Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

10.3791/62205 ◽  
2021 ◽  
Author(s):  
Jing Tang ◽  
Xinzhu He ◽  
Ming Tang ◽  
Xiaorong Xu ◽  
Ximin Zhang
Keyword(s):  
Correlation Networks ◽  
Root Microbiota ◽  
Weighted Correlation

scholarly journals Identifying core biological processes distinguishing human eye tissues with precise systems-level gene expression analyses and weighted correlation networks

2018 ◽  
Vol 27 (19) ◽  
pp. 3325-3339 ◽  
Author(s):  
John M Bryan ◽  
Temesgen D Fufa ◽  
Kapil Bharti ◽  
Brian P Brooks ◽  
Robert B Hufnagel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Biological Processes ◽  
Human Eye ◽  
Correlation Networks ◽  
Eye Tissues ◽  
Gene Expression Analyses ◽  
Weighted Correlation

SPRR2C, DEFB4A, WIF1, CRY2, and KRT19 are correlated with the development of atopic eczema

2020 ◽  
Author(s):  
Yiwu Yu ◽  
Yufei Li ◽  
Miao Jiang ◽  
Jingjun Zhao
Keyword(s):  
Significant Positive Correlation ◽  
Gene Expression Omnibus ◽  
Cytokine Receptor ◽  
Receptor Interaction ◽  
Positive Correlation ◽  
Correlation Networks ◽  
Upregulated Genes ◽  
Key Nodes ◽  
Weighted Correlation ◽  
Blue Module

Abstract BackgroundAtopic eczema (AE) is a chronic relapsing inflammatory skin disease. The objective of this study was to identify key genes related to the development of AE.MethodsThe GSE6012 dataset was obtained from the Gene Expression Omnibus (GEO) database. The limma package was used to analyze differentially expressed genes (DEGs). Then, the weighted gene co-expression network analysis (WGCNA) package was utilized to generate weighted correlation networks of up- and downregulated genes. Additionally, the WGCNA package was used for enrichment analyses to explore the underlying functions of DEGs in modules (weighted correlation sub-networks) significantly associated with AE.ResultsA total of 515 DEGs were identified between lesional and non-lesional skin samples. For the upregulated genes, the blue module was found to have a significant positive correlation with AE. Importantly, small proline-rich protein 2C (SPRR2C) and defensin, beta 4A (DEFB4A) exhibited higher |log fold change (FC)| values and were the key nodes of the network. Moreover, KEGG pathway analysis revealed that the upregulated genes in the blue module were primarily involved in cytokine-cytokine receptor interaction. Additionally, for the downregulated genes, the brown module was found to have a significant positive correlation with AE. Further, WNT inhibitory factor 1 (WIF1), cryptochrome 2 (CRY2), and keratin 19 (KRT19) had higher |log FC| values and were key nodes of the network.ConclusionSPRR2C, DEFB4A, WIF1, CRY2, KRT19 and cytokine-cytokine receptor interaction might be correlated with the development of AE.

scholarly journals Correlation network analysis between phenotypic and genotypic traits of chili pepper

2016 ◽  
Vol 51 (4) ◽  
pp. 372-377 ◽  
Author(s):  
Anderson Rodrigo da Silva ◽  
Elizanilda Ramalho do Rêgo ◽  
Angela Maria dos Santos Pessoa ◽  
Maílson Monteiro do Rêgo
Keyword(s):  
Chili Pepper ◽  
Correlation Network ◽  
Fruit Traits ◽  
Network Information ◽  
Correlation Networks ◽  
Correlation Structures ◽  
Seedling Plant ◽  
Leaf Dimensions ◽  
Link Pattern ◽  
Weighted Correlation

Abstract: The objective of this work was to build weighted correlation networks, in order to discover correlation structures and link patterns among 28 morphoagronomic traits of chili pepper related to seedling, plant, inflorescence, and fruit. Phenotypic and genotypic information of 16 Capsicum genotypes were analyzed. Correlation structures and link patterns can be easily identified in the matrices using the Fruchterman-Reingold algorithm with correlation network information. Both types of correlations showed the same general link pattern among fruit traits, with high broad-sense heritability values and high aptitude of the genotypes for agronomic and ornamental breeding. Leaf dimensions are correlated with a cluster of fruit traits. Correlation networks of chili pepper traits may increase the effectiveness of genotype selection, since both correlated traits and groups can be identified.

Evolving Cascade-Correlation Networks for Time-Series Forecasting.

1994 ◽  
Author(s):  
J. R. McDonnell ◽  
D. E. Waagen
Keyword(s):  
Time Series ◽  
Time Series Forecasting ◽  
Correlation Networks ◽  
Cascade Correlation

The root microbiota—a fingerprint in the soil?

2013 ◽  
Vol 370 (1-2) ◽  
pp. 671-686 ◽  
Author(s):  
Aleklett Kristin ◽  
Hart Miranda
Keyword(s):  
Root Microbiota

scholarly journals COVID-19 lockdown has altered the dynamics between affective symptoms and social isolation among older adults: results from a longitudinal network analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Junhong Yu ◽  
Rathi Mahendran
Keyword(s):  
Older Adults ◽  
Social Isolation ◽  
Anxiety Symptoms ◽  
Community Dwelling ◽  
Depression Symptoms ◽  
Network Analyses ◽  
Correlation Networks ◽  
Affective Symptoms ◽  
Tightly Coupled ◽  
Community Dwelling Older Adults

AbstractThe COVID-19 lockdown has drastically limited social interactions and brought about a climate of fear and uncertainty. These circumstances not only increased affective symptoms and social isolation among community dwelling older adults but also alter the dynamics between them. Using network analyses, we study the changes in these dynamics before and during the lockdown. Community-dwelling older adults (N = 419) completed questionnaires assessing depression, anxiety, and social isolation, before the COVID-19 pandemic, as part of a cohort study, and during the lockdown period. The total scores of these questionnaires were compared across time. For the network analyses, partial correlation networks were constructed using items in the questionnaires as nodes, separately at both timepoints. Changes in edges, as well as nodal and bridge centrality were examined across time. Depression and anxiety symptoms, and social isolation had significantly increased during the lockdown. Significant changes were observed across time on several edges. Greater connectivity between the affective and social isolation nodes at lockdown was observed. Depression symptoms have become more tightly coupled across individuals, and so were the anxiety symptoms. Depression symptoms have also become slightly decoupled from those of anxiety. These changing network dynamics reflect the greater influence of social isolation on affective symptoms across individuals and an increased vulnerability to affective disorders. These findings provide novel perspectives and translational implications on the changing mental health context amidst a COVID-19 pandemic situation.

scholarly journals A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2333
Author(s):  
Lilu Zhu ◽  
Xiaolu Su ◽  
Yanfeng Hu ◽  
Xianqing Tai ◽  
Kun Fu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Temporal Correlation ◽  
Local Association ◽  
Sensing Data ◽  
Multi Scale ◽  
Correlation Networks ◽  
Efficient Integration ◽  
Spatio Temporal ◽  
Query Algorithm

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

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