SCell: integrated analysis of single-cell RNA-seq data.
Bioinformatics 2016;
32:2219-20. [PMID:
27153637 DOI:
10.1093/bioinformatics/btw201]
[Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 04/09/2016] [Indexed: 11/12/2022] Open
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.
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