Dataset Information

I3: A Self-organising Learning Workflow for Intuitive Integrative Interpretation of Complex Genetic Data.

ABSTRACT: We propose a computational workflow (I3) for intuitive integrative interpretation of complex genetic data mainly building on the self-organising principle. We illustrate the use in interpreting genetics of gene expression and understanding genetic regulators of protein phenotypes, particularly in conjunction with information from human population genetics and/or evolutionary history of human genes. We reveal that loss-of-function intolerant genes tend to be depleted of tissue-sharing genetics of gene expression in brains, and if highly expressed, have broad effects on the protein phenotypes studied. We suggest that this workflow presents a general solution to the challenge of complex genetic data interpretation. I3 is available at http://suprahex.r-forge.r-project.org/I3.html.

SUBMITTER: Tan Y

PROVIDER: S-EPMC7056857 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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I3: A Self-organising Learning Workflow for Intuitive Integrative Interpretation of Complex Genetic Data.

Tan Yun Y Jiang Lulu L Wang Kankan K Fang Hai H

Genomics, proteomics & bioinformatics 20191001 5

We propose a computational workflow (I3) for intuitive integrative interpretation of complex genetic data mainly building on the self-organising principle. We illustrate the use in interpreting genetics of gene expression and understanding genetic regulators of protein phenotypes, particularly in conjunction with information from human population genetics and/or evolutionary history of human genes. We reveal that loss-of-function intolerant genes tend to be depleted of tissue-sharing genetics of ...[more]

PMID: 31765831

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Project description:Endotherms such as rats and mice huddle together to keep warm. The huddle is considered to be an example of a self-organising system, because complex properties of the collective group behaviour are thought to emerge spontaneously through simple interactions between individuals. Groups of rodent pups display two such emergent properties. First, huddling undergoes a 'phase transition', such that pups start to aggregate rapidly as the temperature of the environment falls below a critical temperature. Second, the huddle maintains a constant 'pup flow', where cooler pups at the periphery continually displace warmer pups at the centre. We set out to test whether these complex group behaviours can emerge spontaneously from local interactions between individuals. We designed a model using a minimal set of assumptions about how individual pups interact, by simply turning towards heat sources, and show in computer simulations that the model reproduces the first emergent property--the phase transition. However, this minimal model tends to produce an unnatural behaviour where several smaller aggregates emerge rather than one large huddle. We found that an extension of the minimal model to include heat exchange between pups allows the group to maintain one large huddle but eradicates the phase transition, whereas inclusion of an additional homeostatic term recovers the phase transition for large huddles. As an unanticipated consequence, the extended model also naturally gave rise to the second observed emergent property--a continuous pup flow. The model therefore serves as a minimal description of huddling as a self-organising system, and as an existence proof that group-level huddling dynamics emerge spontaneously through simple interactions between individuals. We derive a specific testable prediction: Increasing the capacity of the individual to generate or conserve heat will increase the range of ambient temperatures over which adaptive thermoregulatory huddling will emerge.

Project description:BACKGROUND:EQ-5D is generic measure of health-related quality of life. Studies using EQ-5D generate ordinal data that are interpreted as categories ordered by severity. New analytic approaches taking into account the ordinal nature of the health dimension severity and leading to a better interpretation of EQ-5D data are needed to better elucidate differences in health-related quality of life. We propose utilizing the Improved RIDIT statistical method to analyze EQ-5D outcomes. METHODS:556 Moroccan participants aged over 18 years representing four chronic diseases: back pain (n = 158), renal insufficiency (n = 56), diabetes (n = 82) or hypertension (n = 80) and healthy subjects (n = 180). All participants received the two EQ-5D versions. Two other published data sets were included. The first was extracted from a diabetic Spain study and the second was extracted from a clinical trial study. The Improved RIDIT analyses were carried out using an R statistic program we developed. RESULTS:Applying the Improved RIDIT on the EQ-5D data allowed estimating for the first time the ordinal odds, the Absolute Risk Reduction (ARR) or the Absolute Risk Increase (ARI) and the Number Needed to Treat. The ARI values estimated for Moroccan patients showed that (i) hypertension increased anxiety/depression by 66% and reduced mobility by 65%; (ii) back pain increased pain/discomfort by 69%; (iii) renal insufficiency impacts mobility (ARI = 57%, oddsordinal = 9.95) and usual activities (ARI = 44%, oddsordinal = 6.41) and (iv) diabetes acts only on anxiety/depression (ARI = 50%, oddsordinal = 4.8). Also, we demonstrated that the approach works well in clinical trials. CONCLUSIONS:Improved RIDIT provides more intuitive and informative interpretation of the EQ-5D data by (1) taking into account the level severity; estimating (2) the odds ordinal, (3) the ARR/ARI and the NNT; (4) analyzing the five dimensions of the EQ-5D separately, which gives clinical teams more precision in understanding the treatment/pathology impacts on the health status and completes the EQ-5D data analysis based on score utilities.

Dataset Information

I3: A Self-organising Learning Workflow for Intuitive Integrative Interpretation of Complex Genetic Data.

Publications

I3: A Self-organising Learning Workflow for Intuitive Integrative Interpretation of Complex Genetic Data.

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