Analysis techniques for microarray expression profiling
| Method | Type of analysis |
|---|---|
| Hierarchical agglomerative | Forms clusters starting at lowest level12 |
| Hierarchical divisive | Forms clusters starting at the highest level14,15 |
| Self organising maps | Partitions genes/experiments into prespecific number of groups by mapping onto nodes16,17 |
| K-means | Partitions genes/experiments into prespecific number of groups by finding centroids7 |
| Quality cluster algorithm | Forms groups based on diameter quality measure and jackknife correlations18 |
| Super vector machines | Machine learning process that incorporates non-array information19 |
| Singular value decomposition | Groups genes/experiments by reducing data matrix to eigen vectors (similar to principal component analysis)20,21 |
| Gene shaving | Seeks groups of genes to maximise variation among experiments22 |
| Class prediction | Identifies genes whose behaviour predicts defined classes23 |