PoissonSeq protocols

View PoissonSeq computational protocol

PoissonSeq statistics

To access cutting-edge analytics on consensus tools, life science contexts and associated fields, you will need to subscribe to our premium service.

Subscribe
info

Citations per year

Citations chart
info

Popular tool citations

chevron_left Differential expression Normalization chevron_right
Popular tools chart
info

Tool usage distribution map

Tool usage distribution map
info

Associated diseases

Associated diseases

PoissonSeq specifications

Information


Unique identifier OMICS_01950
Name PoissonSeq
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 3.0
Computer skills Advanced
Version 1.1.2
Stability Stable
Requirements
R(≥2.10), splines, combinat
Source code URL https://cran.r-project.org/src/contrib/PoissonSeq_1.1.2.tar.gz
Maintained Yes

Download


Versioning


Add your version

Documentation


Maintainers


  • person_outline Jun Li <>
  • person_outline Daniela Witten <>
  • person_outline Iain Johnstone <>
  • person_outline Robert Tibshirani <>

Publication for PoissonSeq

PoissonSeq in pipeline

2014
PMCID: 4072957
PMID: 24887547
DOI: 10.1186/gb-2014-15-5-r71

[…] using package sam 2.0 []. briefly, the counts from the data were transformed using anscombe transformation [] to stabilize variance, and then normalized using sequencing depths estimated by poissonseq []. the resulting normalized data are roughly gaussian-distributed and the values are comparable across samples [], resembling microarray data. this dataset was used to perform […]


To access a full list of citations, you will need to upgrade to our premium service.

PoissonSeq in publications

 (19)
PMCID: 5898276
PMID: 29681917
DOI: 10.3389/fpls.2018.00458

[…] selected, which do not use log-transformed data as input and has its own normalization procedure (zhang et al., unpublished). they differ in the distribution assumed for the data (poisson-like for poissonseq, negative binomial for edger, deseq2 and ebseq, and non-parametric for noiseq) and the statistical test used by the method (poisson goodness-of-fit for poissonseq, exact/likelihood ratio […]

PMCID: 5817962
PMID: 29491871
DOI: 10.3389/fpls.2018.00108

[…] tool is less critical (unless for noiseq and cuffdiff 2). rapaport et al. () concluded that deseq, edger, and bayseq have superior specificity and sensitivity, and seem to outperform the limma and poissonseq methods. the worst method seems to be cuffdiff. burden et al. () concluded that the quasiseq tool achieves a low fdr providing the number of replicates in each condition is at least 4. […]

PMCID: 5448817
PMID: 28505151
DOI: 10.1371/journal.pcbi.1005515

[…] of the data models underlying statistical tests. this, in turn, leads to a decrease of the chi-square distributed test statistic of the wald test in dss [], deseq2 [], edger [] and the score test in poissonseq [] resulting in a loss of sensitivity. the same holds for the wilcoxon test in samseq [] where the test statistic moves closer to the mean of the test distribution under the 0-hypothesis. […]

PMCID: 5449622
PMID: 28335026
DOI: 10.1093/nar/gkx161

[…] more reads than the bases on their left and their right and whose read coverage is greater than the average coverage of bases on the same gene and the average coverage of all bases; (ii) using the poissonseq algorithm (), the library size of each sequencing lane was estimated based on the high confidence peaks observed in both duplicate libraries at at least one of the five temperatures; […]

PMCID: 5294900
PMID: 28166718
DOI: 10.1186/s12859-017-1498-y

[…] concluded that no method fits for all situations and results from distinct methods could be largely different. rapaport et al. [] assessed commonly used analysis packages (cuffdiff, edger, deseq, poissonseq [], bayseq, and limma) for rna-seq data. they analyzed human rna-seq data with those methods and emphasize the importance of large sample replicates to accurately detect association […]


To access a full list of publications, you will need to upgrade to our premium service.

PoissonSeq institution(s)
Department of Statistics, Stanford University, Stanford, CA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA; Department of Health Research & Policy, and Statistics, Stanford University, Stanford, CA, USA
PoissonSeq funding source(s)
Supported by National Science Foundation (DMS-9971405) and National Institutes of Health (N01-HV-28183, BIB R01EB1988).

PoissonSeq review

star_border star_border star_border star_border star_border
star star star star star

Andrew Miller

star_border star_border star_border star_border star_border
star star star star star
Desktop
A good method to analyze RNA-seq data based on Poisson goodness-of-fit statistic.