scholarly journals Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA)

PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0240523
Author(s):  
Deisy Morselli Gysi ◽  
Tiago de Miranda Fragoso ◽  
Fatemeh Zebardast ◽  
Wesley Bertoli ◽  
Volker Busskamp ◽  
...  
2017 ◽  
Vol 50 (3) ◽  
pp. 1700075 ◽  
Author(s):  
Guillaume Noell ◽  
Borja G. Cosío ◽  
Rosa Faner ◽  
Eduard Monsó ◽  
German Peces-Barba ◽  
...  

2016 ◽  
Vol 7 (1) ◽  
pp. e2040-e2040 ◽  
Author(s):  
S Zickenrott ◽  
V E Angarica ◽  
B B Upadhyaya ◽  
A del Sol

PLoS ONE ◽  
2014 ◽  
Vol 9 (7) ◽  
pp. e101900 ◽  
Author(s):  
Sriram Devanathan ◽  
Timothy Whitehead ◽  
George G. Schweitzer ◽  
Nicole Fettig ◽  
Attila Kovacs ◽  
...  

2017 ◽  
Vol 34 (4) ◽  
pp. 701-702 ◽  
Author(s):  
Karan Uppal ◽  
Chunyu Ma ◽  
Young-Mi Go ◽  
Dean P Jones

2017 ◽  
Author(s):  
Karan Uppal ◽  
Young-Mi Go ◽  
Dean P. Jones

AbstractSummaryIntegrative omics is a central component of most systems biology studies. Computational methods are required for extracting meaningful relationships across different omics layers. Various tools have been developed to facilitate integration of paired heterogenous omics data; however most existing tools allow integration of only two omics datasets. Further-more, existing data integration tools do not incorporate additional steps of identifying sub-networks or communities of highly connected entities and evaluating the topology of the integrative network under different conditions. Here we present xMWAS, an R package for data integration, network visualization, clustering, differential network analysis of data from biochemical and phenotypic assays, and two or more omics platforms.Availabilityhttps://sourceforge.net/projects/xmwas/[email protected]


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