RNASeqPower protocols

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RNASeqPower specifications

Information


Unique identifier OMICS_15095
Name RNASeqPower
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 1.14.0
Stability Stable
Maintained Yes

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Publication for RNASeqPower

RNASeqPower in pipelines

 (3)
2018
PMCID: 5904416
PMID: 29666288
DOI: 10.1128/mBio.00575-18

[…] the statistical power of our study, and thus the likelihood of identifying a true positive, was found to be 0.8 when the effect size was set at 2.75-fold cutoff, calculated using the r program rnaseqpower (v3.5) () within the bioconductor package using a sample size of 3, a false-discovery rate of 0.05, and the lowest sequencing depth in all libraries (determined through samtools […]

2017
PMCID: 5414158
PMID: 28464817
DOI: 10.1186/s12920-017-0270-5

[…] was performed using deseq2 []. this software was also used to create a matrix of normalized expression for each gene by calculating fragments per million mapped fragments (fpm). utilizing the rnaseqpower package [] in r, we estimated that for alpha of 0.05 and power (1-beta) of 0.8, with an estimated biological variation coefficient of 0.5 (humans), and around 30 cases of poaf, we could […]

2017
PMCID: 5514905
PMID: 28402935
DOI: 10.18632/oncotarget.16664

[…] expressed mirnas with a corrected p-value < 1 × 10−10 and an absolute log2 fold change > 3.5 were selected for validation analysis. the statistical power of the study was calculated using the rnaseqpower package []. the small rna-seq data have been deposited in ncbi's gene expression omnibus [] and are accessible through geo series accession number gse89051 […]


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RNASeqPower in publications

 (13)
PMCID: 5904416
PMID: 29666288
DOI: 10.1128/mBio.00575-18

[…] the statistical power of our study, and thus the likelihood of identifying a true positive, was found to be 0.8 when the effect size was set at 2.75-fold cutoff, calculated using the r program rnaseqpower (v3.5) () within the bioconductor package using a sample size of 3, a false-discovery rate of 0.05, and the lowest sequencing depth in all libraries (determined through samtools […]

PMCID: 5442695
PMID: 28535780
DOI: 10.1186/s12864-017-3797-0

[…] the pe data cbiological coefficient of variation derived from the estimatecommondispersion function of edger for the se data dfold change ethe power calculations performed for the pe data using the rnaseqpower package fthe power calculations performed for the se data using the rnaseqpower package , aaverage depth of coverage, bbiological coefficient of variation derived […]

PMCID: 5514905
PMID: 28402935
DOI: 10.18632/oncotarget.16664

[…] expressed mirnas with a corrected p-value < 1 × 10−10 and an absolute log2 fold change > 3.5 were selected for validation analysis. the statistical power of the study was calculated using the rnaseqpower package []. the small rna-seq data have been deposited in ncbi's gene expression omnibus [] and are accessible through geo series accession number gse89051 […]

PMCID: 5245767
PMID: 27748765
DOI: 10.1038/onc.2016.382

[…] in mcf7 cells, but would be expected to show limited estrogen stimulation of indirect targets. for each condition, three independent replicate samples were analysed by rna-seq. analysis with the rnaseqpower package in r for our data showed that three replicates gave a power >0.8 for a fold change of 1.5. as the sequencing data for one of the three replicates for mcf7-y537s cells treated […]

PMCID: 4839710
PMID: 27100792
DOI: 10.1371/journal.pone.0153782

[…] matrix. quantification results of htseq and rsem were further normalized by using cqn []. roc curves were calculated using proc package []. power analysis was performed with scotty [] and r package rnaseqpower []., the spike-ins plots were done using matlab, partek and r. for spike-ins quantification plots an offset of 10e-3 was added to fpkm values before log2 transformation., ebseq [] […]


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RNASeqPower institution(s)
Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Chinahester, MN, USA; Mayo Vaccine Research Group, Mayo Clinic, Chinahester, MN, USA; Program in Translational Immunovirology and Biodefense, Mayo Clinic, Chinahester, MN, USA
RNASeqPower funding source(s)
This work was funded through the Center for Individualized Medicine at Mayo Clinic (112222) and federal funds from the National Institute of Health, under contract number U01 AI089859.

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