scholarly journals Guidelines for correlation coefficient threshold settings in metabolite correlation networks exemplified on a potato association panel

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
David Toubiana ◽  
Helena Maruenda
Keyword(s):  
Correlation Coefficient ◽  
Network Topology ◽  
Potato Association ◽  
Correlation Network ◽  
Water Recovery ◽  
Irrigation Network ◽  
Association Panel ◽  
Correlation Networks ◽  
Multiple Hypotheses Testing ◽  
Correlation Threshold

Abstract Background Correlation network analysis has become an integral tool to study metabolite datasets. Networks are constructed by omitting correlations between metabolites based on two thresholds—namely the r and the associated p-values. While p-value threshold settings follow the rules of multiple hypotheses testing correction, guidelines for r-value threshold settings have not been defined. Results Here, we introduce a method that allows determining the r-value threshold based on an iterative approach, where different networks are constructed and their network topology is monitored. Once the network topology changes significantly, the threshold is set to the corresponding correlation coefficient value. The approach was exemplified on: (i) a metabolite and morphological trait dataset from a potato association panel, which was grown under normal irrigation and water recovery conditions; and validated (ii) on a metabolite dataset of hearts of fed and fasted mice. For the potato normal irrigation correlation network a threshold of Pearson’s |r|≥ 0.23 was suggested, while for the water recovery correlation network a threshold of Pearson’s |r|≥ 0.41 was estimated. For both mice networks the threshold was calculated with Pearson’s |r|≥ 0.84. Conclusions Our analysis corrected the previously stated Pearson’s correlation coefficient threshold from 0.4 to 0.41 in the water recovery network and from 0.4 to 0.23 for the normal irrigation network. Furthermore, the proposed method suggested a correlation threshold of 0.84 for both mice networks rather than a threshold of 0.7 as applied earlier. We demonstrate that the proposed approach is a valuable tool for constructing biological meaningful networks.

scholarly journals Morphological and metabolic profiling of a tropical‐adapted potato association panel subjected to water recovery treatment reveals new insights into plant vigor

2020 ◽  
Vol 103 (6) ◽  
pp. 2193-2210
Author(s):  
David Toubiana ◽  
Rodrigo Cabrera ◽  
Elisa Salas ◽  
Chiara Maccera ◽  
Gabriel Franco dos Santos ◽  
...  
Keyword(s):  
Metabolic Profiling ◽  
Potato Association ◽  
Water Recovery ◽  
Association Panel ◽  
Plant Vigor ◽  
Recovery Treatment

scholarly journals Exploring the Predictability of 30-Day Extreme Precipitation Occurrence Using a Global SST–SLP Correlation Network

2016 ◽  
Vol 29 (3) ◽  
pp. 1013-1029 ◽  
Author(s):  
Mengqian Lu ◽  
Upmanu Lall ◽  
Jaya Kawale ◽  
Stefan Liess ◽  
Vipin Kumar
Keyword(s):  
Extreme Precipitation ◽  
Time Lag ◽  
Principal Component Regression ◽  
Principal Component ◽  
Correlation Network ◽  
Climatic Data ◽  
Wet Season ◽  
Predictive Skill ◽  
Correlation Networks ◽  
Time Lagged

Abstract Correlation networks identified from financial, genomic, ecological, epidemiological, social, and climatic data are being used to provide useful topological insights into the structure of high-dimensional data. Strong convection over the oceans and the atmospheric moisture transport and flow convergence indicated by atmospheric pressure fields may determine where and when extreme precipitation occurs. Here, the spatiotemporal relationship among sea surface temperature (SST), sea level pressure (SLP), and extreme global precipitation is explored using a graph-based approach that uses the concept of reciprocity to generate cluster pairs of locations with similar spatiotemporal patterns at any time lag. A global time-lagged relationship between pentad SST anomalies and pentad SLP anomalies is investigated to understand the linkages and influence of the slowly changing oceanic boundary conditions on the development of the global atmospheric circulation. This study explores the use of this correlation network to predict extreme precipitation globally over the next 30 days, using a logistic principal component regression on the strong global dipoles found between SST and SLP. Predictive skill under cross validation and blind prediction for the occurrence of 30-day precipitation that is higher than the 90th percentile of days in the wet season is indicated for the selected global regions considered.

scholarly journals Identifying the most influential roads based on traffic correlation networks

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Shengmin Guo ◽  
Dong Zhou ◽  
Jingfang Fan ◽  
Qingfeng Tong ◽  
Tongyu Zhu ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Temporal Correlation ◽  
Correlation Network ◽  
Traffic Flows ◽  
Weighted Degree ◽  
Correlation Networks ◽  
Spatio Temporal ◽  
Smart Traffic ◽  
The Impact ◽  
Impact Distance

Abstract Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows.

scholarly journals A New Method to Correct for Habitat Filtering in Microbial Correlation Networks

2018 ◽  
Author(s):  
Vanessa Brisson ◽  
Jennifer Schmidt ◽  
Trent R. Northen ◽  
John P. Vogel ◽  
Amélie Gaudin
Keyword(s):  
Network Analysis ◽  
Microbial Communities ◽  
Simulated Data ◽  
Correlation Network ◽  
Detection Accuracy ◽  
Biological Interactions ◽  
Host Genotype ◽  
Habitat Filtering ◽  
Data Set ◽  
Correlation Networks

AbstractAmplicon sequencing of 16S, ITS, and 18S regions of microbial genomes is a commonly used first step toward understanding microbial communities of interest for human health, agriculture, and the environment. Correlation network analysis is an emerging tool for investigating the interactions within these microbial communities. However, when data from different habitats (e.g sampling sites, host genotype, etc.) are combined into one analysis, habitat filtering (co-occurrence of microbes due to habitat sampled rather than biological interactions) can induce apparent correlations, resulting in a network dominated by habitat effects and masking correlations of biological interest. We developed an algorithm to correct for habitat filtering effects in microbial correlation network analysis in order to reveal the true underlying microbial correlations. This algorithm was tested on simulated data that was constructed to exhibit habitat filtering. Our algorithm significantly improved correlation detection accuracy for these data compared to Spearman and Pearson correlations. We then used our algorithm to analyze a real data set of 16S-V4 amplicon sequences that was expected to exhibit habitat filtering. Our algorithm was found to effectively reduce habitat effects, enabling the construction of consensus correlation networks from data sets combining multiple related sample habitats.

Network Approach to Inducing Coordinative Structures of Skillful Movements

2011 ◽  
Vol 15 (8) ◽  
pp. 988-996
Author(s):  
Masanori Tsujino ◽  
◽  
Tsutomu Fujinami ◽  
Keisuke Nagai
Keyword(s):  
Motion Capture ◽  
Anatomical Structure ◽  
The Body ◽  
Influential Factor ◽  
Correlation Network ◽  
Rhythmic Pattern ◽  
Body Parts ◽  
Correlation Networks ◽  
Coordinative Structure ◽  
Similarities And Differences

Even though coordination is the key to explaining skillful movement, as advocated by Bernstein, analyzing the coordinative structure of body parts remains yet to be fully addressed. Pattern matching applied to analyzing skillful movement cannot describe the coordinative structure. A correlation network is useful for identifying the most influential factor in the web of correlations among factors. The correlation network is thus thought to be effective in analyzing coordinative structures because it enables us to identify the body partmost influential in skillful movement. As an example of skillful movement, we investigated traditional Japanese Heike-daiko drumming to see if we could describe the coordinative structure through this approach. We created correlation networks among body parts involved in playing the Heike-daiko. We asked a Heike-daiko player to play a rhythmic pattern typical of traditional drumming and collected data on movement using a motion capture device. We split the performance sequence into 10 sections, each exhibiting a unique characteristic of the player. It was difficult for onlookers to distinguish these 10 patterns because differences were too subtle to recognize visually. By applying our method to data, we found overlaps among the 10 sections in that the same set of body parts tends to form a network through the sequence. Results suggest that movement similarities and differences can be captured by comparing correlation networks among body parts. We also found two classes of coordinative structure, one reflecting our anatomical structure and the other quite different from it. We found that second class classifies skillful movement.

Abstract 663: Metabolomics And Correlation Network Analysis Of Hypertension

Hypertension ◽  
2014 ◽  
Vol 64 (suppl_1) ◽  
Author(s):  
zhongmin tian ◽  
le wang ◽  
entai hou ◽  
qiong sun
Keyword(s):  
Amino Acids ◽  
Comprehensive Evaluation ◽  
Small World ◽  
Clustering Coefficient ◽  
Correlation Network ◽  
Beta Alanine ◽  
Topological Parameters ◽  
Correlation Networks ◽  
Metabolomics Study ◽  
Small World Property

The awareness, treatment and controls rates of hypertension for people in their 20s and 30s age are much lower than average. In this paper, a GC/MS based metabolomics study was performed in plasma of young hypertensive men and age-matched normal ones. Correlations of the identified metabolites were analyzed and visualized. A systematic correlation network was constructed with the significance of correlation coefficient setting at threshold of 0.6. Glycine, lysine, cystine and beta-alanine were selected as the most important nodes of the network, with high values of degree. A relatively short average path length and high clustering coefficient suggested a small-world property of the network. Moreover, differential metabolites in young hypertensive men were used to construct a core correlation network for further understanding. Four hubs (lysine, glycine, cystine and tryptophan) were confirmed by a comprehensive evaluation of three centrality indices. The statistical and topological parameters of the network indicated that local disturbance to hubs would rapidly transfer to the whole network. These results demonstrated that the distinct metabolic profiles of young hypertensive men might be due to perturbation of the biosynthesis pathway of amino acids. Integrated analyses of metabolomics and correlation networks would provide a broadened window for further understanding of hypertension. Key Words: metabolomics; hypertension; correlation network; amino acids; statistical and topological characteristics; centrality indices; hubs

scholarly journals New Statistical Methods for Constructing Robust Differential Correlation Networks

2018 ◽  
Author(s):  
Danyang Yu ◽  
Zeyu Zhang ◽  
Kimberly Glass ◽  
Jessica Su ◽  
Dawn L. DeMeo ◽  
...  
Keyword(s):  
Type I Error ◽  
Correlation Network ◽  
Type I ◽  
Computationally Efficient ◽  
Differential Correlation ◽  
Correlation Networks ◽  
Inflated Type ◽  
Correlation Tests ◽  
Computationally Intensive ◽  
Z Transformation

AbstractThe interplay among microRNAs (miRNAs) plays an important role in the developments of complex human diseases. Co-expression networks can characterize the interactions among miRNAs. Differential correlation network is a powerful tool to investigate the differences of co-expression networks between cases and controls. To construct a differential correlation network, the Fisher’s Z-transformation test is usually used. However, the Fisher’s Z-transformation test requires the normality assumption, the violation of which would result in inflated Type I error rate. Several bootstrapping-based improvements for Fisher’s Z test have been proposed. However, these methods are too computationally intensive to be used to construct differential correlation networks for high-throughput genomic data. In this article, we proposed six novel robust equal-correlation tests that are computationally efficient. The systematic simulation studies and a real microRNA data analysis showed that one of the six proposed tests (ST5) overall performed better than other methods.

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.

scholarly journals Community Structure and Systemic Risk of Bank Correlation Networks Based on the U.S. Financial Crisis in 2008

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 162
Author(s):  
Yajing Huang ◽  
Feng Chen
Keyword(s):  
Community Structure ◽  
Financial Crisis ◽  
Systemic Risk ◽  
Balance Sheet ◽  
Banking System ◽  
Financial System ◽  
Point Of View ◽  
Correlation Network ◽  
Power Law Distribution ◽  
Correlation Networks

This paper studies the community structure of the bank correlation network in the financial system and analyzes the systemic risk of the community sub-networks. Based on the balance sheet data of U.S. commercial banks from 2008, we establish a bank correlation network for each state according to the banks’ investment portfolio ratio. First, we analyze the community structure of each bank’s correlation network and verify the effectiveness of the community division from the point of view of the importance of nodes. Then, combining the data of failed banks after the 2008 financial crisis, we find that for small communities, the financial systemic risk will appear to have obvious volatility, and it is quite likely to reach an extremely high level. With the increase in the number of nodes in the community, systemic risk will tend towards a stable and low level. Furthermore, if only communities with failed banks are considered, the regression analysis shows that systemic risk and the size of the community almost follow a power law distribution trend. These results reveal the importance of supervising the banking system at the level of community sub-networks, which has certain guiding significance for the stability of the financial system.

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