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    <p>The OP hasn't mentioned what form they want in their output, but I'm entirely updating this answer with a possible solution.</p> <p>First, some reproducible sample data to work with (that will actually work with <code>t.test</code>).</p> <pre><code>set.seed(1) mymat &lt;- matrix(sample(100, 40, replace = TRUE), ncol = 8, dimnames = list( paste("gene", 1:5, sep = ""), c("A", "A", "A", "B", "B", "B", "C", "C"))) mymat # A A A B B B C C # gene1 27 90 21 50 94 39 49 67 # gene2 38 95 18 72 22 2 60 80 # gene3 58 67 69 100 66 39 50 11 # gene4 91 63 39 39 13 87 19 73 # gene5 21 7 77 78 27 35 83 42 </code></pre> <p>I've left all the hard work to the <code>combn</code> function. Within the <code>combn</code> function, I've made use of the <code>FUN</code> argument to add a function that creates a vector of the <code>t.test</code> "statistic" by each row (I'm assuming one gene per row). I've also added an <code>attribute</code> to the resulting vector to remind us which columns were used in calculating the statistic.</p> <pre><code>temp &lt;- combn(unique(colnames(mymat)), 2, FUN = function(x) { out &lt;- vector(length = nrow(mymat)) for (i in sequence(nrow(mymat))) { out[i] &lt;- t.test(mymat[i, colnames(mymat) %in% x[1]], mymat[i, colnames(mymat) %in% x[2]])$statistic } attr(out, "NAME") &lt;- paste(x, collapse = "") out }, simplify = FALSE) </code></pre> <p>The output of the above is a <code>list</code> of <code>vectors</code>. It might be more convenient to convert this into a <code>matrix</code>. Since we know that each value in a vector represents one row, and each vector overall represents one column value combination (AB, AC, or BC), we can use that for the <code>dimnames</code> of the resulting <code>matrix</code>.</p> <pre><code>DimNames &lt;- list(rownames(mymat), sapply(temp, attr, "NAME")) final &lt;- do.call(cbind, temp) dimnames(final) &lt;- DimNames final # AB AC BC # gene1 -0.5407966 -0.5035088 0.157386919 # gene2 0.5900350 -0.7822292 -1.645448267 # gene3 -0.2040539 1.7263502 1.438525163 # gene4 0.6825062 0.5933218 0.009627409 # gene5 -0.4384258 -0.9283003 -0.611226402 </code></pre> <hr> <p>Some manual verification:</p> <pre><code>## Should be the same as final[1, "AC"] t.test(mymat[1, colnames(mymat) %in% "A"], mymat[1, colnames(mymat) %in% "C"])$statistic # t # -0.5035088 ## Should be the same as final[5, "BC"] t.test(mymat[5, colnames(mymat) %in% "B"], mymat[5, colnames(mymat) %in% "C"])$statistic # t # -0.6112264 ## Should be the same as final[3, "AB"] t.test(mymat[3, colnames(mymat) %in% "A"], mymat[3, colnames(mymat) %in% "B"])$statistic # t # -0.2040539 </code></pre> <hr> <h3>Update</h3> <p>Building on @EDi's answer, here's another approach. It makes use of <code>melt</code> from "reshape2" to convert the data into a "long" format. From there, as before, it's pretty straightforward subsetting work to get what you want. The output there is transposed in relation to the approach taken with the pure <code>combn</code> approach, but the values are the same.</p> <pre><code>library(reshape2) mymatL &lt;- melt(mymat) byGene &lt;- split(mymatL, mymatL$Var1) RowNames &lt;- combn(unique(as.character(mymatL$Var2)), 2, FUN = paste, collapse = "") out &lt;- sapply(byGene, function(combos) { combn(unique(as.character(mymatL$Var2)), 2, FUN = function(x) { t.test(value ~ Var2, combos[combos[, "Var2"] %in% x, ])$statistic }, simplify = TRUE) }) rownames(out) &lt;- RowNames out # gene1 gene2 gene3 gene4 gene5 # AB -0.5407966 0.5900350 -0.2040539 0.682506188 -0.4384258 # AC -0.5035088 -0.7822292 1.7263502 0.593321770 -0.9283003 # BC 0.1573869 -1.6454483 1.4385252 0.009627409 -0.6112264 </code></pre> <hr> <p>The first option is considerably faster, at least on this smaller dataset:</p> <pre><code>microbenchmark(fun1(), fun2()) # Unit: milliseconds # expr min lq median uq max neval # fun1() 8.812391 9.012188 9.116896 9.20795 17.55585 100 # fun2() 42.754296 43.388652 44.263760 45.47216 67.10531 100 </code></pre>
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    1. COyour code is not computing what exactly I want. for example, for the matix in my example, I want to calculate T-test for gene1 for diffrent groups, which means, calculating T-score for gene1 : T-score-gene1 : A<-c(1 6 11), B<-c(6 21), C<-c(26) is calculating pairwise T-score between A vs B A vs C and B vs C . I want to do this for all rows (all genes). so how can I modify your code to do this task ?
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    2. CO@user2806363, sorry, but until you actually post a well-formed question with reproducible sample data *and* a sample of your expected output (as part of the question, not as unreadable code in comments), you're probably not going to get any meaningful help.
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    3. CO@user2806363, I've decided to go ahead and take a stab at what I *think* you might be looking for. In the future, however, it is better to be clear up front so that you don't have to post repeated questions to try to get an acceptable answer. Also, it helps if you show what you have fiddled with in the meantime--there were some good ideas shared in the earlier answers which could have at least gotten you close to how you might (1) want to solve the problem or (2) how you should present the problem to others more clearly.
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