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

Analysis of the composition of heterogeneous tissue has been greatly enabled by recent developments in single-cell transcriptomics. Researchers from UCSF have developed 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.

747 cells sequenced from human fetal neocortex

rna-seq

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

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/SCell.

Contact: aaron.diaz@ucsf.edu

Diaz A, Liu SJ, Sandoval C, Pollen A, Nowakowski TJ, Lim DA, Kriegstein A. (2016) SCell: integrated analysis of single-cell RNA-seq data. Bioinformatics [Epub ahead of print]. [abstract]

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