We had some protein microarrays done I believe using the hu-prot platform. I asked for non-normalised corrected data hoping to run in DeSeq2 (as I have used this for RNAeq data before).

The count data imported as "cts" looks something like this:

Patient ID's in columns and proteins in rows, obviously allot more of each. One note here I had to round values up so there where no decimal places for DeSeq

My metadata looks like this in "coldata": 

Again allot more patients.

Here is an example of case vs control analysis I did

dds_case_control <- DESeqDataSetFromMatrix(countData = cts,
                              colData = coldata,
                              design = ~ case_control)

dds_case_control$case_control <- relevel(dds_case_control$case_control, ref="control")

dds_case_control <- DESeq(dds_case_control)

res_case_control <- results(dds_case_control)

summary(res_case_control)

Which all seems to work fine, but I am not an expert at all with this and wondered if there is some ways within Deseq2 to quality control the results? Or if perhaps if I should use a different R package? I do like Deseq2 as I am now familiar with it, but ay advice welcome.

You are right to want to quality control.

Microarrays do not produce count data, certainly not negative binomial counts. So it is not meaningful to analyse protein microarrays using DESeq2, DESeq or edgeR.

I am not familiar with the hu-prot platform (by which I think you mean the HuProt platform from CDI), but the limma package has been suggested by others for this sort of data.

I went to the CDI website to find articles using HuProt, and the most recent article did their analysis using limma:

http://www.mcponline.org/content/early/2017/01/13/mcp.M116.063602.abstract