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.

Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets ...

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 ...

"The variables are too similar, and the regression analysis isn't working..." When doing data analysis, you may hit this wall. This is the problem known as multicollinearity. Actually, this ...