Expression and Quantification

RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on...

In an effort to address a major challenge when analyzing large single-cell RNA-sequencing datasets, researchers from The University of Texas MD Anderson Cancer Center have developed a new tool...

RNA sequencing is an indispensable research tool used in a broad range of transcriptome analysis studies. The most common application of RNA...

The variation in gene expression profiles of cells captured in different phases of the cell cycle can interfere with cell type identification and functional analysis of single cell RNA-Seq (scRNA-Seq) data. Researchers from the University of Connecticut Health Center have ...

Single-cell RNA sequencing (scRNA-seq) technologies have precipitated the development of bioinformatic tools to reconstruct cell lineage specification and differentiation processes with single-cell precision. However, current start-up costs and...

In situ sequencing enables gene activity inside body tissues to be depicted in microscope images. To facilitate interpretation of the vast quantities of information generated, Uppsala University researchers have now developed an entirely new method of...

Single-cell RNA-sequencing (scRNA-seq) technology is a powerful tool to study organism from a single cell perspective and explore the heterogeneity between cells. Clustering is a fundamental step in scRNA-seq data analysis and it is the key to understand...

Spatially resolved transcriptomics gets increasing attention during the recent years. New protocols based on in situ sequencing or multiplexed single-molecule fluorescent in situ hybridization (sm-FISH) aim at capturing physical positions of single molecules. It enables investigation of expression...

As the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal...

Unsupervised clustering is a common and exceptionally useful tool for large biological datasets. However, clustering requires upfront algorithm and hyperparameter selection, which can introduce bias into the final clustering labels. It is therefore...