INTRODUCTION

Normalisation is a general term for a collection of methods that are directed at resolving the systematic errors and bias introduced by the microarray experimental platform. Normalisation methods stand in contrast with the data analysis methods described in Chapters 7, 8 and 9 that are used to answer the scientific questions for which the microarray experiment has been performed. The aim of this chapter is to give an understanding of why we need to normalise microarray data, and the methods for normalisation that are most commonly used. The chapter is arranged into three further sections:

1. Section 5.2: Data Cleaning and Transformation, looks at the first steps in cleaning and transforming the data generated by the feature extraction software before any further analysis can take place.

2. Section 5.3: Within-Array Normalisation, describes methods that allow for the comparison of the Cy3 and Cy5 channels of a two-colour microarray. This section is only relevant for two-colour arrays.

3. Section 5.4: Between-Array Normalisation, describes methods that allow for the comparison of measurements on different arrays. This section is applicable both to two-colour and single channel arrays, including Affymetrix arrays.

SECTION 5.2 DATA CLEANING AND TRANSFORMATION

The microarray data generated by the feature extraction software is typically in the form of one or more text files (Table 4.2). Before you use the data to answer scientific questions, there are a number of steps that are commonly taken to ensure that the data is of high quality and suitable for analysis. This section describes three stages of data cleaning and transformation:

1. ▪ Removing flagged features

2. ▪ Background subtraction

3. ▪ Taking logarithms