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MODA: MOdule Differential Analysis for weighted gene co-expression network

Authors: Li, Dong; Brown, James B.; Orsini, Luisa; Pan, Zhisong; Hu, Guyu; He, Shan;

MODA: MOdule Differential Analysis for weighted gene co-expression network

Abstract

1 Summary Gene co-expression network differential analysis is designed to help biologists understand gene expression patterns under different conditions. We have implemented an R package called MODA (Module Differential Analysis) for gene co-expression network differential analysis. Based on transcriptomic data, MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes. The usefulness of the method is also demonstrated by synthetic data as well as Daphnia magna gene expression data under different environmental stresses.

Keywords

Molecular Networks (q-bio.MN), FOS: Biological sciences, Quantitative Biology - Molecular Networks, Quantitative Biology - Quantitative Methods, Quantitative Methods (q-bio.QM)

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    influence
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selected citations
These citations are derived from selected sources.
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.
BIP!Impulse provided by BIP!
8
Average
Average
Average
Green
hybrid