Normalisation of microarray gene expression data
This page contains a brief summary of methods and experiences with normalisation of microarray gene expression data, the R scripts I have written for the purpose, and some suggestions for useful quality controls.
My work has primarily been on Agilent one-colour oligonucleotide arrays for gene expression. For other platforms or uses, the methods and results I present here may not be fully valid or applicable.
- Here I give a quick introduction to microarrays, with focus on Agilent gene expression arrays, and explain the main sources of variability and ideas underlying my choice of methods.
- The normalisation method is explained: the different steps, their purpose, and some motivation of these choices.
- QC (quality control)
- A brief overview of quality control checks I have been using.
- Suggestions and advice related to analysis of microarray gene expression data.
- R scripts
- I have made some R scripts for performing microarray normalisation in a streamlined fashion. These are basically wrappers for methods from the R packages
pcaMethods, a simple data structure for organising the data of the different steps of the normalisation, and supporting methods for reading and writing the gene expression data as a matrix table.
- File formats and identifiers
- An brief summary of the file format, naming conventions, and relevant identifiers.
- Download page
- From here, you can download the scripts and get directions to annotation files and sample data to test them on.