #t.test (Pearson)d$value with other distribution? alternative=less or greater
} #d$value to compare with entire distribution
#mu=med for median of values for 1-sided test
}))
colnames(res_pval) <- "pval" # nominal p value
res_pval$id <- row.names(res_pval)
res_pval$adj.pval <- p.adjust(res_pval$pval, method = "BH") #multiple testing correction also "bonferroni"
res <- Reduce(function(x,y)
{
merge(x,y, by = "id", all = TRUE)
},
list(res_N, res_med, res_pval))
return (res)
}
data_result <- calc_id_ttest(d = data)
write.table(data_result, file="C:/R_Temp/t-test.txt", quote = FALSE, row.names = FALSE, col.names = TRUE, sep = "\t")