Deep learning applications in single-cell genomics and transcriptomics data analysis

Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. However, the single-cell technologies generate massive amounts of data that are often high-dimensional, sparse, and complex, thus making analysis with traditional computational approaches difficult and unfeasible. To tackle these chal-lenges, many are turning to deep learning (DL) methods as potential alternatives to the conventional machine learning (ML) algorithms for single-cell studies. DL is a branch of ML capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant im-provements across many domains and applications. In this work, we examine DL applications in genomics, transcriptomics, spatial transcriptomics, and multi-omics integration, and address whether DL techniques will prove to be advantageous or if the single-cell omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized the most pressing challenges of the single-cell omics field. However, using DL models for single-cell omics has shown promising results (in many cases out-performing the previous state-of-the-art models) in data preprocessing and downstream analysis. Although de-velopments of DL algorithms for single-cell omics have generally been gradual, recent advances reveal that DL can offer valuable resources in fast-tracking and advancing research in single-cell.

Automated summary

Traditional bulk sequencing methods have limitations in measuring cell groups, masking heterogeneity and rare populations. Single-cell resolution improves understanding of complex biological systems and diseases, but also generates high-dimensional, sparse, and complex data that makes analysis difficult. Deep learning has shown promising results in single-cell omics for data preprocessing and downstream analysis, but has not revolutionized the field yet. However, recent advances reveal its valuable resources in fast-tracking and advancing research in single-cell.