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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

 (47)
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
PLoS One
PMCID: 5896901
PMID: 29649237
DOI: 10.1371/journal.pone.0194803

[…] vailable 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 were used […]

call_split

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

2018
Oncotarget
PMCID: 5882306
PMID: 29662615
DOI: 10.18632/oncotarget.24634
call_split See protocol

[…] or 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 using […]

library_books

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

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

[…] 92 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. […]

library_books

Comprehensive analysis of normal adjacent to tumor transcriptomes

2017
Nat Commun
PMCID: 5651823
PMID: 29057876
DOI: 10.1038/s41467-017-01027-z

[…] Dimensionality reduction. Dimensionality reduction was performed using the Rtsne (version 0.10) package and the EDASeq package on the log2 CPM values (RNA-seq), or log2 RMA values (microarray). The deconvolution procedure was performed using the DeconRNASeq package. This algorithm adopts a globally optimized no […]

call_split

PRC2 specifies ectoderm lineages and maintains pluripotency in primed but not naïve ESCs

2017
Nat Commun
PMCID: 5610324
PMID: 28939884
DOI: 10.1038/s41467-017-00668-4
call_split See protocol

[…] q system with NextSeq 500 Mid Output Kit v2 (150 cycles).The number of raw reads mapped to human mRNA reference sequence for GRCh38/hg38 using RSEM (rsem-1.2.4), Bowtie2 (v2.2.5), and normalized with EDASeq (v2.2.0). Gene expression is expressed as “normalized tag count.” Other downstream analyses were performed using glbase. In brief, differential expression between differentiation state and the […]

call_split

Whole transcriptome analysis delineates the human placenta gene network and its associations with fetal growth

2017
BMC Genomics
PMCID: 5502484
PMID: 28693416
DOI: 10.1186/s12864-017-3878-0
call_split See protocol

[…] per million <1 in greater than 30 samples (the sample size of the smallest phenotypic group in this study) were considered unexpressed and removed. Read counts were adjusted for GC content using the EDASeq R package [], followed by TMM correction for library size differences across samples using the calcNormFactors function in edgeR R package []. The data was then transformed into logCPM values a […]

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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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