Single-cell multi-omics technologies and methods characterize cell states and activities by
simultaneously integrating various single-modality omics methods that profile the …

Single-cell omics technologies are currently solving biological and medical problems that
earlier have remained elusive, such as discovery of new cell types, cellular differentiation …

Background A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately
detect the number of cell types in the sample, which can be critical for downstream analyses …

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 …

Data analysis and interpretation remain a critical bottleneck in current multi-omics studies.
Here, we introduce OmicsAnalyst, a user-friendly, web-based platform that allows users to …

Genome-wide data is used to stratify patients into classes for precision medicine using
clustering algorithms. A common problem in this area is selection of the number of clusters …

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

The interpretation of pathway enrichment analysis results is frequently complicated by an
overwhelming and redundant list of significantly affected pathways. Here, we present an R …

Elucidation of cell subpopulations at high resolution is a key and challenging goal of single-
cell ribonucleic acid (RNA) sequencing (scRNA-seq) data analysis. Although unsupervised …

The increased availability of high-throughput technologies has enabled biomedical
researchers to learn about disease etiology across multiple omics layers, which shows …