One of the standard methods of high-throughput RNA sequencing analysis is differential expression. However, it does not detect changes in molecular regulation. In contrast to the standard differential...
Read More »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 transcriptional dynamics. Although RNA expression data encode information on gene expression models, existing computational...
Read More »OUTRIDER – a statistical method for detecting aberrantly expressed genes in RNA sequencing data
RNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare...
Read More »ParslRNA-Seq – an efficient and scalable RNAseq analysis workflow for studies of differentiated gene expression
RNA sequencing has become an increasingly affordable way to profile gene expression analyses. Researchers from the LNCC, Brazil have developed a scientific workflow implementing several open...
Read More »Single-molecule counting method for digital quantification of SARS-CoV-2 RNA
Digital counting individual nucleic acid molecule is of great significance for fundamental biological research and accurate diagnosis of genetic diseases, which is hard to achieve with existing single-molecule detection technologies. Researchers at the...
Read More »SPECK – an unsupervised learning approach for cell surface receptor abundance estimation for single cell RNA-sequencing data
The rapid development of single cell transcriptomics has revolutionized the study of complex tissues. Single cell RNA-sequencing (scRNA-seq) can profile tens-of-thousands of dissociated cells from a single...
Read More »Neighboring cell types influence single-cell gene expression variability
Researchers from the University of Tsukuba have designed a statistical framework that identifies regulation of gene expression by neighboring cell...
Read More »Kyoto scientists use mathematics to extract clear signals from single-cell RNA sequencing data
Since scientists first mapped the complete human genome, attention has now turned to the question of how cells use this master copy of genetic...
Read More »NBBt-test – a versatile method for differential analysis of multiple types of RNA-seq data
Rapid development of transcriptome sequencing technologies has resulted in a data revolution and emergence of new approaches to study transcriptomic regulation such as alternative splicing, alternative polyadenylation, CRISPR knockout...
Read More »scCAN – single-cell clustering using autoencoder and network fusion
Unsupervised clustering of single-cell RNA sequencing data (scRNA-seq) is important because it allows us to identify putative cell types. However, the large number of cells (up to millions), the high...
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