scholarly journals A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification

GigaScience ◽  
2019 ◽  
Vol 8 (5) ◽  
Author(s):  
Ren-Hua Chung ◽  
Chen-Yu Kang
Keyword(s):  
Data Analysis ◽  
Complex Disease ◽  
Disease Classification ◽  
Omics Data ◽  
Analysis Methods ◽  
Data Analysis Methods ◽  
Omics Data Analysis

scholarly journals A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification

2018 ◽  
Author(s):  
Ren-Hua Chung ◽  
Chen-Yu Kang
Keyword(s):  
Gene Expression ◽  
Complex Disease ◽  
Disease Model ◽  
Disease Status ◽  
Copy Number Variations ◽  
Disease Classification ◽  
Whole Genome ◽  
Omics Data ◽  
Analysis Methods ◽  
Omics Analysis

AbstractAn integrative multi-omics analysis approach that combines multiple types of omics data including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics, has become increasing popular for understanding the pathophysiology of complex diseases. Although many multi-omics analysis methods have been developed for complex disease studies, there is no simulation tool that simulates multiple types of omics data and models their relationships with disease status. Without such a tool, it is difficult to evaluate the multi-omics analysis methods on the same scale and to estimate the sample size or power when planning a new multi-omics disease study. We developed a multi-omics data simulator OmicsSIMLA, which simulates genomics (i.e., SNPs and copy number variations), epigenomics (i.e., whole-genome bisulphite sequencing), transcriptomics (i.e., RNA-seq), and proteomics (i.e., normalized reverse phase protein array) data at the whole-genome level. Furthermore, the relationships between different types of omics data, such as meQTLs (SNPs influencing methylation), eQTLs (SNPs influencing gene expression), and eQTM (methylation influencing gene expression), were modeled. More importantly, the relationships between these multi-omics data and the disease status were modeled as well. We used OmicsSIMLA to simulate a multi-omics dataset for breast cancer under a hypothetical disease model, and used the data to compare the performance among existing multi-omics analysis methods in terms of disease classification accuracy and run time. Our results demonstrated that complex disease mechanisms can be simulated by OmicsSIMLA, and a random forest-based method showed the highest prediction accuracy when the multi-omics data were properly normalized.

Data analysis methods for defining biomarkers from omics data

2021 ◽  
Author(s):  
Chao Li ◽  
Zhenbo Gao ◽  
Benzhe Su ◽  
Guowang Xu ◽  
Xiaohui Lin
Keyword(s):  
Data Analysis ◽  
Omics Data ◽  
Analysis Methods ◽  
Data Analysis Methods

scholarly journals Objective assessment of SERS thin films: comparison of silver on copper via galvanic displacement with commercially available fabricated substrates

2017 ◽  
Vol 9 (33) ◽  
pp. 4783-4789 ◽  
Author(s):  
Samuel Mabbott ◽  
Yun Xu ◽  
Royston Goodacre
Keyword(s):  
Thin Films ◽  
Data Analysis ◽  
Objective Assessment ◽  
Galvanic Displacement ◽  
Sers Signal ◽  
Analysis Methods ◽  
Data Analysis Methods

Reproducibility of SERS signal acquired from thin films developed in-house and commercially has been assessed using seven data analysis methods.

Using machine learning approaches for multi-omics data analysis: A review

2021 ◽  
Vol 49 ◽  
pp. 107739
Author(s):  
Parminder S. Reel ◽  
Smarti Reel ◽  
Ewan Pearson ◽  
Emanuele Trucco ◽  
Emily Jefferson
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Omics Data ◽  
Learning Approaches ◽  
Omics Data Analysis

Omaha System Public Health Data: Analysis Methods

2010 ◽  
Vol 58 (2) ◽  
pp. e22-e23
Author(s):  
Karen A. Monsen ◽  
Karen S. Martin ◽  
Bonnie L Westra
Keyword(s):  
Public Health ◽  
Data Analysis ◽  
Health Data ◽  
Public Health Data ◽  
Analysis Methods ◽  
Omaha System ◽  
Data Analysis Methods
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