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 have limited accessibility to bench scientists as they require significant amounts of bioinformatics 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 filtering, geneexpression normalization, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein-networ interaction visualization, and pseudo-time cell series construction.ConclusionsGranatum enables broad adoption of scRNA-Seq technology by empowering the 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

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 down-stream analyses [22,[82][83][84][85][86][87][88][89].…”

Current best practices in single‐cell RNA‐seq analysis: a tutorial

“…These platforms provide a basis for the construction of analysis pipelines. Currently available platforms exist on the command line in R (McCarthy et al , ; Butler et al , ) or Python (Wolf et al , ), and as local applications (Patel, ; preprint: Scholz et al , ) or Web servers (Gardeux et al , ; Zhu et al , ) with graphical user interfaces (GUIs). An overview of platforms is available in Zhu et al () and Zappia et al ().…”

“…These platforms are especially useful for exploratory analysis. Platforms such as Granatum (Zhu et al , ) and ASAP (Gardeux et al , ) differ in the tools they integrate, with Granatum including the larger variety of methods. As Web servers, these two platforms are readily available, yet computational infrastructure will limit their ability to scale to large datasets.…”

Using single‐cell multiple omics approaches to resolve tumor heterogeneity

“…As larger amounts of single-cell data become publicly available, there will be increased opportunities to identify subclonal-specific biomarkers at a personalized level. User-friendly data portals for single-cell analysis, such as Granatum, will become increasingly integral in the bench-to-bedside transition [61]. The comprehensive annotation and analysis of single-cell datasets will be the foundation of understanding how cell-to-cell variability in normal and cancer cells influence cellular identity and function in the human body.…”

“…Other tools such as scLVM [58], PseudoGP [59], and SPADE [60] have provided various solutions to analyze heterogeneity with scRNA-seq data computationally. With the scRNA-seq analysis toolbox expanding rapidly, graphical user interface (GUI) pipelines such as Granatum (http://garmiregroup.org/granatum/app) have recently been developed to ensure that accessing the latest development in computational methods is amenable for clinical and noninformatics researchers [61]. In addition, with datasets accumulating at an astonishing speed, there have been efforts like the RIKEN Single-Cell Project (http://singlecell.riken.jp/en/) to consolidate, index, and organize publically available datasets [62].…”