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Protocols

EDASeq specifications

Information


Unique identifier OMICS_01231
Name EDASeq
Alternative name Exploratory Data Analysis and Normalization for RNA-Seq
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data RNA-Seq read data
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License Artistic License version 2.0
Computer skills Advanced
Version 2.14.1
Stability Stable
Requirements
AnnotationDbi, methods, graphics, BiocGenerics, GenomicRanges, Biostrings, BiocStyle, edgeR, DESeq, biomaRt, GenomicFeatures, knitr, KernSmooth, Biobase(>=2.15.1), ShortRead(>=1.11.42), IRanges(>=1.13.9), aroma.light, Rsamtools(>=1.5.75), yeastRNASeq, leeBamViews
Maintained Yes

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Documentation


EDASeq citations

 (57)
library_books

Bronchoalveolar lavage (BAL) cells in idiopathic pulmonary fibrosis express a complex pro inflammatory, pro repair, angiogenic activation pattern, likely associated with macrophage iron accumulation

2018
PMCID: 5896901
PMID: 29649237
DOI: 10.1371/journal.pone.0194803

[…] from the gene expression omnibus database, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse79544., normalization of rna-sequences was obtained using the upper-quartile method provided by edaseq package in r bioconductor, followed by removing unwanted variation (ruv) technique using ruvg function provided by r bioconductor package ruvseq, where a set of 20 house keeping genes […]

call_split

Personalised drug repositioning for Clear Cell Renal Cell Carcinoma using gene expression

2018
PMCID: 5869697
PMID: 29588458
DOI: 10.1038/s41598-018-23195-8
call_split See protocol

[…] as determined by the original tcga analysis using hierarchical cluster analysis. the genes were normalized within samples by gene length and between samples to correct for sequencing depth using the edaseq package (version 2.10.0). only genes which were expressed above 0.5 counts per million (cpm) in at least a sixth of all samples were retained, i.e. selection was irrespective of tissue type. […]

library_books

Non Pleiotropic Coupling of Daily and Seasonal Temporal Isolation in the European Corn Borer

2018
PMCID: 5924522
PMID: 29587435
DOI: 10.3390/genes9040180

[…] libraries (the minimum number of libraries representing the two strains at a given time point). libraries were normalized for gc content and between-library sequencing depth using the r package edaseq [] and input as offsets along with the raw counts into edger []., differential expression analysis was done by fitting negative binomial generalized log-linear models in edger []. specific […]

library_books

Transcriptome and proteome profiling reveals stress induced expression signatures of imiquimod treated Tasmanian devil facial tumor disease (DFTD) cells

2018
PMCID: 5882306
PMID: 29662615
DOI: 10.18632/oncotarget.24634

[…] this analysis, technical replicates were combined and genes with less than 20 aligned reads across all samples were removed. expression levels were normalized by upper quartile normalisation using edaseq [, ]. differential expression was calculated using limma/voom [] (). volcano plots of differential gene expression data were created using the r plot function, and heat maps were produced […]

library_books

An integrated flow cytometry based platform for isolation and molecular characterization of circulating tumor single cells and clusters

2018
PMCID: 5864750
PMID: 29568081
DOI: 10.1038/s41598-018-23217-5

[…] fasta sequence and gene feature annotation files were obtained from thermo fisher and combined with mm10 reference information. gene level counts were upper-quartile normalized using the r package edaseq and converted to transcripts per million (tpm) using the gene effective length., precise plates were prepared for sequencing following manufacturer’s instructions for the bd preciseâ„¢ reagents […]

library_books

Integration of multiple networks and pathways identifies cancer driver genes in pan cancer analysis

2018
PMCID: 5756345
PMID: 29304754
DOI: 10.1186/s12864-017-4423-x

[…] within-lane normalization procedures to adjust for gc-content effects on read counts and between-lane normalization procedures to adjust for distributional differences between lanes using the edaseq package [] as reported in [, ].table 1, for each cancer type, we performed a differential expression analysis (dea) between two classes, normal vs tumoural, using tcgabiolinks [, ], […]


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EDASeq institution(s)
Department of Statistical Sciences, University of Padua, Italy; Department of Genetics, Stanford University, Standford, CA, USA; Division of Biostatistics and Department of Statistics, University of California, Berkeley, CA, USA
EDASeq funding source(s)
This work was funded by grant R01 HG03468 from the NHGRI at the NIH and grant CPDA094285 from the University of Padua.

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