The necessity of data-driven optimization of the number of principal components in scRNA-seq analysis - examples on real and simulated data
This study demonstrates that while fixed defaults for the number of principal components often work, data-driven optimization is essential for scRNA-seq analysis due to variable optimal ranges across diverse datasets, and it provides practical recommendations for selecting these methods based on a comprehensive evaluation of real and simulated data.