sSeq specifications

Information


Unique identifier OMICS_01962
Name sSeq
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 2.0
Computer skills Advanced
Version 1.0.0
Stability Stable
Requirements
RColorBrewer, R(>=3.0), caTools
Maintained Yes

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Documentation


Maintainer


  • person_outline Olga Vitek <>

Publication for sSeq

sSeq in publications

 (4)
PMCID: 5816955
PMID: 29487567
DOI: 10.3389/fendo.2018.00034

[…] rna-seq data were processed using the cell ranger pipeline v1.3, which provides a data matrix of expression for all genes and all cells. differentially expressed genes were analyzed using the sseq method (), as implemented in the r package cellrangerrkit v1.1. the cell phase computation for the single cells follows the ideas described in the supplementary material of the study by macosko […]

PMCID: 5641471
PMID: 28467820
DOI: 10.1038/nature22313

[…] (expression cutoff=0.0125, and dispersion cutoff=0.5). the first 11 principal components were used for the t-sne projection and clustering analysis (resolution=0.3, k.seed=100)., we applied sseq from yu et al. to identify genes that are enriched in a specific cluster (the specific cluster is assigned as group a, and the rest of clusters is assigned as group b). […]

PMCID: 4598124
PMID: 26448625
DOI: 10.1371/journal.pone.0140032

[…] in the differential expression analysis of rna-seq data due to its ability to model the overdispersion in read distributions [–]. the expression in has the similar parametrization as the nb model, sseq, proposed in []. the expected expression μ and the dispersion parameter ϕ in sseq are analogous to abα and 1a, respectively, in our method. however, our approach is different from sseq […]

PMCID: 4240679
PMID: 25573273
DOI: 10.1186/1471-2164-15-S6-S18

[…] benchmarking studies and de method comparisons [-]. we evaluated the performance of rodeo against parametric de detectors that assume a negative binomial count distribution model (bayseq, edger, sseq) and demonstrate that it outperforms the others on several scenarios (details in the methods section). moreover, rodeo's parameter-free framework is very suitable for the diverse and challenging […]


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sSeq institution(s)
Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Department of Statistics, West Lafayette, IN, USA; Department of Computer Science, Purdue University, West Lafayette, IN, USA
sSeq funding source(s)
Supported by the NSF CAREER award DBI-1054826.

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