DeconRNASeq protocols

View DeconRNASeq computational protocol

DeconRNASeq statistics

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


Citations per year

Citations chart

Popular tool citations

chevron_left Cell subpopulation deconvolution chevron_right
Popular tools chart

Tool usage distribution map

Tool usage distribution map

Associated diseases

Associated diseases

DeconRNASeq specifications


Unique identifier OMICS_01230
Name DeconRNASeq
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
Stability Stable
Maintained Yes


Add your version


Publication for DeconRNASeq

DeconRNASeq in pipeline

PMCID: 5932385
PMID: 29755449
DOI: 10.3389/fimmu.2018.00638

[…] detectable expression. the rna-seq data discussed here have been deposited in the ncbi gene expression omnibus (geo) () and are accessible through geo series accession number gse108428., we utilized deconrnaseq software () to perform rna-seq deconvolution analysis to infer cell type compositions of the neuronal preparations isolated from alum-injected and alum + klh-immunized mice. deconrnaseq, […]

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

DeconRNASeq in publications

PMCID: 5832224
PMID: 29494600
DOI: 10.1371/journal.pone.0193067

[…] measuring the required input. examples of such partial deconvolution methods are lsfit and cs-lsfit [] which use least-squares fitting, or the quadratic programming approaches used in qprog [] and deconrnaseq []. the drawback of these methods is that they require hard-to-obtain information on either the sample compositions or high-quality pure cell-type signatures., complete deconvolution […]

PMCID: 5818005
PMID: 29361568
DOI: 10.1242/dev.158501

[…] agglomeration methods were used for cluster analyses. fractional identity between pre-implantation stages and in vitro cultured cells was determined via quadratic programming using the r package deconrnaseq (). average expression levels of cells comprising distinct stages were used as the ‘signature’ dataset, and the relative identity of each culture protocol/sample group was computed […]

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

[…] 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 non-negative decomposition algorithm through quadratic programming for estimating the mixing proportions of distinctive tissue types. […]

PMCID: 5534072
PMID: 28754123
DOI: 10.1186/s13073-017-0458-5

[…] 10,000 null indices and compared to the mse of darmanis et al.-derived indices., cell type deconvolution analysis was confirmed using a previously published algorithm implemented in the r package deconrnaseq []. the “datasets” input to the deconrnaseq function was a normalized count matrix of all ancg brain samples and the “signatures” input consisted of a normalized count matrix […]

PMCID: 5718706
PMID: 29130882
DOI: 10.7554/eLife.26476.049

[…] cell proportions, we summed together the sub-types predictions of cibersort within each major immune cell type., based on the reference profiles and signature genes we derived here for epic., deconrnaseq (v1.16) () does not contain immune cell reference profiles and we used the reference profiles we derived here as well as the corresponding signature genes. we present the results […]

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

DeconRNASeq institution(s)
Biomarker Development, Translational Medicine, Novartis Institutes for BioMedical Research, Cambridge, MA, USA

DeconRNASeq reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review DeconRNASeq