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Software . 2018
Data sources: Datacite
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ZENODO
Software . 2018
Data sources: Datacite
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Software . 2018
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UrszulaCzerwinska/DeconICA: DeconICA first release

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 20 May 2018Publisher:Zenodo
Authors: Urszula Czerwinska;

doi: 10.5281/zenodo.1250069 , 10.5281/zenodo.1250070

UrszulaCzerwinska/DeconICA: DeconICA first release

Abstract

DeconICA AN R PACKAGE FOR IDENTIFYING IMMUNE-RELATED SIGNALS IN TRANSCRIPTOME THROUGH DECONVOLUTION OR UNSUPERVISED SOURCE SEPARATION METHODS You can install deconICA from GitHub with: #install.packages("devtools") devtools::install_github("UrszulaCzerwinska/DeconICA", build_vignettes = TRUE, dependencies = TRUE) The aim of the project is to adapt blind source separation techniques to extract immune-related signals from mixed biological samples. A great example of mixed biological sample is transcriptome measured in heterogenous tissue such as blood or tumor biopsy.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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