Normalisation / Illumina


Normalize Illumina arrays to remove systematic bias.



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.

Normalization methods include 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.

You can choose to include the original probe annotations to the results. Please keep in mind that these can be very outdated.

GenomeStudio/BeadStudio versions differ in the data format they produce. Therefore you need to select the correct software 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.

Identifier type specifies how the data was generated in GenomeStudio/BeadStudio. 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.


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


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.