Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of
millions of cells across species and diseases. These data have spurred the development of …

Single-cell transcriptomics (SCT) is a tour de force in the era of big omics data that has led to
the accumulation of massive cellular transcription data at an astounding resolution of single …

Subpopulation identification, usually via some form of unsupervised clustering, is a
fundamental step in the analysis of many single-cell RNA-seq data sets. This has motivated …

Genetic screens in cancer cell lines inform gene function and drug discovery. More
comprehensive screen datasets with multi-omics data are needed to enhance opportunities …

Single-cell RNA sequencing (scRNA-seq) allows researchers to study cell heterogeneity at
the cellular level. A crucial step in analyzing scRNA-seq data is to cluster cells into …

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 …

Motivation Single-cell RNA-sequencing (scRNA-seq) has enabled the molecular profiling of
thousands to millions of cells simultaneously in biologically heterogenous samples …

Single-cell RNA sequencing (scRNA-seq) is a tool for studying gene expression at the
single-cell level that has been widely used due to its unprecedented high resolution. In the …

To process large-scale single-cell RNA-sequencing (scRNA-seq) data effectively without
excessive distortion during dimension reduction, we present SHARP, an ensemble random …

A primary challenge in the analysis of RNA-seq data is to identify differentially expressed
genes or transcripts while controlling for technical biases. Ideally, a statistical testing …