I have a table like below (it is a small subset of my data. In this table, I measured one variable over 4 different time points (T1,..., T4), now I would like to check is there any significant difference between time points for each sample? Then based on that I will select those samples that have variability in different time points.

My assumption for the data is:

- non-normal distribution.
- unequal variance.
- the same sample size for each dependent group.

I have reviewed several methods (like, Repeated Measure ANOVA, GLM, GEE, linear mixed model, Kruskal Wallis test and GLMM), but I am confused about which one is more appropriate for my data?

```
sample T1 T2 T3 T4
1:824850-825300 0.00000000 0.0000000 0.0000000 0.0000000
1:894445-894831 5.39848590 3.9919398 5.8171244 3.4732853
1:902180-902369 5.30856403 4.7035677 1.6972109 4.0094193
1:911400-911969 3.93351892 8.6449756 3.9462391 5.9417675
1:912000-912125 3.08713416 3.7929570 0.5132366 2.7979578
1:919425-920025 4.37344006 6.4203699 3.5285015 3.4974473
1:934044-934294 9.87882930 11.3788710 7.4419304 6.0622420
1:948960-949100 1.65382187 11.0063484 5.4989633 12.4908832
```

Why not using any of the established statistical frameworks such as

`edgeR`

or`DESeq2`

and a LRT test? Is this count data, because this would be a requirement, raw counts to be precise. If not,`limma`

could be an option. Please add details what these data are.Thank for the reply! Yes, it is normalized data (based on TPM) from different histone marks and from different time points. But here I only showed one histone mark.

is there any way to do comparisons for each row over different time points?