cellCounts – an R function for quantifying 10x Chromium single-cell RNA sequencing data
The 10x Genomics Chromium single-cell RNA sequencing technology is a powerful gene expression profiling platform,…
Browsing: Expression and Quantification
Mandalorion – identifying and quantifying isoforms from accurate full-length transcriptome sequencing reads
Researchers from the University of California Santa Cruz have developed and benchmarked Mandalorion v4.1 for…
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…
RNAseqChef – a web-based integrative transcriptome analysis to automatically detect, integrate, and visualize differentially expressed genes and their biological functions
RNA sequencing (RNA-seq) is a powerful technique for understanding cellular state and dynamics. However, comprehensive…
scMEB – a fast and clustering-independent method for detecting differentially expressed genes in single-cell RNA-seq data
Cell clustering is a prerequisite for identifying differentially expressed genes (DEGs) in single-cell RNA sequencing…
tiny-count – a counting tool for hierarchical classification and quantification of small RNA-seq reads with single-nucleotide precision
Researchers at Colorado State University have developed tiny-count, a highly flexible counting tool that allows for…
lvm-DE – an empirical Bayes method for differential expression analysis of single cells with deep generative models
Detecting differentially expressed genes is important for characterizing subpopulations of cells. In scRNA-seq data, however,…
kimma – flexible linear mixed effects modeling with kinship covariance for RNA-seq data
The identification of differentially expressed genes (DEGs) from transcriptomic datasets is a major avenue of…
DCoNA – a tool for fast differential correlation network analysis
One of the standard methods of high-throughput RNA sequencing analysis is differential expression. However, it…
Gene expression model inference from snapshot RNA data using Bayesian non-parametrics
Gene expression models, which are key towards understanding cellular regulatory response, underlie observations of single-cell…