RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR<ns3:p>The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation studies. In this workflow article, we analyse RNA-sequencing data from the mouse mammary gland, demonstrating use of the popular <ns3:bold>edgeR</ns3:bold> package to import, organise, filter and normalise the data, followed by the <ns3:bold>limma</ns3:bold> package with its <ns3:italic>voom</ns3:italic> method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing. This pipeline is further enhanced by the <ns3:bold>Glimma</ns3:bold> package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. The complete analysis offered by these three packages highlights the ease with which researchers can turn the raw counts from an RNA-sequencing experiment into biological insights using Bioconductor.</ns3:p>
The expanding vistas of spatial transcriptomicsBenchmarking single cell RNA-sequencing analysis pipelines using mixture control experimentsComprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencingA modified Chromium 10x droplet-based protocol that subsamples cells for both short-read and long-read (nanopore) sequencing together with a new computational pipeline (FLAMES) is developed to enable isoform discovery, splicing analysis, and mutation detection in single cells. We identify thousands of unannotated isoforms and find conserved functional modules that are enriched for alternative transcript usage in different cell types and species, including ribosome biogenesis and mRNA splicing. Analysis at the transcript level allows data integration with scATAC-seq on individual promoters, improved correlation with protein expression data, and linked mutations known to confer drug resistance to transcriptome heterogeneity.
Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data<ns4:p> <ns4:bold>Background:</ns4:bold> The commercially available 10x Genomics protocol to generate droplet-based single-cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as three silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also robustness of a dozen methods. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> We found that some methods, including Seurat and Cell Ranger, outperform other methods, although performance seems to be dependent on the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> In light of this, we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis. </ns4:p>