PT  - JOURNAL ARTICLE
AU  - Nafiseh Erfanian
AU  - A. Ali Heydari
AU  - Pablo Iañez
AU  - Afshin Derakhshani
AU  - Mohammad Ghasemigol
AU  - Mohsen Farahpour
AU  - Saeed Nasseri
AU  - Hossein Safarpour
AU  - Amirhossein Sahebkar
TI  - Deep learning applications in single-cell omics data analysis
AID  - 10.1101/2021.11.26.470166
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.11.26.470166
4099  - http://biorxiv.org/content/early/2021/11/27/2021.11.26.470166.short
4100  - http://biorxiv.org/content/early/2021/11/27/2021.11.26.470166.full
AB  - 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, metabolomics 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.Competing Interest StatementThe authors have declared no competing interest.