The innovation of biotechnologies has allowed the accumulation of omics data at an
alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge …

Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to
quantify the gene expression profile of thousands of single cells simultaneously. Analysis of …

Dimensionality reduction is standard practice for filtering noise and identifying relevant
features in large-scale data analyses. In biology, single-cell genomics studies typically begin …

Recent advances in single-cell technologies enable joint profiling of multiple omics. These
profiles can reveal the complex interplay of different regulatory layers in single cells; still …

Integration of single-cell transcriptome datasets from multiple sources plays an important
role in investigating complex biological systems. The key to integration of transcriptome …

Biological tree analysis serves as a pivotal tool in uncovering the evolutionary and
differentiation relationships among organisms, genes, and cells. Its applications span …

In the realm of single-cell analysis, computational approaches have brought an increasing
number of fantastic prospects for innovation and invention. Meanwhile, it also presents …

Abstract Deep Generative Models (DGMs) are becoming instrumental for inferring
probability distributions inherent to complex processes, such as most questions in …

Single-cell transcriptomics experiments provide gene expression snapshots of
heterogeneous cell populations across cell states. These snapshots have been used to infer …

Trajectory inference algorithms aim to reconstruct the developmental trajectory of single
cells from high-dimensional gene expression data. To solve this problem, standard …