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 »
RNA-sequencing (RNA-seq) has emerged as one of the most sensitive tool for gene expression analysis. Among the library preparation methods available, the standard poly(A) + enrichment provides a comprehensive, detailed, and accurate view of polyadenylated RNAs. However, on samples of ...Read More »
RNA-sequencing has become the gold standard for whole-transcriptome gene expression quantification. Multiple algorithms have been developed to derive gene counts from sequencing reads. While a number of benchmarking studies have been conducted, the question remains how individual methods perform at ...Read More »
RNA-seq mixology – designing realistic control experiments to compare protocols and analysis methods
Carefully designed control experiments provide a gold standard for benchmarking new platforms, protocols and pipelines in genomics research. RNA profiling control studies frequently use the mixture design, which takes two distinct samples and combines them in known proportions to induce ...Read More »
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?
RNA-seq is now the technology of choice for genome-wide differential gene expression experiments, but it is not clear how many biological replicates are needed to ensure valid biological interpretation of the results or which statistical tools are best for analyzing ...Read More »