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110759.full

110759.full

Xun Zhu 1,2 , Thomas Wolfgruber 1,2 , Austin Tasato 3 , Lana X Garmire 1, 2* * Correspondence: LGarmire@cc.hawaii.edu 1 Program in Molecular Graduate and Bioengineering, Biology of Hawaii at Manoa, University HI 96816 Honolulu, 2 Program, University Epidemiology Hawaii Cancer Center, Honolulu, of 96813 HI 3 of Electrical Department University of Hawaii Engineering, Manoa, Honolulu, HI 96816 at Single-cell RNA sequencing (scRNA-seq) is an increasingly Background: platform to study heterogeneity at the single cell level. popular methods to process scRNA-seq have limited Computational to bench scientists, as they require significant amount of accessibility We have developed Granatum, a web browser based scRNAseq Results: analysis pipeline to make analysis more broadly accessible to Without a single line of programming code, a user can researchers. through the pipeline, setting parameters and visualizing results click the interactive graphical interface. The pipeline conveniently walks via users through various steps of scRNA-seq analysis. It has a the list of modules, including plate merging and batch comprehensive removal, outlier sample removal, gene filtering, gene effect normalization, cell clustering, differential gene expression expression pathway/ontology enrichment analysis, protein network analysis, Granatum enables much widely adoption of scRNA-seq Conclusions: by empowering the bench scientists with an easy to use technology interface for scRNA-seq data analysis. The code is freely graphical for research use at: http://garmiregroup.org/granatum/code available bioRxiv preprint first posted online Feb. 22, 2017; doi: http://dx.doi.org/10.1101/110759. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. Zhu et al. Page 1 of 22 SOFTWARE Granatum: a graphical single-cell RNA-seq analysis pipeline for genomics scientists Abstract bioinformatics skills. interaction visualization, and pseudo-time cell series construction. Keywords: single-cell; gene expression; graphical; normalization; clustering; differential expression; pathway; pseudo-time; software Background 1

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