I am clustering data using Satija's Seurat package. Link: https://satijalab.org/seurat/pbmc3k_tutorial.html Now I'm doing a presentation about principal component analysis (PCA). As I've understood it, the dimensions that contribute with the highest variance to the dataset is used for the first principal component (PC). The second principal component is perpendicular to the first. Like explained in this video:
In Seurat they present a heat map of the genes (dimensions) presented in the first component, a heat map the genes perpendicular to those and so on. The heat map shows the expression of each gene included in the PC (yellow= high, black = normal and purple = low).
What I'm wondering is: if the first principal component is based on the dimensions with most "extreme" values, contributing the most to the shape of the dataset, then how come the gene expression in the heatmap of PC1 is less "extreme" than the genes in the heatmap of PC2? If there is a bigger difference between gene expressions in PC2, then it should per definition be PC1.
The link shows the heatmaps of PC 1-6 from Satija Seurat, PBMC example (sorry, I was not able to embed the picture this time)