
scSemiAAE – a semi-supervised clustering model for single-cell RNA-seq data
Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA…
Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA…
Cell clustering is a prerequisite for identifying differentially expressed genes (DEGs) in single-cell RNA sequencing…
Novel research from the Department of Biomedical Informatics examines the relationship between complex traits and…
The increasing application of RNA sequencing to study non-model species demands easy-to-use and efficient bioinformatics…
Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement…
Recently, CITE-seq emerged as a multimodal single-cell technology capturing gene expression and surface protein information…
New transcriptome data sets clarifies the evolutionary relationships among orchids and reveals that the plant’s…
Researchers at Karolinska Institutet have developed a molecular method able to detect whether or not…
Glioblastomas (GBM) are aggressive brain tumors with extensive intratumoral heterogeneity that contributes to treatment resistance.…
Researchers at Colorado State University have developed tiny-count, a highly flexible counting tool that allows for…