Error estimates for the analysis of differential expression from RNA-seq count data

October 24, 2014 Leave a comment 4,968 Views

A number of algorithms exist for analysing RNA-sequencing data to infer profiles of differential gene expression. Problems inherent in building algorithms around statistical models of over dispersed count data are formidable and frequently lead to non-uniform p-value distributions for null-hypothesis ...

RNA-Seq Blog Transcriptome Research & Industry News

Tag Archives: QuasiSeq

Error estimates for the analysis of differential expression from RNA-seq count data

Learning to predict RNA sequence expressions from whole slide images

CRIC-seq – global profiling of in-situ RNA–RNA contacts associated with a specific RNA-binding protein

Novel machine-learning algorithm creates atlas of cancer with potential as universal diagnostic platform

Long noncoding RNA profiling may be effective for stratification in pediatric AML

spSeudoMap – cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data

SoCube – an innovative end-to-end doublet detection algorithm for analyzing scRNA-seq data

Quantabio adds powerful bead booster chemistry to ultra-fast sparQ RNA-Seq HMR kit