Introduction of Microarray Data Analysis
Large complexity of raw gene expression data generated by oligonucleotide chips,
spotted arrays, or whatever technology is used, create challenging data
analysis and data management problems. Such volumes of data are too large to analyze by simple sorting in
spreadsheets, or plotting as graphs. Each type of microarray has it own unique
analysis features. Here are general procedures to analysis microarray:
| Analysis Stage | Description | Examples of Methods |
| Normalization | Remove systematic variation. Equalized overall signal across array to be compared, ensures linearity of response across abundance classes. | Whole chip Per gene Quantile Lowess Dye swap |
| Comparative | Compares expression of a gene across two or more samples to determine significant changes in expression. | ANOVA Fold change Rank order (MAS 5 etc.) Permutation(SAM) |
| Clustering | Identifies significant correlation in expression data across experiments/conditions | Hierarchical clustering k-means clustering Self-organizing maps and many more |
| Biological overlay | Identify functions for give genes; functional clusters of genes; hypothesis generation | Multi-database access (Source) Functional grouping (Gene Ontology, KEGG, GenMAPP) PubMed Correlations (PubGene) |