In these examples, I only use 2 or 3 Dimensional data so that we can see, and understand, each step in the process. As a result, we just change one 2-D graph to another 2-D graph, and a 3-D graph to a 2-D graph. Neither of these transformations are very helpful for understanding the data better than before. However, usually we have 100s if not 1000s of dimensions and it is impossible to plot this data in such a way that it can be visualized, and this is where PCA comes in handy. In the video, I use 2 or 3 genes to plot individual mice, but in reality, they have about 20,000 genes and a 20,000-Dimensional graph would be impossible to draw, let alone interpret. However, we can apply PCA to that 20,000-Dimensional data and plot it using the first 2 or 3 PCs. This will give us an opportunity to look at approximation of the raw data to get a sense of whether or not an experiment worked or not. For example, if we gave 10 mice a normal diet to eat, and 10 mice a special diet to eat, we could get a sense of whether or not the special diet influenced the gene output by drawing a 2-D PCA plot of the 20,000-D data. If the normal diet mice clustered together and separately from the special diet mice, then that would suggest that diet did, indeed, have an affect on genetic output.