sSeq statistics

info info

Citations per year

Number of citations per year for the bioinformatics software tool sSeq
info

Tool usage distribution map

This map represents all the scientific publications referring to sSeq per scientific context
info info

Associated diseases

info

Popular tool citations

chevron_left Differential expression Normalization chevron_right
Want to access the full stats & trends on this tool?

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 […]


Want to access the full list of citations?
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.

sSeq reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review sSeq