Integrated Multi-omics Analysis of Ovarian Cancer Using Variational Autoencoder

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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.
Original languageEnglish
JournalNature Scientific Reports
Publication statusPublished - 18 Mar 2021

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