inst/extdata/pca.md

In OceaneCsn/DIANE: DIANE

Principal Component Analysis

Performing PCA on normalized RNA-Seq counts can be really informative about the conditions that impact gene expression the most. During PCA, new variables are computed, as linear combinations of your initial variables (e.g. experimental conditions). Those new variables, also called principal components, are designed to carry the maximum of the data variability.

We can plot the correlations of the initial variables to the new variables, the principal components, to see which ones contribute the most to the overall expression changes.

Each principal component explains a certain amount of the total variability, and those relative percentages are shown in what is called the screeplot.

OceaneCsn/DIANE documentation built on Jan. 10, 2024, 6:43 p.m.