High-throughput single-cell profiling provides an unprecedented ability to uncover the
molecular states of millions of cells. These technologies are, however, destructive to cells …

Dimensionality reduction is standard practice for filtering noise and identifying relevant
features in large-scale data analyses. In biology, single-cell genomics studies typically begin …

Arguably one of the most famous dimensionality reduction algorithms of today is t-distributed
stochastic neighbor embedding (t-SNE). Although being widely used for the visualization of …

Clustering is widely used for single-cell analysis, but current methods are limited in
accuracy, robustness, ease of use, and interpretability. To address these limitations, we …

Motivation The advent of highly multiplexed in situ imaging cytometry assays has
revolutionized the study of cellular systems, offering unparalleled detail in observing cellular …

Clustering analysis is widely used to group objects by similarity, but for complex datasets
such as those produced by single-cell analysis, the currently available clustering methods …

Single-cell omics enable the profiles of cells, which contain large numbers of biological
features, to be quantified. Cluster analysis, a dimensionality reduction process, is used to …

In single-cell genomics, we can simultaneously assay hundreds of thousands of cells, their
molecular contents, and how they respond to perturbation, from genetic knockouts to …