InterSIM: Simulation tool for multiple integrative ‘omic datasets’

Chalise, Prabhakar; Rama, Raghavan; Fridley, Brooke L.

doi:10.1016/j.cmpb.2016.02.011

Cited by 25 publications

(15 citation statements)

References 11 publications

(10 reference statements)

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“…3a). We simulated these datasets using the InterSIM CRAN package 24 . This package generates three omics datasets with imposed reference clustering.…”

Section: Resultsmentioning

confidence: 99%

Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer

et al. 2021

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High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve proper integration, joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehensive benchmark with practical guidelines. We perform a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluate their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we use TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assess their classification of multi-omics single-cell data. From these in-depth comparisons, we observe that intNMF performs best in clustering, while MCIA offers an effective behavior across many contexts. The code developed for this benchmark study is implemented in a Jupyter notebook—multi-omics mix (momix)—to foster reproducibility, and support users and future developers.

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“…3a). We simulated these datasets using the InterSIM CRAN package 24 . This package generates three omics datasets with imposed reference clustering.…”

Section: Resultsmentioning

confidence: 99%

Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer

et al. 2021

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“…We first evaluated the jDR approaches on artificial multi-omics datasets. We simulated these datasets using the InterSIM CRAN package 24 . This package generates three artificial omics datasets with imposed reference clustering by manipulating TCGA ovarian cancer data.…”

Section: ) Benchmarking Joint Dimensionality Reduction Approaches Onmentioning

confidence: 99%

“…The simulated multi-omics datasets have been produced by the InterSIM 24 CRAN package. InterSIM simulates multiple interrelated data types with realistic intra-and inter-relationships based on the DNA methylation, mRNA gene expression, and protein expression from TCGA ovarian cancer data.…”

Section: Data Simulationmentioning

confidence: 99%

Benchmarking joint multi-omics dimensionality reduction approaches for cancer study

Cantini

Zakeri

Hernandez

et al. 2020

Preprint

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High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve this multi-omics data integration, Joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehensive benchmark with practical guidelines.We performed a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluated their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we used TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assessed their classification of multi-omics singlecell data.From these in-depth comparisons, we observed that intNMF performs best in clustering, while MCIA offers a consistent and effective behavior across many contexts. The full code of this benchmark is implemented in a Jupyter notebook -multi-omics mix (momix) -to foster reproducibility, and support data producers, users and future developers.

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“…Hence, HIBACHI simulates genotypes and phenotypes under the complex biological and mathematical models, but it cannot generate other types of omics data. Another tool is InterSIM [16], which simulates methylation rates and normalized gene and protein expression levels based on the ovarian cancer (OV) data from The Cancer Genome Atlas (TCGA) project [17]. The correlations within and between each data type are also modeled on the basis of the correlation structures observed in the OV data.…”

Section: Introductionmentioning

confidence: 99%

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

Chung

Kang

2019

GigaScience

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Background An 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, only a few simulation tools that simulate multiple types of omics data and model their relationships with disease status are available, and these tools have their limitations in simulating the multi-omics data. Results We developed the multi-omics data simulator OmicsSIMLA, which simulates genomics (i.e., single-nucleotide polymorphisms [SNPs] and copy number variations), epigenomics (i.e., bisulphite sequencing), transcriptomics (i.e., RNA sequencing), 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 methylation quantitative trait loci (SNPs influencing methylation), expression quantitative trait loci (SNPs influencing gene expression), and expression quantitative trait methylations (methylations 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 runtime. We also used OmicsSIMLA to simulate a multi-omics dataset with a scale similar to an ovarian cancer multi-omics dataset. The neural network–based multi-omics analysis method ATHENA was applied to both the real and simulated data and the results were compared. Our results demonstrated that complex disease mechanisms can be simulated by OmicsSIMLA, and ATHENA showed the highest prediction accuracy when the effects of multi-omics features (e.g., SNPs, copy number variations, and gene expression levels) on the disease were strong. Furthermore, similar results can be obtained from ATHENA when analyzing the simulated and real ovarian multi-omics data. Conclusions OmicsSIMLA will be useful to evaluate the performace of different multi-omics analysis methods. Sample sizes and power can also be calculated by OmicsSIMLA when planning a new multi-omics disease study.

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InterSIM: Simulation tool for multiple integrative ‘omic datasets’

Cited by 25 publications

References 11 publications

Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer

Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer

Benchmarking joint multi-omics dimensionality reduction approaches for cancer study

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

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