Methods for profiling genes at the single-cell level have revolutionized our ability to study
several biological processes and systems including development, differentiation, response …

Single-cell RNA sequencing (scRNA-seq) can map cell types, states and transitions during
dynamic biological processes such as tissue development and regeneration. Many …

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 …

The rapid progress of protocols for sequencing single-cell transcriptomes over the past
decade has been accompanied by equally impressive advances in the computational …

Motivation Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for
studying cellular decision-making processes. Robust inference of cell state transition paths …

Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at
the single-cell level. Currently, many analysis tools are available to better utilize these …

Single-cell RNASeq (scRNASeq) has emerged as a powerful method for quantifying the
transcriptome of individual cells. However, the data from scRNASeq experiments is often …

Cells are the basic building blocks of biological systems, with inherent unique molecular
features and development trajectories. The study of single cells facilitates in‐depth …

Single-cell RNA genomics technologies are revolutionizing biomedical science by profiling
single cells with unprecedented resolution, providing fundamental insights into the role of …

The abundance of new computational methods for processing and interpreting
transcriptomes at a single cell level raises the need for in silico platforms for evaluation and …