Uniform manifold approximation and projection is a nonlinear dimension reduction method
often used for visualizing data and as pre-processing for further machine-learning tasks …

Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …

Our aim here was not to argue which algorithm, t-SNE or UMAP, is more suitable for single-
cell studies. Once informative initialization is used, the two algorithms appear to preserve the …

Over 100 years of studies in Drosophila melanogaster and related species in the genus
Drosophila have facilitated key discoveries in genetics, genomics, and evolution. While high …

Dimension reduction (DR) algorithms project data from high dimensions to lower
dimensions to enable visualization of interesting high-dimensional structure. DR algorithms …

Background Standard preprocessing of single-cell RNA-seq UMI data includes
normalization by sequencing depth to remove this technical variability, and nonlinear …

Transcriptomic analysis plays a key role in biomedical research. Linear dimensionality
reduction methods, especially principal-component analysis (PCA), are widely used in …

We consider the vector embedding problem. We are given a finite set of items, with the goal
of assigning a representative vector to each one, possibly under some constraints (such as …

Dimensionality reduction, also known as manifold learning, is an area of machine learning
used for extracting informative features from data for better representation of data or …

The intrinsic functional organization of the brain changes into older adulthood. Age
differences are observed at multiple spatial scales, from global reductions in modularity and …