Batch Effect Visualisation

An excerpt from A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data

"Significant batch effects can be seen by the perfect separation of different batches on the PCA score plots for most data sets. Other visualization techniques can also be used to evaluate batch effects such as hierarchical clustering dendrogram, correlation heat-map and variance components pie chart from analysis of variance. The latter is a quantitative technique that gives the variances contributed by all factors when the class labels of all the samples are available. This allows the comparison of variances contributed by batch effects, biological effects and other effects. However, for cross-batch prediction in real applications, the class labels of the samples in the test set (future batch) are to be predicted and are unavailable, and thus analysis of variance cannot be applied for the endpoint factor. This approach is useful for evaluating the sources of variation and process control of sample handling and processing when all of these factors are recorded and reported."