Motivation The advancements of single-cell sequencing methods have paved the way for
the characterization of cellular states at unprecedented resolution, revolutionizing the …

Dendritic cells (DCs) play essential roles in innate and adaptive immunity and show high
heterogeneity and intricate ontogeny. Advances in high-throughput sequencing …

More and more researchers use single-cell RNA sequencing (scRNA-seq) technology to
characterize the transcriptional map at the single-cell level. They use it to study the …

Motivation Single-cell RNA sequencing (scRNA-seq) technologies have been testified
revolutionary for their promotion on the profiling of single-cell transcriptomes at single-cell …

Dimensionality reduction of high-dimensional data is crucial for single-cell RNA sequencing
(scRNA-seq) visualization and clustering. One prominent challenge in scRNA-seq studies …

Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to
probe gene expression profiles at single-cell resolution. Deep imputation methods have …

Accurate and consistent estimation of cell-to-cell similarity is crucial for clustering single-cell
RNA-seq (scRNA-seq) data. However, the high sparsity of scRNA-seq data often leads to …

Graphical model selection is a seemingly impossible task when many pairs of variables are
never jointly observed; this requires inference of conditional dependencies with no …

The tremendous progress of single-cell sequencing technology has given researchers the
opportunity to study cell development and differentiation processes at single-cell resolution …

Single-cell clustering is the most significant part of single-cell RNA sequencing (scRNA-seq)
data analysis. One main issue facing the scRNA-seq data is noise and sparsity, which poses …