Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources
As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Metabolites |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-1989/10/5/202 |
| _version_ | 1797567906030026752 |
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| author | Tara Eicher Garrett Kinnebrew Andrew Patt Kyle Spencer Kevin Ying Qin Ma Raghu Machiraju Ewy A. Mathé |
| author_facet | Tara Eicher Garrett Kinnebrew Andrew Patt Kyle Spencer Kevin Ying Qin Ma Raghu Machiraju Ewy A. Mathé |
| author_sort | Tara Eicher |
| collection | DOAJ |
| description | As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study. |
| first_indexed | 2024-03-10T19:47:46Z |
| format | Article |
| id | doaj.art-97b385f195464012ae836a195ad60ee1 |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-10T19:47:46Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-97b385f195464012ae836a195ad60ee12023-11-20T00:37:55ZengMDPI AGMetabolites2218-19892020-05-0110520210.3390/metabo10050202Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and ResourcesTara Eicher0Garrett Kinnebrew1Andrew Patt2Kyle Spencer3Kevin Ying4Qin Ma5Raghu Machiraju6Ewy A. Mathé7Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USABiomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USADivision of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USABiomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USAComprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USABiomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USABiomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USABiomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USAAs researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.https://www.mdpi.com/2218-1989/10/5/202multi-omics integrationdimensionality reductionco-regulationpathway enrichmentclusteringmachine learning |
| spellingShingle | Tara Eicher Garrett Kinnebrew Andrew Patt Kyle Spencer Kevin Ying Qin Ma Raghu Machiraju Ewy A. Mathé Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources Metabolites multi-omics integration dimensionality reduction co-regulation pathway enrichment clustering machine learning |
| title | Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources |
| title_full | Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources |
| title_fullStr | Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources |
| title_full_unstemmed | Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources |
| title_short | Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources |
| title_sort | metabolomics and multi omics integration a survey of computational methods and resources |
| topic | multi-omics integration dimensionality reduction co-regulation pathway enrichment clustering machine learning |
| url | https://www.mdpi.com/2218-1989/10/5/202 |
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