Normalizing out the 1st and more components from the data. This is usefull if the data is seperated in its first component(s) by unwanted or biased variance. Such as sex or experiment location etc.

SeqPCA-Lite is a lightweight, Python-based pipeline designed for the rapid filtering and sequence-space exploration of enzyme candidates (e.g., esterases, α/β-hydrolases) mined from large databases ...

In the previous three articles, I explained the mechanism of PCA from scratch. Because you have the experience of manual calculations with NumPy, you understand what the library is doing behind the ...

Since PCA is a method that maximizes "variance," the results depend heavily on the scale (units) of each feature. For example, if variables in centimeters and meters are mixed, variables with larger ...