Complex Disease Individual Molecular Characterization Using Infinite Sparse Graphical Independent Component Analysis

Rincourt, Sarah-Laure; Michiels, Stefan; Drubay, Damien

doi:10.1177/11769351221105776

Cited by 3 publications

(1 citation statement)

References 28 publications

(47 reference statements)

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“…18 The tuning hyperparameters were calibrated by parallelizable Bayesian optimization based on 7 initialization evaluations and 30 epochs (50 epochs for Cluster 2), using the R package “ParBayesianOptimization”. 36,37 We ran training XGboost models with 3000 rounds at first, then the optimal number of rounds ( n ) was selected by mean-squared error (MSE) as the following equation: …”

Section: Methodsmentioning

confidence: 99%

Exposome approaches to assessing the association between urban land use environment and depressive symptoms in young adulthood: a FinnTwin12 cohort study

Wang

Whipp

Heinonen-Guzejev

et al. 2023

Preprint

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Background: Depressive symptoms lead to a serious public health burden and are considerably affected by the environment. Land use, describing the urban living environment, has an impact on mental health, but complex relationship assessment is rare. Objectives: We aimed to examine the complicated association between urban land use and depressive symptoms among young adults with differential land use environments, by applying multiple models, as an exposome study. Methods: We included 1804 individual twins from the FinnTwin12 cohort, living in urban areas in 2012. There were 8 types of land use exposures in 3 buffer radii. The depressive symptoms were assessed through General Behavior Inventory (GBI) in young adulthood (mean age: 24.1). First, K-means clustering was performed to distinguish participants with differential land use environments. Then, linear elastic net penalized regression and eXtreme Gradient Boosting (XGBoost) were used to reduce dimensions or prioritize for importance and examine the linear and nonlinear relationships. Results: Two clusters were identified with notable differences in the percentage of high-density residential, low-density residential, and natural land use. One is more typical of city centers, and another of suburban areas. A heterogeneous pattern in results was detected from the linear elastic net penalized regression model among the overall sample and the two separated clusters. Agricultural residential land use in a 100 m buffer contributed to GBI most (coefficient: 0.097) in the "suburban" cluster among 11 selected exposures. In the "city center" cluster, none of the land use exposures was associated with GBI. From the XGBoost models, we observed that ranks of the importance of land use exposures on GBI and their nonlinear relationships are also heterogeneous in the two clusters. Discussion: As a hypothesis-generating study, we found heterogeneous linear and nonlinear relationships between urban land use environment and depressive symptoms under different contexts in pluralistic exposome analyses.

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Section: Methodsmentioning

confidence: 99%

Exposome approaches to assessing the association between urban land use environment and depressive symptoms in young adulthood: a FinnTwin12 cohort study

Wang

Whipp

Heinonen-Guzejev

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

The association between urban land use and depressive symptoms in young adulthood: a FinnTwin12 cohort study

Wang,

Whipp,

Heinonen-Guzejev

et al. 2023

J Expo Sci Environ Epidemiol

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Background Depressive symptoms lead to a serious public health burden and are considerably affected by the environment. Land use, describing the urban living environment, influences mental health, but complex relationship assessment is rare. Objective We aimed to examine the complicated association between urban land use and depressive symptoms among young adults with differential land use environments, by applying multiple models. Methods We included 1804 individual twins from the FinnTwin12 cohort, living in urban areas in 2012. There were eight types of land use exposures in three buffer radii. The depressive symptoms were assessed through the General Behavior Inventory (GBI) in young adulthood (mean age: 24.1). First, K-means clustering was performed to distinguish participants with differential land use environments. Then, linear elastic net penalized regression and eXtreme Gradient Boosting (XGBoost) were used to reduce dimensions or prioritize for importance and examine the linear and nonlinear relationships. Results Two clusters were identified: one is more typical of city centers and another of suburban areas. A heterogeneous pattern in results was detected from the linear elastic net penalized regression model among the overall sample and the two separated clusters. Agricultural residential land use in a 100 m buffer contributed to GBI most (coefficient: 0.097) in the “suburban” cluster among 11 selected exposures after adjustment with demographic covariates. In the “city center” cluster, none of the land use exposures was associated with GBI, even after further adjustment with social indicators. From the XGBoost models, we observed that ranks of the importance of land use exposures on GBI and their nonlinear relationships are also heterogeneous in the two clusters. Impact This study examined the complex relationship between urban land use and depressive symptoms among young adults in Finland. Based on the FinnTwin12 cohort, two distinct clusters of participants were identified with different urban land use environments at first. We then employed two pluralistic models, elastic net penalized regression and XGBoost, and revealed both linear and nonlinear relationships between urban land use and depressive symptoms, which also varied in the two clusters. The findings suggest that analyses, involving land use and the broader environmental profile, should consider aspects such as population heterogeneity and linearity for comprehensive assessment in the future.

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A non-parametric Bayesian joint model for latent individual molecular profiles and survival in oncology

Rincourt¹,

Michiels²,

Drubay³

2022

J. Bioinform. Comput. Biol.

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The development of prognostic molecular signatures considering the inter-patient heterogeneity is a key challenge for the precision medicine. We propose a joint model of this heterogeneity and the patient survival, assuming that tumor expression results from a mixture of a subset of independent signatures. We deconvolute the omics data using a non-parametric independent component analysis with a double sparseness structure for the source and the weight matrices, corresponding to the gene-component and individual-component associations, respectively. In a simulation study, our approach identified the correct number of components and reconstructed with high accuracy the weight ([Formula: see text]0.85) and the source ([Formula: see text]0.75) matrices sparseness. The selection rate of components with high-to-moderate prognostic impacts was close to 95%, while the weak impacts were selected with a frequency close to the observed false positive rate ([Formula: see text]25%). When applied to the expression of 1063 genes from 614 breast cancer patients, our model identified 15 components, including six associated to patient survival, and related to three known prognostic pathways in early breast cancer (i.e. immune system, proliferation, and stromal invasion). The proposed algorithm provides a new insight into the individual molecular heterogeneity that is associated with patient prognosis to better understand the complex tumor mechanisms.

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Complex Disease Individual Molecular Characterization Using Infinite Sparse Graphical Independent Component Analysis

Cited by 3 publications

References 28 publications

Exposome approaches to assessing the association between urban land use environment and depressive symptoms in young adulthood: a FinnTwin12 cohort study

Exposome approaches to assessing the association between urban land use environment and depressive symptoms in young adulthood: a FinnTwin12 cohort study

The association between urban land use and depressive symptoms in young adulthood: a FinnTwin12 cohort study

A non-parametric Bayesian joint model for latent individual molecular profiles and survival in oncology

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