Microarray data Analysis.pptx
- 2. • Microarrays can be used in many types of experiments including genotyping, epigenetics, translation profiling and gene expression profiling. • Gene expression profiling is by far the most common use of microarray technology. Both one and two colour microarrays can be used for this type of experiment. The process of analysing gene expression data is similar for both types of microarrays and involves
- 3. • feature extraction; • quality control; • normalisation; • differential expression analysis; • biological interpretation of the results; • submission of data to a public database.
- 5. Feature extraction Feature extraction is the process of converting the scanned image of the microarray into quantifiable (computable) values and annotating it with the gene IDs, sample names and other useful information Feature extraction involves the conversion of the scanned microarray image to quantifiable values that are saved in binary (e.g. CEL) or text format.
- 6. Feature extraction • This process is often performed using the software provided by the microarray manufacturer. The output of this process is raw (i.e. unprocessed) data files that can be in binary or text format • After the feature extraction process, the data can be analysed. Array manufacturers often provide software to open and analyse their raw data files. These programs may not always be available, may become obsolete after a few years, or may not be flexible enough for your needs. There are several free software tools that are suitable for the downstream processing of microarray files. Examples are the Galaxy platform, GenePattern, GeneSpring (licence required) and the statistics software R.
- 7. Quality control • Quality control of microarray data begins with the visual inspection of the scanned microarray images to make sure that there are no obvious splotches, scratches or blank areas • After feature extraction, the data analysis software packages can be used to make diagnostic plots (for example of background signal, average intensity values and percentage of genes above background) to help identify problematic arrays, reporters or samples
- 8. Examples of quality control plots made when analysing differential expression data in Expression Atlas. Left to right: Array intensity distributions, PCA plot, density estimates.
- 9. Normalisation • Normalisation of microarray data is used to control for technical variation between assays, while preserving the biological variation. There are many ways to normalise the data, and the methods used depend on: • the type of array; • the design of the experiment; • assumptions made about the data (e.g. 'the majority of genes represented on the microarray are not expected to be differentially expressed in the test group relative to controls'); • and the package being used to analyse the data.
- 10. Differential expression analysis • The goal of differential expression analysis is to identify genes whose expression differs under different conditions. An important consideration for differential expression analysis is correction for multiple testing. This is a statistical phenomenon that occurs when thousands of comparisons (e.g. the comparison of expression of multiple genes in multiple conditions) are performed for a small number of samples (most microarray experiments have less than five biological replicates per condition). This leads to an increased chance of false positive results
- 11. Biological interpretation of gene expression data • Many of the methods for visualising and interpreting microarray data can also be used for RNA-seq experiments.
- 12. Submission of data to a public repository • Once you have finished generating microarray data (e.g. raw data files and processed/normalised data files are ready), it is important that you submit the data files together with metadata to a public database such as ArrayExpress. This helps to ensure the reproducibility of your experiment and is now a requirement of many journals and funding bodies.