The advent of single-cell sequencing is providing unprecedented opportunities to
disentangle tissue complexity and investigate cell identities and functions. However, the …

Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular
heterogeneity within complex tissues. However, various technical artifacts can be present in …

Background Systematic description of library quality and sequencing performance of single-
cell RNA sequencing (scRNA-seq) data is imperative for subsequent downstream modules …

Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular
transcriptomics heterogeneity. However, the data are noise-prone because gene expression …

Single-cell RNA-seq (scRNA-seq) is emerging as a promising technology for profiling cell-to-
cell variability in cell populations. However, the combination of technical noise and intrinsic …

Single-cell RNA-seq (scRNA-seq) is emerging as a promising technology to characterize
and dissect the cell-to-cell variability. However, the mixture of technical noise and intrinsic …

Systematic measurement biases make normalization an essential step in single-cell RNA
sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind …

Quality control (QC) is a critical component of single-cell RNA-seq (scRNA-seq) processing
pipelines. Current approaches to QC implicitly assume that datasets are comprised of one …

Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene
expression at the cellular level. However, due to the extremely low levels of transcripts in a …

The advent of single-cell omics technology has promoted our understanding of the genomic,
epigenomic, and transcriptomic heterogeneity in individual cells. Compared to traditional …