SCell: integrated analysis of single-cell RNA-seq data
Author(s) -
Aaron A. Diaz,
Siyuan J. Liu,
Carmen Sandoval,
Alex A. Pollen,
Tom J. Nowakowski,
Daniel A. Lim,
Arnold R. Kriegstein
Publication year - 2016
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btw201
Subject(s) - executable , computer science , source code , scripting language , software , normalization (sociology) , rna seq , data mining , skyline , graphical user interface , dimensionality reduction , workflow , transcriptome , database , artificial intelligence , programming language , biology , gene , gene expression , biochemistry , sociology , anthropology
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.
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