Checking significance in trend of time-series analysis for count data

Question

[Assa Yeroslaviz]

I have a small matrix of count data from different time-points, which looks like that:

essentials.mean
            t1    t2     t3     t4     t5     t6     t7
Gene-1 3381.0 2132.5 1743.0 1774.5  724.0  125.5   56.0
Gene-2 4191.5 2894.0 2323.0 1323.0 2481.5  194.5   94.5
Gene-3 4522.0 2516.0 1751.0  803.0  443.5  163.5   54.0
Gene-4 5139.5 3412.0 3153.5 2669.5 1855.5  130.0   85.0
Gene-5 4447.0 1870.5 1425.5  907.5  324.5   34.0   55.0
...
`

When plotting the genes I can clearly see a decrease in expression intensities over time, for almost all of them.

I would like to know, if there is away to test for significance for this trend. How can I create a model to show, that this decrease is statistically significant?

Thanks
Assa

[ATpoint]

https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/designmatrices.html#linear-time-series

You can test for time series in limma. This guide may help deciding which sorts of time series you want to model.

[Assa Yeroslaviz]

I'm not sure, this would be the right approach, as I'm only interested in looking at 20 genes from the complete data set. Would this works with limma, If i subset my counts matrix to leave only 20 genes?

But thanks, I'll have a look at limma.