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Genome Med. 2017 Dec 5;9(1):108. doi: 10.1186/s13073-017-0492-3.

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

Author information

1
Graduate Program in Molecular Biology and Bioengineering, University of Hawaii at Manoa, Honolulu, HI, 96816, USA.
2
Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA.
3
Department of Electrical Engineering, University of Hawaii at Manoa, Honolulu, HI, 96816, USA.
4
Graduate Program in Molecular Biology and Bioengineering, University of Hawaii at Manoa, Honolulu, HI, 96816, USA. LGarmire@cc.hawaii.edu.
5
Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA. LGarmire@cc.hawaii.edu.

Abstract

BACKGROUND:

Single-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.

RESULTS:

We 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.

CONCLUSIONS:

Granatum 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.

KEYWORDS:

Clustering; Differential expression; Gene expression; Graphical; Imputation; Normalization; Pathway; Pseudo-time; Single-cell; Software

PMID:
29202807
PMCID:
PMC5716224
DOI:
10.1186/s13073-017-0492-3
[Indexed for MEDLINE]
Free PMC Article

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