## Normalisation / Illumina

### Description

Normalize Illumina arrays to remove systematic bias.

### Parameters

- Within array normalization method (none, scale, quantile, vsn) [quantile]
- BeadStudio version (1,2,3) [1]
- Chip type (empty, ...) [empty]
- ID type (TargetID, ProbeID) [TargetID]

### Details

This tool normalizes Illumina files that have been imported using the Import tool. This tool splits BeadSummaryData into separate "chips", which allows the user to select which chips to normalize together, or to exclude from the analysis.

Within array normalization methods offer basic options, such as scaling the chips to the same median, quantile normalizing the chips to make the expression value follow the same distribution on all chips, and variance stabilizing normalization. Please note that during normalization the data is also log2-transformed

If the flags are produced, the Illumina detection values are automatically recoded into P/M/A flags.

Beadstudio versions differ in the data format they produce. Therefore you need to select the correct BeadStudio version in order to be able to get the right results.

It is obligatory to enter the chiptype. Otherwise annotation-based analyses do not work.

ID type specifies how the data was generated in BeadStdio. There are typically several probes per each gene. TargetIDs summarize all these probes as an expression estimate for one gene. ProbeID does not make this summarization, and several probes per gene remain in the dataset. TargetIDs are alphanumeric codes, such as 0610005A07RIK or GI_10047089-S, whereas ProbeIDs are numerical codes, such as 5570647.

### Output

A tab-delimited text file containing gene names, expression estimates and call values ("flags"). This file is suitable for all further analyses.

### References

This tool uses the Bioconductor package limma.

For normalization, please cite:

Smyth, G. K., and Speed, T. P. (2003). Normalization of cDNA microarray data. Methods 31, 265-273.

For background correction steps, please cite:

Ritchie, M. E., Silver, J., Oshlack, A., Silver, J., Holmes, M., Diyagama, D., Holloway, A., and Smyth, G. K. (2007). A comparison of background correction methods for two-colour microarrays. Bioinformatics.