Hi everybody, 
I have the counts (obtained by HTSeq) for a lot of genes(~58,000) at different time points (replicates).

    gene                           t1_S1    t1_S2
    ENSG00000000003.14              0        0
    ENSG00000000005.5               0        0
    ENSG00000000419.12              1        3
     [...]

I woul like to calculate the correlation between the counts over each gene at the same timepoint to understand how reproducible the replication timing and progression is for each repeat. 
Any suggestions? 
Do you want to test the correlation between the different timepoints or between the different genes.

Let say you have 10 timepoints and 58000 genes

To test the different timepoints :

    cor(xx, method="pearson")
will give you a 10x10 matrix , so 100 correlations calculation (even though I guess the `cor` function is smart and should not compute twice the `cor` function between col A and col B ; and between col B and col A ; thus 45 correlations should be computed)

To test the different genes (in a pairwise manner) :

    cor(t(xx), method="pearson")

here a 58,000 x 58,000 matrix , = 3.364e+09 correlations (or 1,681,971,000 correlations if `cor` function is smart). That's why R crashes, it will take to long to compute so many correlations.

---

> Edit based on OP comments 

Use the coefficent of variation : https://en.wikipedia.org/wiki/Coefficient_of_variation : 

    dat.coeff.var <- apply(dat,1,function(x){sd(x)/mean(x)})

