JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

AbstractMotivationA complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.ResultsWe propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data.Availability:R scripts available at https://github.com/jijiadong/JDINACContact:[email protected] information:Supplementary data are available at bioRxiv online.

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JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

Bioinformatics ◽

10.1093/bioinformatics/btx360 ◽

2017 ◽

Vol 33 (19) ◽

pp. 3080-3087 ◽

Cited By ~ 13

Author(s):

Jiadong Ji ◽

Di He ◽

Yang Feng ◽

Yong He ◽

Fuzhong Xue ◽

...

Keyword(s):

Network Analysis ◽

Interaction Network ◽

Joint Density ◽

High Dimensional ◽

Omics Data ◽

Differential Interaction ◽

Non Parametric

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Simultaneous differential network analysis and classification for matrix-variate data with application to brain connectivity

Biostatistics ◽

10.1093/biostatistics/kxab007 ◽

2021 ◽

Author(s):

Hao Chen ◽

Ying Guo ◽

Yong He ◽

Jiadong Ji ◽

Lei Liu ◽

...

Keyword(s):

Network Analysis ◽

Medical Diagnosis ◽

Brain Connectivity ◽

Interaction Patterns ◽

Medical Diagnoses ◽

Differential Network Analysis ◽

Differential Interaction ◽

Clinical Biomarkers ◽

Differential Network ◽

The Matrix

Summary Growing evidence has shown that the brain connectivity network experiences alterations for complex diseases such as Alzheimer’s disease (AD). Network comparison, also known as differential network analysis, is thus particularly powerful to reveal the disease pathologies and identify clinical biomarkers for medical diagnoses (classification). Data from neurophysiological measurements are multidimensional and in matrix-form. Naive vectorization method is not sufficient as it ignores the structural information within the matrix. In the article, we adopt the Kronecker product covariance matrices framework to capture both spatial and temporal correlations of the matrix-variate data while the temporal covariance matrix is treated as a nuisance parameter. By recognizing that the strengths of network connections may vary across subjects, we develop an ensemble-learning procedure, which identifies the differential interaction patterns of brain regions between the case group and the control group and conducts medical diagnosis (classification) of the disease simultaneously. Simulation studies are conducted to assess the performance of the proposed method. We apply the proposed procedure to the functional connectivity analysis of an functional magnetic resonance imaging study on AD. The hub nodes and differential interaction patterns identified are consistent with existing experimental studies, and satisfactory out-of-sample classification performance is achieved for medical diagnosis of AD.

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Understanding neighborhood isolation through spatial interaction network analysis using location big data

Environment and Planning A Economy and Space ◽

10.1177/0308518x19891911 ◽

2019 ◽

Vol 52 (6) ◽

pp. 1027-1031 ◽

Cited By ~ 1

Author(s):

Timothy Prestby ◽

Joseph App ◽

Yuhao Kang ◽

Song Gao

Keyword(s):

Network Analysis ◽

Large Scale ◽

Spatial Interaction ◽

Interaction Network ◽

Location Based Services ◽

Interaction Patterns ◽

Individual Level ◽

Mobility Data ◽

The Usa ◽

Hidden Biases

Hidden biases of racial and socioeconomic preferences shape residential neighborhoods throughout the USA. Thereby, these preferences shape neighborhoods composed predominantly of a particular race or income class. However, the assessment of spatial extent and the degree of isolation outside the residential neighborhoods at large scale is challenging, which requires further investigation to understand and identify the magnitude and underlying geospatial processes. With the ubiquitous availability of location-based services, large-scale individual-level location data have been widely collected using numerous mobile phone applications and enable the study of neighborhood isolation at large scale. In this research, we analyze large-scale anonymized smartphone users’ mobility data in Milwaukee, Wisconsin, to understand neighborhood-to-neighborhood spatial interaction patterns of different racial classes. Several isolated neighborhoods are successfully identified through the mobility-based spatial interaction network analysis.

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Identification of key proteins involved in stickleback environmental adaption with system-level analysis

10.1101/2020.02.11.943522 ◽

2020 ◽

Author(s):

Martina Hall ◽

Dietmar Kültz ◽

Eivind Almaas

Keyword(s):

Protein Interactions ◽

Population Sample ◽

Interaction Network ◽

Environmental Parameters ◽

System Level ◽

Environmental Salinity ◽

Differential Interaction ◽

Differential Network ◽

Proteomic Dataset ◽

Two Populations

ABSTRACTUsing abundance measurements of 1,490 proteins from four separate populations of three-spined sticklebacks, we implemented a system-level approach to correlate proteome dynamics with environmental salinity and temperature and the fish’s population and morphotype. We identified sets of robust and accurate fingerprints that predict environmental salinity, temperature, morphotype and the population sample origin, observing that proteins with specific functions are enriched in these fingerprints. Highly apparent functions represented in all fingerprints include ion transport, proteostasis, growth, and immunity, suggesting that these functions are most diversified in populations inhabiting different environments.Applying a differential network approach, we analyzed the network of protein interactions that differs between populations. Looking at specific population combinations of differential interaction, we identify sets of connected proteins. We find that these sets and their corresponding enriched functions reflect key processes that have diverged between the four populations. Moreover, the extent of divergence, i.e. the number of enriched functions that differ between populations, is highest when all three environmental parameters are different between two populations. Key nodes in the differential interaction network signify functions that are also inherent in the fingerprints, most prominently proteostasis-related functions. However, the differential interaction network also reveals additional functions that have diverged between populations, notably cytoskeletal organization and morphogenesis.Having such a large proteomic dataset, the strength of these analyses is that the results are purely data-driven, not based on previous findings and hypotheses about adaptation. With such an unbiased approach applied on a large proteomic dataset, we find the strongest signals given by the data, making it possible to develop more discriminatory and complex biomarkers for specific contexts of interest.

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Identification of key proteins involved in stickleback environmental adaptation with system-level analysis

Physiological Genomics ◽

10.1152/physiolgenomics.00078.2020 ◽

2020 ◽

Vol 52 (11) ◽

pp. 531-548

Author(s):

Martina Hall ◽

Dietmar Kültz ◽

Eivind Almaas

Keyword(s):

Protein Interactions ◽

Population Sample ◽

Interaction Network ◽

Environmental Parameters ◽

System Level ◽

Data Set ◽

Environmental Salinity ◽

Differential Interaction ◽

Proteomic Data ◽

Differential Network

Using abundance measurements of 1,490 proteins from four separate populations of three-spined sticklebacks, we implemented a system-level approach to correlate proteome dynamics with environmental salinity and temperature and the fish's population and morphotype. We identified robust and accurate fingerprints that classify environmental salinity, temperature, morphotype, and the population sample origin, observing that proteins with specific functions are enriched in these fingerprints. Highly apparent functions represented in all fingerprints include ion transport, proteostasis, growth, and immunity, suggesting that these functions are most diversified in populations inhabiting different environments. Applying a differential network approach, we analyzed the network of protein interactions that differs between populations. Looking at specific population combinations of differential interaction, we identify sets of connected proteins. We find that these sets and their corresponding enriched functions reflect key processes that have diverged between the four populations. Moreover, the extent of divergence, i.e., the number of enriched functions that differ between populations, is highest when all three environmental parameters are different between two populations. Key nodes in the differential interaction network signify functions that are also inherent in the fingerprints, most prominently proteostasis-related functions. However, the differential interaction network also reveals additional functions that have diverged between populations, notably cytoskeletal organization and morphogenesis. The strength of these analyses is that the results are purely data driven. With such an unbiased approach applied on a large proteomic data set, we find the strongest signals given by the data, making it possible to develop more discriminatory and complex biomarkers for specific contexts of interest.

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Disparity-filtered differential correlation network analysis: a case study on CRC metabolomics

Journal of Integrative Bioinformatics ◽

10.1515/jib-2021-0030 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Silvia Sabatini ◽

Amalia Gastaldelli

Keyword(s):

Network Analysis ◽

Null Model ◽

Great Accuracy ◽

Classification Model ◽

Interaction Patterns ◽

Metabolomics Data ◽

Network Reduction ◽

Differential Network Analysis ◽

Differential Correlation ◽

Differential Network

Abstract Differential network analysis has become a widely used technique to investigate changes of interactions among different conditions. Although the relationship between observed interactions and biochemical mechanisms is hard to establish, differential network analysis can provide useful insights about dysregulated pathways and candidate biomarkers. The available methods to detect differential interactions are heterogeneous and often rely on assumptions that are unrealistic in many applications. To address these issues, we develop a novel method for differential network analysis, using the so-called disparity filter as network reduction technique. In addition, we propose a classification model based on the inferred network interactions. The main novelty of this work lies in its ability to preserve connections that are statistically significant with respect to a null model without favouring any resolution scale, as a hard threshold would do, and without Gaussian assumptions. The method was tested using a published metabolomic dataset on colorectal cancer (CRC). Detected hub metabolites were consistent with recent literature and the classifier was able to distinguish CRC from polyp and healthy subjects with great accuracy. In conclusion, the proposed method provides a new simple and effective framework for the identification of differential interaction patterns and improves the biological interpretation of metabolomics data.

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In Silico Protein Interaction Network Analysis of Virulence Proteins Associated with Invasive Aspergillosis for Drug Discovery

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666181120150633 ◽

2019 ◽

Vol 19 (2) ◽

pp. 146-155 ◽

Cited By ~ 1

Author(s):

Renu Chaudhary ◽

Meenakshi Balhara ◽

Deepak Kumar Jangir ◽

Mehak Dangi ◽

Mrridula Dangi ◽

...

Keyword(s):

Network Analysis ◽

Invasive Aspergillosis ◽

Protein Interaction ◽

Drug Target ◽

Interaction Network ◽

Enrichment Analysis ◽

Homology Model ◽

Ppi Network ◽

Virulence Proteins ◽

Hub Proteins

<P>Background: Protein-Protein interaction (PPI) network analysis of virulence proteins of Aspergillus fumigatus is a prevailing strategy to understand the mechanism behind the virulence of A. fumigatus. The identification of major hub proteins and targeting the hub protein as a new antifungal drug target will help in treating the invasive aspergillosis. </P><P> Materials & Method: In the present study, the PPI network of 96 virulence (drug target) proteins of A. fumigatus were investigated which resulted in 103 nodes and 430 edges. Topological enrichment analysis of the PPI network was also carried out by using STRING database and Network analyzer a cytoscape plugin app. The key enriched KEGG pathway and protein domains were analyzed by STRING.Conclusion:Manual curation of PPI data identified three proteins (PyrABCN-43, AroM-34, and Glt1- 34) of A. fumigatus possessing the highest interacting partners. Top 10% hub proteins were also identified from the network using cytohubba on the basis of seven algorithms, i.e. betweenness, radiality, closeness, degree, bottleneck, MCC and EPC. Homology model and the active pocket of top three hub proteins were also predicted.</P>

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