Deep learning applications in single-cell omics data analysis [article]

Nafiseh Erfanian, A. Ali Heydari, Pablo Ianez, Afshin Derakhshani, Mohammad Ghasemigol, Mohsen Farahpour, Saeed Nasseri, Hossein Safarpour, Amirhossein Sahebkar
<span title="2021-11-27">2021</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Deep learning (DL) is a branch of machine learning (ML) capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across a range of domains and applications. Single-cell (SC) omics are often high-dimensional, sparse, and complex, making DL techniques ideal for analyzing and processing such data. We examine DL applications in a variety of single-cell omics (genomics, transcriptomics, proteomics,
more &raquo; ... olomics and multi-omics integration) and address whether DL techniques will prove to be advantageous or if the SC omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized or addressed the most pressing challenges of the SC omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) but lacking the needed biological interpretability in many cases. Although such developments have generally been gradual, recent advances reveal that DL methods can offer valuable resources in fast-tracking and advancing research in SC.
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