The other way around is definitely easier. RNA-seq analysis is a mostly solved problem with DESeq2 (or edgeR/limma). The tutorials are very detailed. The most difficult part is experimental design, which you probably know already.
Deep learning, on the other hand, is so fraught with pitfalls and traps. Even if you can code up a model successfully, it's very easy to trick yourself that you're doing very well (see a previous discussion at https://news.ycombinator.com/item?id=27376839). In my opinion, most of the work should be spent on making sure that you're not tricking yourself.
literally the first 5 years of training in any large scale data analysis should be "how not to trick yourself into thinking you found something significant that generalizes"
Deep learning, on the other hand, is so fraught with pitfalls and traps. Even if you can code up a model successfully, it's very easy to trick yourself that you're doing very well (see a previous discussion at https://news.ycombinator.com/item?id=27376839). In my opinion, most of the work should be spent on making sure that you're not tricking yourself.