beadarrayFilter: An R Package to Filter

Microarrays enable the expression levels of thousands of genes to be measured simultane- ously. However, only a small fraction of these genes are expected to be expressed under different experimental conditions. Nowadays, filtering has been introduced as a step in the microarray pre- processing pipeline. Gene filtering aims at reducing the dimensionality of data by filtering redundant features prior to the actual statistical analysis. Previous filtering methods focus on the Affymetrix platform and can not be easily ported to the Illumina platform. As such, we developed a filtering method for Illumina bead arrays. We developed an R package, beadarrayFilter, to implement the latter method. In this paper, the main functions in the package are highlighted and using many examples, we illustrate how beadarrayFilter can be used to filter bead arrays. Although different microarrays platforms share the same principle of hybridizing DNA to a complementary probe, they differ considerably by design. Unlike the Affymetrix microarrays which have sets of unique probes targeting a particular gene (resulting in a probe set for a targeted gene), Illumina microarrays have sets of identical probes. Thus, the existing filtering methods can not be readily ported to the Illumina platform. As a result, Forcheh et al. (2012) developed a filtering method for Illumina bead arrays. Forcheh et al. (2012) equally showed that filtering improves the analysis of differential expression. We provide the implementation of their method in the beadarrayFilter R software package. The beadarrayFilter package can take a normalized data frame or a normalized bead array ExpressionSetIllumina object (obtained using the summarize or readBeadSummaryData functions in the Bioconductor package beadarray by Dunning et al., 2007) or a normalized LumiBatch object as input and returns a list containing a filtered data frame or a filtered bead array ExpressionSetIllumina object or a filtered LumiBatch object, respectively. The package can also process summarized and normalized average intensities (eSet), their standard errors (seSet) and the number of beads used for summarization (nSet) as input and returns a list of components including the intra-cluster correlations (ICC), which can be used to assess different filtering strategies. The paper contains a brief background of the filtering methodology followed by the introduction of the beadarrayFilter package with illustrative examples.