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Evaluation and Comparison of Multi-Omics Data Integration Methods for Subtyping of Cutaneous Melanoma
There is a growing number of multi-domain genomic datasets for human tumors. Multi-domain data are usually interpreted after separately analyzing single-domain data and integrating the results post hoc. Data fusion techniques allow for the real integration of multi-domain data to ideally improve the...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775581/ https://www.ncbi.nlm.nih.gov/pubmed/36551996 http://dx.doi.org/10.3390/biomedicines10123240 |
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author | Amaro, Adriana Pfeffer, Max Pfeffer, Ulrich Reggiani, Francesco |
author_facet | Amaro, Adriana Pfeffer, Max Pfeffer, Ulrich Reggiani, Francesco |
author_sort | Amaro, Adriana |
collection | PubMed |
description | There is a growing number of multi-domain genomic datasets for human tumors. Multi-domain data are usually interpreted after separately analyzing single-domain data and integrating the results post hoc. Data fusion techniques allow for the real integration of multi-domain data to ideally improve the tumor classification results for the prognosis and prediction of response to therapy. We have previously described the joint singular value decomposition (jSVD) technique as a means of data fusion. Here, we report on the development of these methods in open source code based on R and Python and on the application of these data fusion methods. The Cancer Genome Atlas (TCGA) Skin Cutaneous Melanoma (SKCM) dataset was used as a benchmark to evaluate the potential of the data fusion approaches to improve molecular classification of cancers in a clinically relevant manner. Our data show that the data fusion approach does not generate classification results superior to those obtained using single-domain data. Data from different domains are not entirely independent from each other, and molecular classes are characterized by features that penetrate different domains. Data fusion techniques might be better suited for response prediction, where they could contribute to the identification of predictive features in a domain-independent manner to be used as biomarkers. |
format | Online Article Text |
id | pubmed-9775581 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97755812022-12-23 Evaluation and Comparison of Multi-Omics Data Integration Methods for Subtyping of Cutaneous Melanoma Amaro, Adriana Pfeffer, Max Pfeffer, Ulrich Reggiani, Francesco Biomedicines Article There is a growing number of multi-domain genomic datasets for human tumors. Multi-domain data are usually interpreted after separately analyzing single-domain data and integrating the results post hoc. Data fusion techniques allow for the real integration of multi-domain data to ideally improve the tumor classification results for the prognosis and prediction of response to therapy. We have previously described the joint singular value decomposition (jSVD) technique as a means of data fusion. Here, we report on the development of these methods in open source code based on R and Python and on the application of these data fusion methods. The Cancer Genome Atlas (TCGA) Skin Cutaneous Melanoma (SKCM) dataset was used as a benchmark to evaluate the potential of the data fusion approaches to improve molecular classification of cancers in a clinically relevant manner. Our data show that the data fusion approach does not generate classification results superior to those obtained using single-domain data. Data from different domains are not entirely independent from each other, and molecular classes are characterized by features that penetrate different domains. Data fusion techniques might be better suited for response prediction, where they could contribute to the identification of predictive features in a domain-independent manner to be used as biomarkers. MDPI 2022-12-13 /pmc/articles/PMC9775581/ /pubmed/36551996 http://dx.doi.org/10.3390/biomedicines10123240 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Amaro, Adriana Pfeffer, Max Pfeffer, Ulrich Reggiani, Francesco Evaluation and Comparison of Multi-Omics Data Integration Methods for Subtyping of Cutaneous Melanoma |
title | Evaluation and Comparison of Multi-Omics Data Integration Methods for Subtyping of Cutaneous Melanoma |
title_full | Evaluation and Comparison of Multi-Omics Data Integration Methods for Subtyping of Cutaneous Melanoma |
title_fullStr | Evaluation and Comparison of Multi-Omics Data Integration Methods for Subtyping of Cutaneous Melanoma |
title_full_unstemmed | Evaluation and Comparison of Multi-Omics Data Integration Methods for Subtyping of Cutaneous Melanoma |
title_short | Evaluation and Comparison of Multi-Omics Data Integration Methods for Subtyping of Cutaneous Melanoma |
title_sort | evaluation and comparison of multi-omics data integration methods for subtyping of cutaneous melanoma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775581/ https://www.ncbi.nlm.nih.gov/pubmed/36551996 http://dx.doi.org/10.3390/biomedicines10123240 |
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