
MeDuSA – mixed model-based deconvolution of cell-state abundances along a one-dimensional trajectory
Deconvoluting cell-state abundances from bulk RNA-sequencing data can add considerable value to existing data, but…
Deconvoluting cell-state abundances from bulk RNA-sequencing data can add considerable value to existing data, but…
RNA sequencing (RNA-seq) is a powerful technique for understanding cellular state and dynamics. However, comprehensive…
Cell clustering is a prerequisite for identifying differentially expressed genes (DEGs) in single-cell RNA sequencing…
Researchers at Colorado State University have developed tiny-count, a highly flexible counting tool that allows for…
Detecting differentially expressed genes is important for characterizing subpopulations of cells. In scRNA-seq data, however,…
The identification of differentially expressed genes (DEGs) from transcriptomic datasets is a major avenue of…
One of the standard methods of high-throughput RNA sequencing analysis is differential expression. However, it…
Gene expression models, which are key towards understanding cellular regulatory response, underlie observations of single-cell…
RNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely…
RNA sequencing has become an increasingly affordable way to profile gene expression analyses. Researchers from…