NB - Major update to answer: December 16, 2019
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It would help to state the specific arrays that you have.
There are different ideas on how best to do this - a search of the World Wide Web reveals this. I would not look for a package but instead begin to think critically about how it could work and what needs to be done.
1, 'Z-score' merge
Firstly, if you are interested in processing each dataset independently, then take my approach here: A: How to integrate multiple data sets from microarray platform prior meta-analysis
In this way, each dataset is processed and normalised independently. Then, they are respectively filtered so that genes are matched across all datasets, followed by a transformation to Z-scores (independently for each dataset). Once they are on the 'standard score distribution' (Z-scale), you can conduct statistical analyses on this merged dataset, and it may be recommended to include ArrayVersion as a covariate in your models.
This is best for downstream analyses based on correlation analyses, like network analysis.
2, 'direct' merge
Attempt a 'direct' merge, as per the approach here: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE6nnn/GSE6344/suppl/GSE6344_MethodsDetails.PDF
Here, datasets are again normalised independently and then genes are filtered so that they match. Then, a scaling factor is applied to one array so that the arrays can easily be merged together. There is further info here: Regarding Microarray Platforms
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Further posts on this topic that I have made:
This question has been already discussed. Did you check ?
I saw some questions on the same topic, many are from 3-4 years ago, as someone suggested I checked insilicoDB and virtualarray packages and I saw that they are discontinued from the latest bioconductor version (which I am using).Someone suggested sva (I read the vignette and it is something that is not the best for my work) so I asked if asked again to know what are the best method and packages available now.
This paper is quite new, maybe you can read it which includes review of cross-platform normalization. It could be useful.
thank you, I read this article and it is quite interesting, some of the packages used are not anymore available.
So you want to compare condition 1 on platform A with condition 2 on platform B? I'd say you can't.
Yes, the exact arrangement of the samples/conditions, and the array types is important