Integrated multi-omics analysis of ovarian cancer using variational autoencoders

Abstract Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In rec...

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Autores principales:	Muta Tah Hira, M. A. Razzaque, Claudio Angione, James Scrivens, Saladin Sawan, Mosharraf Sarkar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3df6c2d9b23b40b1a3a6c34eb8bb4cdb
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spelling	oai:doaj.org-article:3df6c2d9b23b40b1a3a6c34eb8bb4cdb2021-12-02T16:31:17ZIntegrated multi-omics analysis of ovarian cancer using variational autoencoders10.1038/s41598-021-85285-42045-2322https://doaj.org/article/3df6c2d9b23b40b1a3a6c34eb8bb4cdb2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85285-4https://doaj.org/toc/2045-2322Abstract Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In recent years, Deep Learning (DL) approaches have become a useful tool in integrated multi-omics analysis of cancer data. However, high dimensional multi-omics data are generally imbalanced with too many molecular features and relatively few patient samples. This imbalance makes a DL based integrated multi-omics analysis difficult. DL-based dimensionality reduction technique, including variational autoencoder (VAE), is a potential solution to balance high dimensional multi-omics data. However, there are few VAE-based integrated multi-omics analyses, and they are limited to pancancer. In this work, we did an integrated multi-omics analysis of ovarian cancer using the compressed features learned through VAE and an improved version of VAE, namely Maximum Mean Discrepancy VAE (MMD-VAE). First, we designed and developed a DL architecture for VAE and MMD-VAE. Then we used the architecture for mono-omics, integrated di-omics and tri-omics data analysis of ovarian cancer through cancer samples identification, molecular subtypes clustering and classification, and survival analysis. The results show that MMD-VAE and VAE-based compressed features can respectively classify the transcriptional subtypes of the TCGA datasets with an accuracy in the range of 93.2-95.5% and 87.1-95.7%. Also, survival analysis results show that VAE and MMD-VAE based compressed representation of omics data can be used in cancer prognosis. Based on the results, we can conclude that (i) VAE and MMD-VAE outperform existing dimensionality reduction techniques, (ii) integrated multi-omics analyses perform better or similar compared to their mono-omics counterparts, and (iii) MMD-VAE performs better than VAE in most omics dataset.Muta Tah HiraM. A. RazzaqueClaudio AngioneJames ScrivensSaladin SawanMosharraf SarkarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Muta Tah Hira M. A. Razzaque Claudio Angione James Scrivens Saladin Sawan Mosharraf Sarkar Integrated multi-omics analysis of ovarian cancer using variational autoencoders
description	Abstract Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In recent years, Deep Learning (DL) approaches have become a useful tool in integrated multi-omics analysis of cancer data. However, high dimensional multi-omics data are generally imbalanced with too many molecular features and relatively few patient samples. This imbalance makes a DL based integrated multi-omics analysis difficult. DL-based dimensionality reduction technique, including variational autoencoder (VAE), is a potential solution to balance high dimensional multi-omics data. However, there are few VAE-based integrated multi-omics analyses, and they are limited to pancancer. In this work, we did an integrated multi-omics analysis of ovarian cancer using the compressed features learned through VAE and an improved version of VAE, namely Maximum Mean Discrepancy VAE (MMD-VAE). First, we designed and developed a DL architecture for VAE and MMD-VAE. Then we used the architecture for mono-omics, integrated di-omics and tri-omics data analysis of ovarian cancer through cancer samples identification, molecular subtypes clustering and classification, and survival analysis. The results show that MMD-VAE and VAE-based compressed features can respectively classify the transcriptional subtypes of the TCGA datasets with an accuracy in the range of 93.2-95.5% and 87.1-95.7%. Also, survival analysis results show that VAE and MMD-VAE based compressed representation of omics data can be used in cancer prognosis. Based on the results, we can conclude that (i) VAE and MMD-VAE outperform existing dimensionality reduction techniques, (ii) integrated multi-omics analyses perform better or similar compared to their mono-omics counterparts, and (iii) MMD-VAE performs better than VAE in most omics dataset.
format	article
author	Muta Tah Hira M. A. Razzaque Claudio Angione James Scrivens Saladin Sawan Mosharraf Sarkar
author_facet	Muta Tah Hira M. A. Razzaque Claudio Angione James Scrivens Saladin Sawan Mosharraf Sarkar
author_sort	Muta Tah Hira
title	Integrated multi-omics analysis of ovarian cancer using variational autoencoders
title_short	Integrated multi-omics analysis of ovarian cancer using variational autoencoders
title_full	Integrated multi-omics analysis of ovarian cancer using variational autoencoders
title_fullStr	Integrated multi-omics analysis of ovarian cancer using variational autoencoders
title_full_unstemmed	Integrated multi-omics analysis of ovarian cancer using variational autoencoders
title_sort	integrated multi-omics analysis of ovarian cancer using variational autoencoders
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3df6c2d9b23b40b1a3a6c34eb8bb4cdb
work_keys_str_mv	AT mutatahhira integratedmultiomicsanalysisofovariancancerusingvariationalautoencoders AT marazzaque integratedmultiomicsanalysisofovariancancerusingvariationalautoencoders AT claudioangione integratedmultiomicsanalysisofovariancancerusingvariationalautoencoders AT jamesscrivens integratedmultiomicsanalysisofovariancancerusingvariationalautoencoders AT saladinsawan integratedmultiomicsanalysisofovariancancerusingvariationalautoencoders AT mosharrafsarkar integratedmultiomicsanalysisofovariancancerusingvariationalautoencoders
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Integrated multi-omics analysis of ovarian cancer using variational autoencoders

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