Horizontal and vertical integrative analysis methods for mental disorders omics data

Wang, Shuaichao; Shi, Xingjie; Wu, Mengyun; Ma, Shuangge

doi:10.1038/s41598-019-49718-5

Cited by 6 publications

(7 citation statements)

References 57 publications

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“…Significantly different from the existing studies, a new analysis strategy is proposed to borrow information across a GeO‐GGM and its corresponding GeR‐GGM, so that the estimation of the GeO‐GGM can assist the estimation of the GeR‐GGM, and vice versa. Loosely speaking, this strategy shares some similar spirit with the vertical data integration (Wang et al, 2019). This study may advance from the existing literature in the following aspects.…”

Section: Introductionmentioning

confidence: 91%

Assisted estimation of gene expression graphical models

Zhang

Sun

et al. 2021

Genetic Epidemiology

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In the study of gene expression data, network analysis has played a uniquely important role. To accommodate the high dimensionality and low sample size and generate interpretable results, regularized estimation is usually conducted in the construction of gene expression Gaussian Graphical Models (GGM).Here we use GeO-GGM to represent gene-expression-only GGM. Gene expressions are regulated by regulators. gene-expression-regulator GGMs (GeR-GGMs), which accommodate gene expressions as well as their regulators, have been constructed accordingly. In practical data analysis, with a "lack of information" caused by the large number of model parameters, limited sample size, and weak signals, the construction of both GeO-GGMs and GeR-GGMs is often unsatisfactory. In this article, we recognize that with the regulation between gene expressions and regulators, the sparsity structures of a GeO-GGM and its GeR-GGM counterpart can satisfy a hierarchy. Accordingly, we propose a joint estimation which reinforces the hierarchical structure and use the construction of a GeO-GGM to assist that of its GeR-GGM counterpart and vice versa. Consistency properties are rigorously established, and an effective computational algorithm is developed. In simulation, the assisted construction outperforms the separation construction of GeO-GGM and GeR-GGM. Two The Cancer Genome Atlas data sets are analyzed, leading to findings different from the direct competitors.

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Section: Introductionmentioning

confidence: 91%

Assisted estimation of gene expression graphical models

Zhang

Sun

et al. 2021

Genetic Epidemiology

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“…Transfer learning has been applied to problems in medical and biological studies, including predictions of protein localization (Mei et al, 2011), biological imaging diagnosis (Shin et al, 2016), drug sensitivity prediction (Turki et al, 2017) and integrative analysis of 'multi-omics' data, see, for instance, Sun and Hu (2016), Hu et al (2019), and Wang et al (2019). It has also been applied to natural language processing (Daumé III, 2007) and recommendation systems (Pan & Yang, 2013) in machine learning.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for High-Dimensional Linear Regression: Prediction, Estimation and Minimax Optimality

Cai

2021

Journal of the Royal Statistical Society Series B: Statistical Methodology

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This paper considers estimation and prediction of a highdimensional linear regression in the setting of transfer learning where, in addition to observations from the target model, auxiliary samples from different but possibly related regression models are available. When the set of informative auxiliary studies is known, an estimator and a predictor are proposed and their optimality is established. The optimal rates of convergence for prediction and estimation are faster than the corresponding rates without using the auxiliary samples. This implies that knowledge from the informative auxiliary samples can be transferred to improve the learning performance of the target problem. When the set of informative auxiliary samples is unknown, we propose a data-driven procedure for transfer learning, called Trans-Lasso, and show its robustness to non-informative auxiliary samples and its efficiency in knowledge transfer. The proposed procedures are demonstrated in numerical studies and are applied to a dataset concerning the associations among gene expressions. It is shown that Trans-Lasso leads to improved performance in gene expression prediction in a target tissue by incorporating data from multiple different tissues as auxiliary samples.

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“…It is expected that, through the combination of different types of omics data, scientists can better capture or dissect the genetic contribution in a specific disease. Some successful applications include several breast cancer studies ( Beckmann et al, 2007 ; Curtis et al, 2012 ; Zahnleiter et al, 2013 ; Hendrickx et al, 2017 ), a prostate cancer study ( Taylor et al, 2010 ), and a study of mental disorders ( Wang et al, 2019 ). Review articles about such integrated analysis have received great attention recently (see for instance Richardson et al, 2016 ; Sun and Hu, 2016 ; and Wu et al, 2019 , among many others and references therein).…”

Section: Introductionmentioning

confidence: 99%

“…As an alternative, methods that examine simultaneously both types of markers have been adopted. Such a scheme is termed as hierarchical or vertical ( Richardson et al, 2016 ; Wang et al, 2019 ; Wu et al, 2019 ). For instance, Gamazon et al (2014) suggested the use of a single SNV association test with the copy number status as a covariate to achieve better performance.…”

Section: Introductionmentioning

confidence: 99%

“…Most current integration studies analyze omics data with a vertical scheme. In a vertical integration study, different genomic profiling from the same subject is collected; while in a horizontal integration study, such as the meta-analysis of multiple GWAS datasets, the collection of the same type of omics data from different subjects in multiple studies is collected ( Richardson et al, 2016 ; Sun and Hu, 2016 ; Wang et al, 2019 ; Wu et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data

Hsiao

et al. 2021

Front. Genet.

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Genomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate the data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNVs) and copy number variations (CNVs) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNVs and CNVs compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigation.

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Horizontal and vertical integrative analysis methods for mental disorders omics data

Cited by 6 publications

References 57 publications

Assisted estimation of gene expression graphical models

Assisted estimation of gene expression graphical models

Transfer Learning for High-Dimensional Linear Regression: Prediction, Estimation and Minimax Optimality

An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data

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