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Protocols

QuasiSeq specifications

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Unique identifier OMICS_01963
Name QuasiSeq
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, GNU General Public License version 2.0
Computer skills Advanced
Version 1.0-10-1
Stability Stable
Maintained Yes

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  • person_outline Long Qu <>

Publication for QuasiSeq

QuasiSeq citations

 (17)
library_books

Optimization of an RNA Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size

2018
PMCID: 5817962
PMID: 29491871
DOI: 10.3389/fpls.2018.00108

[…] 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. the next best performing packages are edger and deseq2. in other studies, both edger and deseq […]

library_books

The In Feed Antibiotic Carbadox Induces Phage Gene Transcription in the Swine Gut Microbiome

2017
PMCID: 5550749
PMID: 28790203
DOI: 10.1128/mBio.00709-17

[…] by the spiked-in rna counts of individual samples to normalize the counts to the weight of the original fecal samples. statistics were all completed with counts normalized to spiked-in rna counts. quasiseq v. 1.0-8, an r bioconductor package, was used to compare samples treated with carbadox-treated and untreated samples. the statistical model used normalized read counts as the dependent […]

library_books

RD26 mediates crosstalk between drought and brassinosteroid signalling pathways

2017
PMCID: 5333127
PMID: 28233777
DOI: 10.1038/ncomms14573

[…] were used to independently calculate the read depth of each annotated gene. genes with an average of at least one uniquely mapped read across samples were tested for differential expression using quasiseq (http://cran.r-project.org/web/packages/quasiseq). the generalized linear model quasi-likelihood spline method assuming negative binomial distribution of read counts implemented […]

library_books

Post weaning blood transcriptomic differences between Yorkshire pigs divergently selected for residual feed intake

2016
PMCID: 4724083
PMID: 26801403
DOI: 10.1186/s12864-016-2395-x

[…] the statistical programming language r (version 3.1.0) was used for all statistical analyses, unless indicated otherwise. differential expression analysis was carried out by using the r package “quasiseq” (version 1.0-4) []. for each of the 12280 genes in the final count table, we used quasiseq to fit a full generalized linear model with a negative binomial response and a log link […]

library_books

Transcriptome profiling of soybean (Glycine max) roots challenged with pathogenic and non pathogenic isolates of Fusarium oxysporum

2015
PMCID: 4687377
PMID: 26689712
DOI: 10.1186/s12864-015-2318-2

[…] downstream analyses were only performed for soybean and not for f. oxysporum. the abundance of soybean transcripts are expressed in upper quartile normalized counts as calculated by htseq-count and quasiseq programs [, ]. one of the three biological replicates for the fo36 inoculated sample at 96 hpi was excluded for further expression level analysis due to a relatively small number of reads. […]

library_books

Detecting Differentially Expressed Genes with RNA seq Data Using Backward Selection to Account for the Effects of Relevant Covariates

2015
PMCID: 4666287
PMID: 26660449
DOI: 10.1007/s13253-015-0226-1

[…] et al. (). a recent review of methods was provided by lorenz et al. (). to conduct our tests for differential expression and to assess the significance of covariates, we use the r (r core team ) quasiseq package, which implements the quasi-likelihood testing method developed by lund et al. (). this approach was recently found by burden et al. () to be the “best performing package […]


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QuasiSeq institution(s)
Statistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD, USA; Department of Statistics, Iowa State University, Ames, IA, USA; University of Oxford, Oxford, UK; Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
QuasiSeq funding source(s)
Supported by the National Research Initiative of the USDA-CSREES Grant No. 2008-35600-18786; the National Science Foundation grant number 0820610; the General Sir John Monash Scholarship; and the National Health and Medical Research Council Program Grant 490037.

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