RNA-Seq Blog Transcriptome Research & Industry News
scRNA-seq profiles each represent a highly partial sample of mRNA molecules from a unique cell that can never be resampled, and robust analysis must...
Read More »MetaCell – analysis of single-cell RNA-seq data using K-nn graph partitions
GenePattern Notebook for analysis and exploration of single-cell transcriptomic data
Single-cell RNA sequencing (scRNA-seq) has emerged as a popular method to profile gene expression at the resolution of individual cells...
Read More »SCEED – design and analysis of single cell RNA-seq experiments for cell type identification
The advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent...
Read More »GSEPD – a Bioconductor package for RNA-seq gene set enrichment and projection display
RNA-seq, wherein RNA transcripts expressed in a sample are sequenced and quantified, has become a widely used technique to study disease and development. With RNA-seq, transcription abundance can be measured, differential expression genes...
Read More »Single cell RNA-seq data clustering using TF-IDF based methods
Single cell transcriptomics is critical for understanding cellular heterogeneity and identification of novel cell types. Leveraging the recent advances in single cell RNA sequencing (scRNA-Seq) technology requires novel unsupervised...
Read More »A systematic performance evaluation of clustering methods for single-cell RNA-seq data
Subpopulation identification, usually via some form of unsupervised clustering, is a fundamental step in the analysis of many single-cell RNA-seq data sets. This has motivated the development and application of a broad range of clustering methods, based on various..
Read More »Webinar Recording Available – Combining scRNA-Seq and Flow Cytometry
Learn how to use Dimensionality Reduction, Clustering and Hierarchial Gating, and Geneset Enrichment Analysis! Systems biology is poised to revolutionize our understanding of disease models and multi-omics studies as exemplified here are a key step along that exciting journey of ...
Read More »Assessment of data transformations for model-based clustering of RNA-Seq data
Quality control, global biases, normalization, and analysis methods for RNA-Seq data are quite different than those for microarray-based studies. The assumption of normality is reasonable for microarray based gene expression data; however, RNA-Seq data tend to follow an over-dispersed Poisson ...
Read More »Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference
Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecendented opportunity to investigate at the molecular level fundamental biological questions, such as stem cell differentiation or the discovery ...
Read More »ICAclust – Independent Component Analysis (ICA) based-clustering of temporal RNA-seq data
Gene expression time series (GETS) analysis aims to characterize sets of genes according to their longitudinal patterns of expression. Due to the large number of genes evaluated in GETS analysis, an useful strategy to summarize biological functional processes and regulatory ...
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