Principal component analysis (PCA) is often applied for analyzing data in the most diverse
areas. This work reports, in an accessible and integrated manner, several theoretical and …

INTRODUCTION The brain is responsible for cognition, behavior, and much of what makes
us uniquely human. The development of the brain is a highly complex process, and this …

Single-cell RNA-sequencing (scRNA-seq) is a powerful high-throughput technique that
enables researchers to measure genome-wide transcription levels at the resolution of single …

For a long time, gut bacteria have been recognized for their important roles in the
occurrence and progression of gastrointestinal diseases like colorectal cancer, and the ever …

Background The constant evolving and development of next-generation sequencing
techniques lead to high throughput data composed of datasets that include a large number …

Background Principal component analysis (PCA) is an essential method for analyzing single-
cell RNA-seq (scRNA-seq) datasets, but for large-scale scRNA-seq datasets, computation …

The recent advancements in single-cell technologies, including single-cell chromatin
accessibility sequencing (scCAS), have enabled profiling the epigenetic landscapes for …

Confounding factors exist widely in various biological data owing to technical variations,
population structures and experimental conditions. Such factors may mask the true signals …

In fields, such as ecology, microbiology and genomics, non-Euclidean distances are widely
applied to describe pairwise dissimilarity between samples. Given these pairwise distances …

Distinguishing biological from technical variation is crucial when integrating and comparing
single-cell genomics datasets across different experiments. Existing methods lack the …