There are two quality control metrics that can be calculated fairly easily using APT. The first you already mentioned is using the detection above background algorithm (DABG). While it is typically not a good idea to apply this to gene level (i.e. core, extended, etc.) you can calculate it for every probeset using APT. Once these detection p-values are calculated you can compute the percentage of the chip detected by applying a p-value threshold. In my experience you want to have around a 30% detected rate or above. The quality of the chip will translate to gene level.
Positive vs negative area under the curve (AUC) is the second basic quality control metric. APT will also calculate this for each chip in this calculation APT is assessing the discrimination of probes which should be "on" (i.e. house keeping genes, etc) compared to probes which should have no hybridization (i.e. antigenomic sequences, inactive parts of the genome, etc).
Depending what you are planning to do with the microarray data you may or may not want to utilize the normalization routines built into APT. I suggest you look into RMA (APT), SCAN (Bioconductor), and COMBAT (Bioconductor), but again this is heavily dependent on what you plan to do with the data.
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