Tools
Tools
Hira, MT, Razzaque, MA, Angione, C, Scrivens, J, Sawan, S and Sarker, M (2021) Integrated multi‑omics analysis of ovarian cancer using variational autoencoders. Scientific Reports, 11 (1). ISSN 2045-2322
|
Text
Integrated multi-omics analysis of ovarian cancer using variational autoencoders.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview |
|
|
Text
Author Correction Integrated multi‑omics analysis of ovarian cancer using variational autoencoders.pdf - Published Version Available under License Creative Commons Attribution. Download (744kB) | Preview |
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.
| Item Type: | Article |
|---|---|
| Subjects: | R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) R Medicine > RS Pharmacy and materia medica |
| Divisions: | Pharmacy & Biomolecular Sciences |
| Publisher: | Nature Publishing Group |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 15 Feb 2024 11:31 |
| Last Modified: | 15 Feb 2024 11:31 |
| DOI or ID number: | 10.1038/s41598-021-85285-4 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/22628 |
 |
View Item |