Single-cell transcriptomics can profile thousands of cells in a single experiment and identify
novel cell types, states and dynamics in a wide variety of tissues and organisms. Standard …

The advent of single-cell sequencing started a new era of transcriptomic and genomic
research, advancing our knowledge of the cellular heterogeneity and dynamics. Cell type …

Single-cell RNA sequencing (scRNA-seq) has become an empowering technology to profile
the transcriptomes of individual cells on a large scale. Early analyses of differential …

CIBERSORTx is a suite of machine learning tools for the assessment of cellular abundance
and cell type-specific gene expression patterns from bulk tissue transcriptome profiles. With …

Background Single-cell transcriptomics is rapidly advancing our understanding of the
cellular composition of complex tissues and organisms. A major limitation in most analysis …

Background Microglia are the endogenous immune cells of the brain and act as sensors of
pathology to maintain brain homeostasis and eliminate potential threats. In Alzheimer's …

Deconvolution of cell mixtures in “bulk” transcriptomic samples from homogenate human
tissue is important for understanding disease pathologies. However, several experimental …

Automated cell type identification is a key computational challenge in single‐cell RNA‐
sequencing (sc RNA‐seq) data. To capitalise on the large collection of well‐annotated sc …

The cell type identification is among the most important tasks in single-cell RNA-sequencing
(scRNA-seq) analysis. Many in silico methods have been developed and can be roughly …

Low blood flow through the fetal left heart is often conjectured as an etiology for hypoplastic
left heart syndrome (HLHS). To investigate if a decrease in left heart flow results in growth …