Aaron Diaz1, Siyuan J Liu2, Carmen Sandoval2, Alex Pollen2, Tom J Nowakowski2, Daniel A Lim3, Arnold Kriegstein2. 1. Department of Neurological Surgery, UCSF Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research. 2. Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research. 3. Department of Neurological Surgery, UCSF Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research.
Abstract
UNLABELLED: 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. AVAILABILITY AND IMPLEMENTATION: 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. CONTACT: aaron.diaz@ucsf.edu.
UNLABELLED: 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. AVAILABILITY AND IMPLEMENTATION: 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. CONTACT: aaron.diaz@ucsf.edu.
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