Figure 1
From: Embracing noise to improve cross-batch prediction accuracy
PCA plots of data sets used. PCA plots are typically used to visualize batch effects. These data sets are chosen from the FDA-led Microarray Quality Control (MAQC) Consortium project. See [9] for details on data sets. Based on the PCA plots, data set A contains the most batch effects (points are separated by batches instead of class labels) while data set F contains the least. Note that data set I is a negative control where class labels are randomly assigned.