A visualization suite for major forms of bulk and single-cell RNAseq data in R. dittoSeq is
color blindness-friendly by default, robustly documented to power ease-of-use and allows …

In the last decade, single cell RNAseq (scRNAseq) datasets have grown in size from a
single cell to millions of cells. Due to its high dimensionality, it is not always feasible to …

Motivation Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene
expression at the level of individual cells. However, preparing raw sequence data for further …

Background RNA sequencing has become a ubiquitous technology used throughout life
sciences as an effective method of measuring RNA abundance quantitatively in tissues and …

Here we present ICARUS, a web server to enable users without experience in R to
undertake single cell RNA-seq analysis. The focal point of ICARUS is its intuitive tutorial …

Motivation As the generation of complex single-cell RNA sequencing datasets becomes
more commonplace it is the responsibility of researchers to provide access to these data in a …

Transcriptional profiling using RNA sequencing (RNAseq) has emerged as a powerful
methodology to quantify global gene expression patterns in various contexts from single …

Motivation Single-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of
thousands of individual cells, enabling the molecular exploration of tissues at the cellular …

Despite the growing availability of sophisticated bioinformatic methods for the analysis of
single-cell RNA-seq data, few tools exist that allow biologists without extensive bioinformatic …

Background As high-throughput sequencing applications continue to evolve, the rapid
growth in quantity and variety of sequence-based data calls for the development of new …