The Eminence of Co-Expressed Ties in Schizophrenia Network Communities
Abstract
:1. Introduction
2. Related Work
2.1. Network Approach for Disease Modeling
2.2. Prominence of Community Detection in Biological Networks
2.3. Tie Structure Analysis
3. Methods
3.1. Collecting Gene Data
3.2. Identifying Functional Modules and Creating the Gene Network
3.3. Categorizing the Gene Components
3.4. Modularity Based Community Detection
3.5. Optimizing Modularity Using Nonlinear Embedding
3.6. Implementing LTSACom for Community Detection
3.7. Validation of Gene Communities
3.8. Discovering the Tie Structure from Communities
3.9. Multiple Correspondence Analysis
4. Results
4.1. Description of the Gene Dataset
4.2. Supervised LDA for Topic Modeling
4.3. Modularity-Based Community Detection
Algorithm 1 LTSACom for community detection | |
Input | Input the modularity matrix M derived from schizophrenia gene dataset for detection of gene communities |
Step 1 | Compute the nearest neighbors using the local information among genes in tangent space |
Step 2 | Construct the unweighted alignment matrix A based on the embedded vectors in the matrix M |
Step 3 | Global optimization of A based on local tangents using eigenvector decomposition |
Output | Compute the modularity index for the dataset to identify gene communities |
4.4. Validating the Community Structure
4.5. Performance Analysis of LTSACom
4.6. Identifying Tie Structure from Gene Communities
4.7. Multiple Correspondence Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Sl. No. | Study | Methodology Adopted | Significant Contributions |
---|---|---|---|
1 | [24] | Disrupted brain function is modeled using non-linear models like gaussian naïve bayes and SVM (Support Vector Machine) | Non-linear models resulted in better functional brain mapping with 86% accuracy |
2 | [25] | Functional connectivity between brain components are investigated | Network topology derived from fMRI (functional Magnetic Resonance Imaging) indicated robustness of schizophrenia brain connectivity with minimal hub regions |
3 | [26] | Neurodevelopmental model is developed for detecting schizophrenia using longitudinal population studies | Postmortem gene expression and brain imaging denotes phenotypic characterization with developmental risk |
4 | [14] | Disruption in brain patterns are detected from functional networks using fMRI experiment | Disruptions are found to be global in nature owing to long distance correlations with 93% classification accuracy differentiating schizophrenia and control samples |
5 | [27] | Triple brain network model is proposed based on central executive network (CEN) and default mode to detect aberrant behavior in schizophrenia | The unitary mechanism of the disease is identified in cognitive, negative and positive domains |
6 | [28] | Several drugs including dopaminergic, cholinergic, glutamatergic, GABA (Gamma-Aminobutyric Acid), kappa opioid, cannabinoid and serotonergic are evaluated to understand their interaction patterns in schizophrenia | The stimulants impacting progression of schizophrenia are identified from the drug models |
7 | [12] | Multiple alterations in brain disorders are identified using a network model | The network model detected the positive symptoms of diseases using integrated approach from social, biological and psychological factors. |
8 | [29] | Predictive model is developed based on functional network patterns to detect schizophrenia | Sparse multivariate regression model applied on whole-brain functionality resulted in 74% accuracy for predicting schizophrenia |
9 | [30] | Magnetic resonance imaging data is utilized for mapping differences in brain structure | Overlapping regions of 2% is observed in cerebral, frontal and temporal regions. |
10 | [31] | Differentially expressed schizophrenia transcripts are identified using dysregulated genes | Two markers RGS1 and CCL4 are identified with 97% accuracy from 27% of patient subset |
Sl. No. | Study | Algorithm/Methodology Adopted for Community Detection | Contributions/Future Scope |
---|---|---|---|
1 | [32] | Overlapping communties are detected based on modularity metric in complex networks | Non-trivial interactions are detected across protein and word graphs |
2 | [33] | Hierarchy structure is inferred from biochemical networks to identify topological properties with enhanced accuracy | Several network phenomenas are identified by considering hierarchy as the central dogma for network orientation |
3 | [34] | Several community detection algorithms are designed for network visualization and clustering | GLay platform is found to be suitable for analysis of large biological networks using community detection |
4 | [35] | Differential evolution algorithm is designed for detecting network communities using modularity as fitness function | The algorithm does not require prior knowledge of the network structure, hence outperforms other community detection algorithms on real world datasets |
5 | [36] | Generative model is developed from undirected graphs to detect network communities | Community structure helps in detecting functional connections and dynamics for uncovering biological mechanisms |
6 | [37] | Modularity-based community detection agorithms are compared on benchmark networks | Protein interaction networks are investigated to identify multiple biological modules and hierarchical organization |
7 | [38] | Community detection is performed for detecting modularity in multiplex networks based on recursive clustering | Randomization improves the quality of community detection based on threshold p-value for disease dataset |
8 | [39] | Biologically relevant modules are deteted as non-overlapping communities using modularity and conductance metrics | It is noted that overlapping community detection algorithms are preferred for identification of diseases modules |
Sl. No. | Study | Methodology Adopted | Significant Conclusion |
---|---|---|---|
1 | [21] | The cohesive power of weak ties helps in analyzing connected components | Emphasis on weak ties reveals the connectivity and topological features in the network |
2 | [40] | Communication patterns are observed in mobile networks based on strength of interaction across the ties | Information diffusion revealed neither strong nor weak ties are effective |
3 | [41] | Importance of ties is explored and a recommender system is developed using probability-based matrix factorization algorithm | Different types of social network ties are classified using the recommendation system with enhanced accuracy and specificity |
4 | [42] | The word of mouth implication is demonstrated using agent-based modeling approach | Strong ties are found to disseminate information accurately compared to weak ties |
5 | [43] | Strongly connected components are analyzed based on the local bow-tie structures in world wide web graphs | Differences were observed between WWW graphs and other graphs due to global bow-tie associations |
Sl. No | Gene Modules | Number of Genes |
---|---|---|
1. | Inflammation | 73 |
2. | Immune response | 213 |
3. | Genetic factors | 674 |
4. | Neurotransmitters | 149 |
5. | Metabolites | 168 |
6. | Stress Inducers | 36 |
Trials | Modularity | SP | FUA | MABA | FN | FEC | InfoMap | LTSACom |
---|---|---|---|---|---|---|---|---|
20 | 0.1 | 0.6784 | 0.7622 | 0.7981 | 0.8021 | 0.7123 | 0.7892 | 0.9194 |
40 | 0.2 | 0.6755 | 0.7544 | 0.8083 | 0.8166 | 0.7851 | 0.7985 | 0.8996 |
60 | 0.3 | 0.7081 | 0.765 | 0.8348 | 0.7933 | 0.8037 | 0.7831 | 0.8631 |
80 | 0.4 | 0.7002 | 0.7598 | 0.8329 | 0.7821 | 0.8136 | 0.8043 | 0.9386 |
100 | 0.5 | 0.6923 | 0.7322 | 0.8071 | 0.8031 | 0.8203 | 0.8124 | 0.8931 |
120 | 0.6 | 0.7093 | 0.7999 | 0.8199 | 0.7932 | 0.8108 | 0.8342 | 0.9032 |
140 | 0.7 | 0.7129 | 0.7132 | 0.8478 | 0.8155 | 0.8093 | 0.8188 | 0.9203 |
160 | 0.8 | 0.7361 | 0.6992 | 0.8332 | 0.7973 | 0.8478 | 0.8109 | 0.9201 |
180 | 0.9 | 0.7102 | 0.7338 | 0.8554 | 0.8045 | 0.8313 | 0.8013 | 0.9289 |
200 | 1.0 | 0.7009 | 0.7221 | 0.8441 | 0.7899 | 0.8396 | 0.8032 | 0.9256 |
Sl. No. | Coexpressed Gene Ties | Gene Names | Categories They Belong to |
---|---|---|---|
1. | Akt1 | Protein kinase B | Immune response, Genetic factors, Neurotransmitters, Metabolites |
2. | DRD2 | Dopamine Receptor 2 | Genetic factors, Neurotransmitters, Metabolites, Stress inducers |
3. | IL10 | Interleukin 10 | Inflammatory, Immune response, Genetic factors, Metabolites |
4. | PPP3CC | Protein Phosphatase 3 Catalytic Subunit Gamma | Immune response, Genetic factors, Neurotransmitters, Metabolites |
5. | PLA2G4A | Phospholipase A2 Group 4A | Inflammatory, Immune response, Genetic factors, Neurotransmitters, |
6. | GSK3B | Glycogen synthase kinase-3B | Immune response, Genetic factors, Neurotransmitter, Metabolites |
7. | NO | Nitric oxide | Inflammatory, Immune response, Neurotransmitter, Stress inducer |
8. | COX2 | Cyclooxygenase 2 | Inflammatory, Immune response, Genetic factor, Neurotransmitter |
9. | FKBP5 | FK506 Binding Protein 5 | Immune response, Genetic factors, Stress inducers |
10. | IL1B | Interleukin 1 beta | Inflammatory, Immune response, Metabolite |
11. | COMT | Catechol-O-methyltransferase | Genetic factors, Neurotransmitter, Stress inducer |
12. | DISC1 | Disrupted in Schizophrenia 1 | Genetic factor, Metabolite, Stress inducer |
13. | PDE4B | Phosphodiesterase 4B | Immune response, Genetic factor, Metabolite |
14. | PNPO | Pyridoxine 5’-phosphatase oxidase | Genetic factor, Metabolite, Stress inducer |
15. | NFAT4 | Nuclear Factor of Activated T Cells 4 | Inflammatory, Immune response, Genetic factor |
16. | IFNG | Interferon gamma | Inflammatory, Immune response, Genetic factor |
17. | TNFA | Tumor Necrosis Factor Alpha | Inflammatory, Immune response, Genetic factor |
18. | IL3RA | Interleukin 3 Receptor Subunit Alpha | Inflammatory, Immune response, Genetic factor |
19. | IL4 | Interleukin 4 | Inflammatory, Immune response, Genetic factor |
20. | GRIK3 | Glutamate Ionotropic Receptor Kainate Type Subunit 3 | Genetic factor, Neurotransmitter, Metabolite |
21. | VEGF | Vascular endothelial growth factor | Inflammatory, Genetic factor, Metabolite |
22. | HTR4 | 5-Hydroxytryptamine Receptor 4 | Genetic factor, Neurotransmitter, Metabolite |
23. | GNAS | Guanine nucleotide binding protein, alpha stimulating | Immune response, Genetic factor, Neurotransmitter |
24. | CHRNA7 | Cholinergic Receptor Nicotinic Alpha 7 | Genetic factor, Neurotransmitter, Metabolite |
25. | DTNBP1 | Dysbindin 1 | Genetic factor, Neurotransmitter, Metabolite |
26. | RELN | Reelin | Genetic factor, Neurotransmitter, Metabolite |
27. | DRD1 | Dopamine Receptor D1 | Genetic factor, Neurotransmitter, Metabolite |
28. | DRD3 | Dopamine Receptor D3 | Genetic factor, Neurotransmitter, Metabolite |
29. | DRD5 | Dopamine Receptor D5 | Genetic factor, Neurotransmitter, Metabolite |
30. | ERBB4 | Epidermal growth factor receptor family 4 | Genetic factor, Neurotransmitter, Metabolite |
31. | GRIA2 | Glutamate Ionotropic Receptor AMPA (Alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) Type Subunit 2 | Genetic factor, Neurotransmitter |
32. | HSPA1A | Heat Shock Protein Family A Member 1A | Immune response, Genetic factor |
33. | RASD2 | RASD Family Member 2 | Genetic factor, Metabolite |
34. | IDO | Indoleamine 2,3-dioxygenase | Immune response, Metabolite |
35. | PGE2 | Prostaglandin E2 | Inflammatory, Immune response |
36. | KYN | Kynurenine | Inflammatory, Immune response |
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Sridhar, A.; GS, S.; Reddy, A.M.; Bhattacharjee, B.; Nagaraj, K. The Eminence of Co-Expressed Ties in Schizophrenia Network Communities. Data 2019, 4, 149. https://doi.org/10.3390/data4040149
Sridhar A, GS S, Reddy AM, Bhattacharjee B, Nagaraj K. The Eminence of Co-Expressed Ties in Schizophrenia Network Communities. Data. 2019; 4(4):149. https://doi.org/10.3390/data4040149
Chicago/Turabian StyleSridhar, Amulyashree, Sharvani GS, AH Manjunatha Reddy, Biplab Bhattacharjee, and Kalyan Nagaraj. 2019. "The Eminence of Co-Expressed Ties in Schizophrenia Network Communities" Data 4, no. 4: 149. https://doi.org/10.3390/data4040149