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PreprintCited by 44Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Phylogenetic Studies

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Deep Learning in Spatial Transcriptomics: Learning From the Next Next-Generation Sequencing

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Heydari, A. Ali, Sindi, Suzanne S.·2022·Biophysics Reviews
DOI 10.1101/2022.02.28.482392PMC PMC10903438

Preprint — published as 10.1063/5.0091135 · Paper page

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Abstract

Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. The data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially-resolved data (such as spatial resolution, sensitivity and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering among others. However, deep-learning models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially-resolved transcriptomics, while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight the domains in which we anticipate transformational DL applications.

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Metadata

Authors
Heydari, A. A., & Sindi, S. S.
Affiliations
University of California, Merced (US)
ORCID
Venue
Biophysics Reviews
Publisher
openRxiv
Published
2022-03-02
Volume
4
Issue
1
Pages
011306-011306
ISSN
2688-4089 (ISSN-L)
DOI
10.1101/2022.02.28.482392
PMC
PMC10903438
OpenAlex
W4319347394
Cited by
44
References
1
Citations by year
2026: 6 · 2025: 13 · 2024: 10 · 2023: 12 · 2022: 3
FWCI
1.08
Type
journal article
Language
en
Open access
Licence
cc-by-nd
Locations
  • Biophysics Reviews (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Science Foundation (R01-GM126548, 1840265)
  • National Institutes of Health (5r01gm126548-04, GM126548, R01-GM126548)
  • National Heart, Lung, and Blood Institute (R15HL146779)
  • National Human Genome Research Institute (F31HG012718)
  • National Institute of General Medical Sciences (R01-GM126548)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
spatial transcriptomics, deep learning, spatial reconstruction, spatial clustering, spatial alignment, multimodal data, tissue organization, gene expression profiling, single-cell RNA sequencing, histological images

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