Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge Article Swipe
Hegang Chen
,
Yuyin Lu
,
Zhiming Dai
,
Yuedong Yang
,
Qing Li
,
Yanghui Rao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbae314
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbae314
Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explanations. In addition, most of the previous work based on generative models is limited to shallow neural networks with one to three layers of latent variables, which may limit the capabilities of the models. Here, we propose a deep interpretable generative model called d-scIGM for single-cell data analysis. d-scIGM combines sawtooth connectivity techniques and residual networks, thereby constructing a deep generative framework. In addition, d-scIGM incorporates hierarchical prior knowledge of biological domains to enhance the interpretability of the model. We show that d-scIGM achieves excellent performance in a variety of fundamental tasks, including clustering, visualization, and pseudo-temporal inference. Through topic pathway studies, we found that d-scIGM-learned topics are better enriched for biologically meaningful pathways compared to the baseline models. Furthermore, the analysis of drug response data shows that d-scIGM can capture drug response patterns in large-scale experiments, which provides a promising way to elucidate the underlying biological mechanisms. Lastly, in the melanoma dataset, d-scIGM accurately identified different cell types and revealed multiple melanin-related driver genes and key pathways, which are critical for understanding disease mechanisms and drug development.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bbae314
- https://academic.oup.com/bib/article-pdf/25/4/bbae314/58425807/bbae314.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400344236
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- OpenAlex ID
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https://openalex.org/W4400344236Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bib/bbae314Digital Object Identifier
- Title
-
Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledgeWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-23Full publication date if available
- Authors
-
Hegang Chen, Yuyin Lu, Zhiming Dai, Yuedong Yang, Qing Li, Yanghui RaoList of authors in order
- Landing page
-
https://doi.org/10.1093/bib/bbae314Publisher landing page
- PDF URL
-
https://academic.oup.com/bib/article-pdf/25/4/bbae314/58425807/bbae314.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://academic.oup.com/bib/article-pdf/25/4/bbae314/58425807/bbae314.pdfDirect OA link when available
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Interpretability, Computer science, Generative model, Artificial intelligence, Inference, Generative grammar, Machine learning, Hierarchy, Deep learning, Variety (cybernetics), Biological data, Bioinformatics, Biology, Market economy, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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63Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.incorporates | 118 |
| abstract_inverted_index.insufficient | 49 |
| abstract_inverted_index.technologies | 7 |
| abstract_inverted_index.Nevertheless, | 37 |
| abstract_inverted_index.explanations. | 53 |
| abstract_inverted_index.heterogeneity | 13 |
| abstract_inverted_index.interpretable | 92 |
| abstract_inverted_index.microfluidics | 4 |
| abstract_inverted_index.understanding | 225 |
| abstract_inverted_index.transcriptomic | 35 |
| abstract_inverted_index.visualization, | 147 |
| abstract_inverted_index.d-scIGM-learned | 158 |
| abstract_inverted_index.melanin-related | 215 |
| abstract_inverted_index.pseudo-temporal | 149 |
| abstract_inverted_index.interpretability | 128 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.74801542 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |