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Abstract <bold>Background</bold>Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals and plants. While some microorganisms cause diseases, most of them help in biological processes such as decomposition, fermentation and nutrient cycling. Much research has been conducted on the study of microbial communities in various environments and how their interactions and relationships can provide insight into various diseases. Co-occurrence network inference algorithms help us understand the complex associations of micro-organisms, especially bacteria. Existing network inference algorithms employ techniques such as correlation, regularized linear regression, and conditional dependence, which have different hyper-parameters that determine the sparsity of the network. These complex microbial communities form intricate ecological networks that are fundamental to ecosystem functioning and host health. Understanding these networks is crucial for developing targeted interventions in both environmental and clinical settings. The emergence of high-throughput sequencing technologies has generated unprecedented amounts of microbiome data, necessitating robust computational methods for network inference and validation. <bold>Results</bold>Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples, both of which have several drawbacks that limit their applicability in real microbiome composition data sets. We propose a novel cross-validation method to evaluate co-occurrence network inference algorithms, and new methods for applying existing algorithms to predict on test data. Our method demonstrates superior performance in handling compositional data and addressing the challenges of high dimensionality and sparsity inherent in real microbiome datasets. The proposed framework also provides robust estimates of network stability. <bold>Conclusions</bold>Our empirical study shows that the proposed cross-validation method is useful for hyper-parameter selection (training) and comparing the quality of inferred networks between different algorithms (testing). This advancement represents a significant step forward in microbiome network analysis, providing researchers with a reliable tool for understanding complex microbial interactions. The method’s applicability extends beyond microbiome studies to other fields where network inference from high-dimensional compositional data is crucial, such as gene regulatory networks and ecological food webs. Our framework establishes a new standard for validation in network inference, potentially accelerating discoveries in microbial ecology and human health.
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An Organic Visual Metaphor for Public Understanding of Conditional Co-occurrences
Decisions made by domain experts, such as in healthcare and market research, are influenced by the conditional co-occurrence of different events. Learning about conditional co-occurrence is also beneficial for non-experts-the general public. By understanding the co-occurrences of diseases, it is easier to understand which diseases individuals are susceptible to. However, co-occurrence data is often complex. In order for a public understanding of conditional co-occurrence, there needs to be a simpler form to convey such complex information. We introduce an organic visual metaphor, which can provide a summary of the conditional co-occurrences within a large set of items and is accessible to the public with its organic shape. We develop a prototype application offering not only an overview for users to gain insights on how co-occurrence patterns evolve based on user-defined criteria (e.g., how do sex and age affect likelihood), but also functionality to explore the hierarchical data in-depth. We conducted two case studies with this prototype to demonstrate the effectiveness of our design.
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- Award ID(s):
- 1741536
- PAR ID:
- 10182562
- Date Published:
- Journal Name:
- IEEE SciVis 2018
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SciVis.2018.8823624
