Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists

Abstract

BackgroundSingle-cell RNA sequencing (scRNA-Seq) is an increasingly popular platform to study heterogeneity at the single-cell level. Computational methods to process scRNA-Seq data are not very accessible to bench scientists as they require a significant amount of bioinformatic skills.ResultsWe have developed Granatum, a web-based scRNA-Seq analysis pipeline to make analysis more broadly accessible to researchers. Without a single line of programming code, users can click through the pipeline, setting parameters and visualizing results via the interactive graphical interface. Granatum conveniently walks users through various steps of scRNA-Seq analysis. It has a comprehensive list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene-expression normalization, imputation, gene filtering, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series construction.ConclusionsGranatum enables broad adoption of scRNA-Seq technology by empowering bench scientists with an easy-to-use graphical interface for scRNA-Seq data analysis. The package is freely available for research use at http://garmiregroup.org/granatum/app Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-017-0492-3) contains supplementary material, which is available to authorized users.

Reproducibility of Methods to Detect Differentially Expressed Genes from Single-Cell RNA Sequencing

“…Yet, in practice, many studies use the bulk-cell −based DE methods for single-cell data, such as edgeR (Wang et al, 2016) or limma (Ziegenhain et al, 2017). Furthermore, various pipelines and workflows of RNA-seq analysis do not consider scRNA-seq data specifically (Lun et al, 2016;Chen et al, 2016;Law et al, 2016) and suggest users apply the bulkcell−based methods to scRNA-seq data (Zhu et al, 2017).…”

The ReproGenomics Viewer: a multi-omics and cross-species resource compatible with single-cell studies for the reproductive science community

“…More recently, the rapid emergence of novel technologies enabling the study of biological systems at a single-cell resolution (Linnarsson and Teichmann, 2016) has again presented serious concerns for the storage, mining, and visualization of such complex datasets (Raja et al, 2017). Several dedicated databases and webservers have been set up to host (Cao et al, 2017;Abugessaisa et al, 2018) and to explore (Lang et al, 2015;DeTomaso and Yosef, 2016;Zhu et al, 2017;Weinreb et al, 2018) single-cell RNA-sequencing (scRNA-seq) datasets.…”

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

“…Additionally, principal component (PC) scores are also used as the input of other non-linear dimensionality reduction [67][68][69][70][71][72][73] and clustering methods [74][75][76][77] in order to preserve the global structure, avoid the "curse of dimensionality" [78][79][80][81], and save memory space. A wide variety of scRNA-seq data analysis tools actually include PCA as an internal function or utilize PC scores as input for downstream analyses [22,[82][83][84][85][86][87][88][89].…”