Table 1. Summary of the processing methods recommended for different levels of data.

Data Type	Analysis Task	Recommended Approach
All levels	Quality assessment	Examine scanner metrics
Rawa	Local background adjustment	Median background subtraction
Raw	Transformation	log2
Bead-levelb	Spatial artefact detection & removal	BASH
Bead-level	Quality assessment	Examine image plots & boxplots
Bead-level	Summarization	Default Illumina method
Summary-levelc	Data export from BeadStudio/ GenomeStudio	Non background corrected, non normalized, Sample and Control “Probe Profile” tables
Summary-level	Quality assessment	Examine boxplots of regular & control probes, MDS plots
Summary-level	Background correction	Normal-exponential convolution using negative controls
Summary-level	Normalization	Quantile
Summary-level	Transformation	log2
Summary-level	Estimation of proportion of expressed probes in a sample	Mixture model that uses negative controls (propexpr [29])
Summary-level	Probe filtering	Based on annotation quality
Summary-level	Differential expression analysis	Linear modelling using weights

^aRaw data comprises one observation per pixel, per array.

^bBead-level data comprises one observation per bead, per array.

^cSummary-level data comprises one observation per probe type, per sample.