CAP-RNAseq – an integrated pipeline for functional annotation and prioritization of co-expression clusters
Cluster analysis is one of the most widely used exploratory methods for visualization and grouping…
Browsing: Clustering
Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network
The advancement of single-cell sequencing technology has smoothed the ability to do biological studies at…
JOINTLY – interpretable joint clustering of single-cell transcriptomes
Single-cell and single-nucleus RNA-sequencing (sxRNA-seq) is increasingly being used to characterise the transcriptomic state of…
Quantum annealing-based clustering of single cell RNA-seq data
Cluster analysis is a crucial stage in the analysis and interpretation of single-cell gene expression…
GCFG – combining global-constrained concept factorization and a regularized gaussian graphical model for clustering single-cell RNA-Seq data
Single-cell RNA sequencing technology is one of the most cost-effective ways to uncover transcriptomic heterogeneity.…
DISCERN – deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection
Single-cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing…
CellBender – unsupervised removal of systematic background noise from droplet-based single-cell experiments
Droplet-based single-cell assays, including single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq) and cellular indexing…