Tag Archives: scRNASeq

Single cell RNA sequencing (scRNAseq) technique is becoming increasingly popular for unbiased and high-resolutional transcriptome analysis of heterogeneous cell populations. Despite its many advantages, scRNAseq, like any other genomic sequencing technique, is susceptible to the influence of confounding effects. Controlling ...

Most existing dimensionality reduction and clustering packages for single cell RNA-Seq (scRNASeq) data deal with dropouts by heavy modelling and computational machinery. Here researchers from the Victor Chang Cardiac Research Institute introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ...

Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. Researchers at the Fred Hutchinson Cancer Research Center propose a two-part, generalized linear model for ...