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. 2016 Jul 15;32(14):2219-20.
doi: 10.1093/bioinformatics/btw201. Epub 2016 Apr 19.

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

Aaron Diaz  1 , Siyuan J Liu  2 , Carmen Sandoval  2 , Alex Pollen  2 , Tom J Nowakowski  2 , Daniel A Lim  3 , Arnold Kriegstein  2
Affiliations

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

Aaron Diaz et al. Bioinformatics. .

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.

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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Figures

Fig. 1.
Fig. 1.
747 cells sequenced from human fetal neocortex. Lineage reconstruction, via a Gabriel-graph shortest distance path and LOWESS regression, models the kinetics of gene expression during commitment to the excitatory-neuronal lineage (Color version of this figure is available at Bioinformatics online.)
See this image and copyright information in PMC

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