Efficient method(s) for comparison of data sets that are generated from different microarray experiments (meta-analysis) have been asked [previously][1]. Based on the discussion, I found [RankProd][2] R package. However, after using the method, I wanted to compare the results with an another method since RankProd was developed years back. I wold like to know any alternative method available for integrating multiple data set(RNASeq not included).

Out of 10 data sets, 8 are from Affymetrix  and 2 are Agilent. Expression profiles are based on different developmental stages of an organ in Arabidopsis.





  [1]: https://www.biostars.org/p/4418/
  [2]: https://bioconductor.org/packages/release/bioc/html/RankProd.html
**Updated February 29, 2020**

**Updated October 9, 2018**

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#If the array versions are the same
If the Affymetrix arrays are all of the same version/type, then just process them all together at the same time. Same for the Agilent arrays. Afterwards, use one as the training dataset and the other as the validation dataset, i.e., obtain results from one, and then corroborate these [results] in the other.

You could attempt to perform statistical analyses on them combined, too (i.e. not as training and validation), but, in this case, include `ArrayVersion` (i.e. batch) as a covariate in downstream analyses - there will likely be some batch effect.

#If all data-set arrays are different

 - Process each *independently*
 - Summarise expression values across whole genes *independently*
 - Create a list of common features/genes across all arrays
 - Convert the data to Z-scores *independently*
 - Merge (bind) the data together
 - Build regression models predicting for your outcome and include
   '`ArrayVersion`' as a covariate in the model

My advice is to avoid the use of programs that aim to directly correct for batch. It is preferable to include batch as a covarate in your statistical models.

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**NB - a word of caution about these approaches:** these are not 'fool proof'. If you are trying to analyse, e.g., brain tissue and skin tissue together, then the expression profiles will be so different such that you could not analyse them together in any way. Proceed with caution with these approaches that I mention, and take time to analyse the output via histograms and box-and-whisker plots.