A nice background reading on microarray t-test is the
limma Bioconductor package. It's a very popular package for what you're doing, and I personally take it like a bible. Give it a read when you have an opportunity.
1. Paired vs independent
Let's take a look what a typical paired samples should be:
Paired samples occur when we compare two treatments and each sample given one treatment is
naturally paired with a particular sample given the other treatment. This is a special case of blocking
with blocks of size two. The classical test associated with this situation is the paired t-test. Suppose an experiment is conducted to compare a new treatment (T) with a control (C). Six dogs are used from three sib-ships. For each sib-pair, one dog is given the treatment while the other
dog is a control.
What it really mean is that, if you apply both control and treatment to the same biology source, it's a repeated measurement and therefore you should use the paired t-test.
There is no information on how you conducted your experiment in your question, so I assume your samples are independent and therefore you should use the independent t-test. But please double check.
2. Equal or unequal variance
This is normally not a problem in my works because I just run the
limma package and let it figure out the variance... (it uses a Bayesian denominator, but let's not get into that).
There's no right or wrong answer on how you assume your variance. From what I see your data, the variance looks quite close in your control samples and treatment samples. You might want make box-plots for your samples and decide if the equal variance assumption is reasonable. There're some formal ways to do that, but a simple box-plot visualization should be sufficient.