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SCell: integrated analysis of single-cell RNA-seq data.


ABSTRACT: Analysis of the composition of heterogeneous tissue has been greatly enabled by recent developments in single-cell transcriptomics. We present SCell, an integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface. Scripts and protocols for the high-throughput pre-processing of large ensembles of single-cell, RNA-seq datasets are provided as an additional resource.Binary executables for Windows, MacOS and Linux are available at http://sourceforge.net/projects/scell, source code and pre-processing scripts are available from https://github.com/diazlab/SCellSupplementary information: Supplementary data are available at Bioinformatics online.aaron.diaz@ucsf.edu.

SUBMITTER: Diaz A 

PROVIDER: S-EPMC4937196 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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SCell: integrated analysis of single-cell RNA-seq data.

Diaz Aaron A   Liu Siyuan J SJ   Sandoval Carmen C   Pollen Alex A   Nowakowski Tom J TJ   Lim Daniel A DA   Kriegstein Arnold A  

Bioinformatics (Oxford, England) 20160419 14


<h4>Unlabelled</h4>Analysis of the composition of heterogeneous tissue has been greatly enabled by recent developments in single-cell transcriptomics. We present SCell, an integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface. Scripts and protocol  ...[more]

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