Hi! It's not clear to my what you're doing. When you say:

"14 independent samples subjected <...> at different times" are you implying that:

1. The ATAC-Seq was generated at biological days 1 .. 2 .. 10 (for example) while RNA-seq was at biological days 1 ... 3 .. 5 ?

2. You sent off 14 ATAC-seq libraries for sequencing, each one independently to the sequencing centre? And the 14 RNA-seq libraries were also independently sent?

3. The ATAC-Seq and RNA-seq were generated from the same cells, at the same biological day (i.e. you split your cell population into two, and then made libraries from the two different protocols). Then you sent off say 5 and then 5 and then another 4 libraries to the same or different sequencing centres? 

In the case of (1) and (2) there really is no truly "valid" way of doing the comparison. In the case of (3), you can analyse the RNA-seq and ATAC-seq independently of each other (as appropriate for each of the techniques), making sure that you have a "batch" variable in your design formula which takes into account which samples were run on the same sequencing machine in the same run (this is actually what's important, not which sequencing centre your samples went to). I'm also assuming that you have at least three replicates per assay for each of the biological conditions you're investigating (or two, at the non-ideal-and-really-invalid bare minimum). 

Then you combine the output (say, differential gene expression with ATAC-seq differentially detected peaks at the same time points). 14 samples isn't really enough to try things like WGCNA and other more complex, fun, correlation tools.

Indeed, even just trying to fit the same GLM to counts from different data types is a bit of a stretch. For starters, they will have different modes of variability, so trying to estimate a single dispersion value for each "gene" will be inappropriate, let alone trying to fit a mean-dispersion trend. The biases will also be different so you'd have to normalise the counts from each data type separately, precluding direct comparisons between data types. The best you could do would be as Mike suggests, where you compare the log-fold changes upon treatment between data types. Even that's a bit sketchy, because differences are inevitable - for example, why would you ever expect a 2-fold increase in chromatin accessibility to result in a 2-fold increase in transcription? The entire process depends on so many other things, it's not likely to be a linear relationship in general.

Anyway, assuming your samples are separated into biological conditions, your best shot would be to look for condition-level correlations, i.e., changes in binding associated with changes in expression. This corresponds to scenario 3 in Darya's post. Here, the idea would be to analyse each data type separately to avoid the problems I've mentioned above.