Effective dimension reduction is an essential step in analysis of single cell RNA-seq
(scRNAseq) count data, which are high-dimensional, sparse, and noisy. Principal …

Effective dimension reduction is essential for single cell RNA-seq (scRNAseq) analysis.
Principal component analysis (PCA) is widely used, but requires continuous, normally …

Background Dimensionality reduction is an indispensable analytic component for many
areas of single-cell RNA sequencing (scRNA-seq) data analysis. Proper dimensionality …

Background Many computational methods have been developed recently to analyze single-
cell RNA-seq (scRNA-seq) data. Several benchmark studies have compared these methods …

A typical single-cell RNA sequencing (scRNA-seq) experiment will measure on the order of
20 000 transcripts and thousands, if not millions, of cells. The high dimensionality of such …

High-dimensional data are becoming increasingly common in nearly all areas of science.
Developing approaches to analyze these data and understand their meaning is a pressing …

Dimensionality reduction is a critical step in the analysis of single-cell RNA-seq (scRNA-seq)
data. The standard approach is to apply a transformation to the count matrix followed by …

Dimensionality reduction is a crucial step in essentially every single-cell RNA-sequencing
(scRNA-seq) analysis. In this chapter, we describe the typical dimensionality reduction …

High dimensionality and noise have limited the new biological insights that can be
discovered in scRNA-seq data. While dimensionality reduction tools have been developed …

Single-cell RNA sequencing (scRNA-seq) can determine gene expression in numerous
individual cells simultaneously, promoting progress in the biomedical sciences. However …