SCell statistics

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Citations per year

Number of citations per year for the bioinformatics software tool SCell
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Tool usage distribution map

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Associated diseases

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Popular tool citations

chevron_left Normalization Cell lineage and pseudotime inference scRNA-seq data visualization Clustering Dimensionality reduction Quality control Differential expression detection Gene filtering scRNA-seq data integration chevron_right
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SCell specifications

Information


Unique identifier OMICS_11545
Name SCell
Software type Package/Module
Interface Command line interface, Graphical user interface
Restrictions to use None
Input data SCell accepts as input a matrix of raw gene counts with genes as rows and cells as columns.
Operating system Unix/Linux, Mac OS, Windows
Computer skills Advanced
Stability Stable
Maintained Yes

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Versioning


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Maintainer


  • person_outline Aaron Diaz

Publication for SCell

SCell citation

library_books

Single Cell Transcriptomics Bioinformatics and Computational Challenges

2016
Front Genet
PMCID: 5030210
PMID: 27708664
DOI: 10.3389/fgene.2016.00163

[…] l., ). However, one issue of these approaches is that the thresholds chosen for filtering are arbitrary and should differ according to the dataset (Jiang, P. et al., ). SinQC (Jiang, P. et al., ) and SCell (Diaz et al., ) are two QC tools specifically designed for scRNA-seq data. SinQC uses sequencing library quality to confirm gene expression outliers. It computes different quality metrics (e.g., […]


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SCell institution(s)
Department of Neurological Surgery, UCSF, San Francisco, CA, USA; Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, San Francisco, CA, USA; Veterans Affairs Medical Center, San Francisco, CA, USA
SCell funding source(s)
This work has been supported by UCSF-CTSI UL1 TR000004; a Shurl and Kay Circa Foundation Research Grant; a Damon Runyon Cancer Research Foundation postdoctoral fellowship (DRG-2166-13); a VA award 5I01 BX000252; and by NIH awards U01 MH105989 and R01NS075998.

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