A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets
Abstract Background Rapid advancements in high-throughput sequencing technologies allow for detailed and accurate measurement of omics features within their biological context. The integration of different omics types creates heterogeneous datasets, presenting challenges in analysis due to variation...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-08-01
|
| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-025-11925-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849226572845809664 |
|---|---|
| author | Eonyong Han Hwijun Kwon Inuk Jung |
| author_facet | Eonyong Han Hwijun Kwon Inuk Jung |
| author_sort | Eonyong Han |
| collection | DOAJ |
| description | Abstract Background Rapid advancements in high-throughput sequencing technologies allow for detailed and accurate measurement of omics features within their biological context. The integration of different omics types creates heterogeneous datasets, presenting challenges in analysis due to variations in measurement units, sample numbers, and features. Currently, there is a lack of generalized guidelines for making decisions in multi-omics study design (MOSD), such as selecting an appropriate number of samples and features, type of preprocessing and integration for robust analysis results. We propose a suggestive guideline for MOSD, involving nine important factors: sample size, feature selection, preprocessing strategy, noise characterization, class balance, number of classes, cancer subtype combination, omics combination, and clinical features. Results To assess the effectiveness of our proposed MOSD guidelines, we designed and conducted seven benchmark tests using 10 clustering methods on various TCGA cancer datasets with an objective of clustering cancer subtypes. The results indicated robust performance in terms of cancer subtype discrimination when adhering to the following criteria: 26 or more samples per class, selecting less than 10% of omics features, maintaining a sample balance under a 3:1 ratio, and keeping the noise level below 30%. Feature selection was particularly important, improving clustering performance by 34%. Conclusion These findings provide evidence-based recommendations for MOSD, enabling researchers to optimize analytical approaches and enhance the reliability of results across cancer datasets. The proposed MOSD framework offers a suggestive guideline addressing both computational and biological factors for multi-omics data integration. |
| format | Article |
| id | doaj-art-5dccffe3007b412193224e1ca17bfc3d |
| institution | Kabale University |
| issn | 1471-2164 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj-art-5dccffe3007b412193224e1ca17bfc3d2025-08-24T11:09:31ZengBMCBMC Genomics1471-21642025-08-0126111910.1186/s12864-025-11925-yA review on multi-omics integration for aiding study design of large scale TCGA cancer datasetsEonyong Han0Hwijun Kwon1Inuk Jung2School of Computer Science and Engineering, Kyungpook National UniversitySchool of Computer Science and Engineering, Kyungpook National UniversitySchool of Computer Science and Engineering, Kyungpook National UniversityAbstract Background Rapid advancements in high-throughput sequencing technologies allow for detailed and accurate measurement of omics features within their biological context. The integration of different omics types creates heterogeneous datasets, presenting challenges in analysis due to variations in measurement units, sample numbers, and features. Currently, there is a lack of generalized guidelines for making decisions in multi-omics study design (MOSD), such as selecting an appropriate number of samples and features, type of preprocessing and integration for robust analysis results. We propose a suggestive guideline for MOSD, involving nine important factors: sample size, feature selection, preprocessing strategy, noise characterization, class balance, number of classes, cancer subtype combination, omics combination, and clinical features. Results To assess the effectiveness of our proposed MOSD guidelines, we designed and conducted seven benchmark tests using 10 clustering methods on various TCGA cancer datasets with an objective of clustering cancer subtypes. The results indicated robust performance in terms of cancer subtype discrimination when adhering to the following criteria: 26 or more samples per class, selecting less than 10% of omics features, maintaining a sample balance under a 3:1 ratio, and keeping the noise level below 30%. Feature selection was particularly important, improving clustering performance by 34%. Conclusion These findings provide evidence-based recommendations for MOSD, enabling researchers to optimize analytical approaches and enhance the reliability of results across cancer datasets. The proposed MOSD framework offers a suggestive guideline addressing both computational and biological factors for multi-omics data integration.https://doi.org/10.1186/s12864-025-11925-yMulti-omicsIntegrationStudy designMachine learning |
| spellingShingle | Eonyong Han Hwijun Kwon Inuk Jung A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets BMC Genomics Multi-omics Integration Study design Machine learning |
| title | A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets |
| title_full | A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets |
| title_fullStr | A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets |
| title_full_unstemmed | A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets |
| title_short | A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets |
| title_sort | review on multi omics integration for aiding study design of large scale tcga cancer datasets |
| topic | Multi-omics Integration Study design Machine learning |
| url | https://doi.org/10.1186/s12864-025-11925-y |
| work_keys_str_mv | AT eonyonghan areviewonmultiomicsintegrationforaidingstudydesignoflargescaletcgacancerdatasets AT hwijunkwon areviewonmultiomicsintegrationforaidingstudydesignoflargescaletcgacancerdatasets AT inukjung areviewonmultiomicsintegrationforaidingstudydesignoflargescaletcgacancerdatasets AT eonyonghan reviewonmultiomicsintegrationforaidingstudydesignoflargescaletcgacancerdatasets AT hwijunkwon reviewonmultiomicsintegrationforaidingstudydesignoflargescaletcgacancerdatasets AT inukjung reviewonmultiomicsintegrationforaidingstudydesignoflargescaletcgacancerdatasets |