Expression and Quantification

clusterExperiment and RSEC – A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets

Clustering of genes and/or samples is a common task in gene expression analysis. The goals in cluste...
The impact of similarity metrics on single-cell RNA-seq data clustering

Advances in high-throughput sequencing on single-cell gene expressions [single-cell RNA sequencing (...
CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type

Single-cell RNA sequencing (scRNA-seq) is a powerful tool to study heterogeneity and dynamic changes...
Using supervised learning methods for gene selection in RNA-Seq case-control studies

Whole transcriptome studies typically yield large amounts of data, with expression values for all ge...
Interpretation of differential gene expression results of RNA-seq data

Differential gene expression (DGE) analysis is one of the most common applications of RNA-sequencing...

clusterExperiment and RSEC – A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets

Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of...

The impact of similarity metrics on single-cell RNA-seq data clustering

21 days ago Leave a comment 927 Views

Advances in high-throughput sequencing on single-cell gene expressions [single-cell RNA sequencing (scRNA-seq)] have enabled transcriptome profiling on individual cells from complex samples. A common goal in scRNA-seq data...

CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type

27 days ago Leave a comment 632 Views

Single-cell RNA sequencing (scRNA-seq) is a powerful tool to study heterogeneity and dynamic changes in cell populations. Clustering scRNA-seq is essential in identifying new cell types and studying their characteristics. University of Pennsylvania...

Using supervised learning methods for gene selection in RNA-Seq case-control studies

28 days ago Leave a comment 975 Views

Whole transcriptome studies typically yield large amounts of data, with expression values for all genes or transcripts of the genome. The search for genes...

Interpretation of differential gene expression results of RNA-seq data

August 15, 2018 Leave a comment 3,013 Views

Differential gene expression (DGE) analysis is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes across two or more conditions and is widely used in many...

ScISOr-Seq – single-cell isoform RNA sequencing

July 26, 2018 Leave a comment 1,161 Views

Full-length isoform sequencing has advanced our knowledge of isoform biology. However, apart from applying full-length isoform sequencing to very few single cells, isoform sequencing has been limited...

dropEst – pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments

July 16, 2018 Leave a comment 1,063 Views

Recent single-cell RNA-seq protocols based on droplet microfluidics use massively multiplexed barcoding to enable simultaneous measurements of transcriptomes for thousands of individual cells. The increasing complexity of such data creates challenges for subsequent computational processing...

Page 1 of 3512 3 4 5 » 10 20 30...Last »

RNA-Seq Blog Transcriptome Research & Industry News

Expression and Quantification

clusterExperiment and RSEC – A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets

The impact of similarity metrics on single-cell RNA-seq data clustering

CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type

Using supervised learning methods for gene selection in RNA-Seq case-control studies

Interpretation of differential gene expression results of RNA-seq data

clusterExperiment and RSEC – A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets

The impact of similarity metrics on single-cell RNA-seq data clustering

CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type

Using supervised learning methods for gene selection in RNA-Seq case-control studies

Interpretation of differential gene expression results of RNA-seq data

ScISOr-Seq – single-cell isoform RNA sequencing

dropEst – pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments

Potential pitfalls in analyzing and quantifying lowly-expressed genes and small RNAs with alignment-free pipelines

SAVER – gene expression recovery for single-cell RNA sequencing

Gene expression distribution deconvolution in single-cell RNA sequencing

GraphDDP – a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge

Single-cell RNA-Seq gives scientists unprecedented view of brain development

BGP – identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process

iSEE – an interactive Shiny-based graphical user interface for exploring data stored in SummarizedExperiment objects

Vex-seq – high-throughput identification of the impact of genetic variation on pre-mRNA splicing efficiency

Medicare covers Veracyte’s RNA-Seq based Envisia Test

Upcoming Webinar – ChIP-Seq / ATAC-Seq Analysis and Integration with Gene Expression Data

Ambiguous splice sites distinguish circRNA and linear splicing in the human genome