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Protocols

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

Versioning


No version available

Documentation


Maintainer


  • person_outline Olga Vitek

Publication for sSeq

sSeq citations

 (7)
call_split

Single Cell Deconvolution of Fibroblast Heterogeneity in Mouse Pulmonary Fibrosis

2018
Cell Rep
PMCID: 5908225
PMID: 29590628
DOI: 10.1016/j.celrep.2018.03.010
call_split See protocol

[…] alysis was performed for dimension reduction. Top 10 principal components (PCs) were selected by using a permutation-based test implemented in Seurat and passed to t-SNE for clustering visualization. sSeq version 1.0.0 integrated in the Cell Ranger R kit was used for modeling the gene expression with negative binomial distribution to identify genes whose expression was enriched in specific cluster […]

library_books

Regulatory Architecture of the LβT2 Gonadotrope Cell Underlying the Response to Gonadotropin Releasing Hormone

2018
PMCID: 5816955
PMID: 29487567
DOI: 10.3389/fendo.2018.00034

[…] le-cell 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 e […]

library_books

Non equivalence of Wnt and R spondin ligands during Lgr5+ intestinal stem cell self renewal

2017
Nature
PMCID: 5641471
PMID: 28467820
DOI: 10.1038/nature22313

[…] dispersion (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). There are a few difference […]

library_books

Modeling Exon Specific Bias Distribution Improves the Analysis of RNA Seq Data

2015
PLoS One
PMCID: 4598124
PMID: 26448625
DOI: 10.1371/journal.pone.0140032

[…] s 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 and other […]

library_books

Dynamics in Transcriptomics: Advancements in RNA seq Time Course and Downstream Analysis

2015
Comput Struct Biotechnol J
PMCID: 4564389
PMID: 26430493
DOI: 10.1016/j.csbj.2015.08.004

[…] negative binomial methods is less accurate. Therefore, a new shrinkage estimation has been introduced in order to analyze data with few replicates (4 or less), which was incorporated into a new tool sSeq . Moreover, resampling of at least three biological replicates per time point was shown to improve the identification of oscillating genes without increasing false positives rates . Recently, a n […]

library_books

Comparative exomics of Phalaris cultivars under salt stress

2014
BMC Genomics
PMCID: 4240679
PMID: 25573273
DOI: 10.1186/1471-2164-15-S6-S18

[…] eral 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 […]

Citations

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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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