2.3.2. Software and Websites Implementing Microarray Meta-Analysis and Cross-Platform Merging/Normalization
Software, including packages in R/Bioconductor and websites allowing users to implement microarray meta-analysis and cross-platform merging and normalization methods are listed in Table 1. Different experiments from multiple different arrays can be directly merged from the CEL files simultaneously using several packages implemented in R [69] including inSilico Merging [70], the CONOR [2], and virtualArray [71]. The inSilico Merging package implements XPN, DWD, and Combat, and the package CONOR additionally implements the GQ method. The virtualArray package allows cross-platform normalization using empirical Bayes methods (default) or the user may select one quantile discretization, normal discretization normalization, gene quantile normalization, median rank scores, quantile normalization, or mean centering [71]. This batch effect removal step can be supervised allowing the user to specify samples into groups based on platform as well as other attributes (e.g., cell type). Before the combined expression data undergoes cross-platform normalization, the data must be transformed to a common scale (e.g., log2) and resolution (e.g., 12, 14, 16, or 20 bit) [71]. As with meta-analysis, low expression and low variance genes are typically filtered out.
microarrays-04-00389-t001_Table 1 Table 1  List of software and websites for performing microarray meta-analysis.

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