A gene regulatory network–aware graph learning method for cell identity annotation in single-cell RNA-seq data Article Swipe
Mengyuan Zhao
,
Jiawei Li
,
Xiaoyi Liu
,
Ke Ma
,
Jijun Tang
,
Fei Guo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/gr.278439.123
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/gr.278439.123
Cell identity annotation for single-cell transcriptome data is a crucial process for constructing cell atlases, unraveling pathogenesis, and inspiring therapeutic approaches. Currently, the efficacy of existing methodologies is contingent upon specific data sets. Nevertheless, such data are often sourced from various batches, sequencing technologies, tissues, and even species. Notably, the gene regulatory relationship remains unaffected by the aforementioned factors, highlighting the extensive gene interactions within organisms. Therefore, we propose scHGR, an automated annotation tool designed to leverage gene regulatory relationships in constructing gene-mediated cell communication graphs for single-cell transcriptome data. This strategy helps reduce noise from diverse data sources while establishing distant cellular connections, yielding valuable biological insights. Experiments involving 22 scenarios demonstrate that scHGR precisely and consistently annotates cell identities, benchmarked against state-of-the-art methods. Crucially, scHGR uncovers novel subtypes within peripheral blood mononuclear cells, specifically from CD4 + T cells and cytotoxic T cells. Furthermore, by characterizing a cell atlas comprising 56 cell types for COVID-19 patients, scHGR identifies vital factors like IL1 and calcium ions, offering insights for targeted therapeutic interventions.
Related Topics
Concepts
Biology
Annotation
Transcriptome
Computational biology
Leverage (statistics)
Gene regulatory network
Gene
RNA-Seq
Gene Annotation
Genetics
Gene expression
Computer science
Genome
Machine learning
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1101/gr.278439.123
- OA Status
- green
- Cited By
- 9
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401503596
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401503596Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/gr.278439.123Digital Object Identifier
- Title
-
A gene regulatory network–aware graph learning method for cell identity annotation in single-cell RNA-seq dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-07-01Full publication date if available
- Authors
-
Mengyuan Zhao, Jiawei Li, Xiaoyi Liu, Ke Ma, Jijun Tang, Fei GuoList of authors in order
- Landing page
-
https://doi.org/10.1101/gr.278439.123Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/11368180Direct OA link when available
- Concepts
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Biology, Annotation, Transcriptome, Computational biology, Leverage (statistics), Gene regulatory network, Gene, RNA-Seq, Gene Annotation, Genetics, Gene expression, Computer science, Genome, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
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2025: 9Per-year citation counts (last 5 years)
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66Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.approaches. | 20 |
| abstract_inverted_index.benchmarked | 121 |
| abstract_inverted_index.demonstrate | 112 |
| abstract_inverted_index.identities, | 120 |
| abstract_inverted_index.mononuclear | 133 |
| abstract_inverted_index.single-cell | 4, 87 |
| abstract_inverted_index.therapeutic | 19, 171 |
| abstract_inverted_index.Furthermore, | 145 |
| abstract_inverted_index.connections, | 103 |
| abstract_inverted_index.consistently | 117 |
| abstract_inverted_index.constructing | 12, 81 |
| abstract_inverted_index.establishing | 100 |
| abstract_inverted_index.highlighting | 59 |
| abstract_inverted_index.interactions | 63 |
| abstract_inverted_index.relationship | 52 |
| abstract_inverted_index.specifically | 135 |
| abstract_inverted_index.Nevertheless, | 33 |
| abstract_inverted_index.communication | 84 |
| abstract_inverted_index.gene-mediated | 82 |
| abstract_inverted_index.methodologies | 26 |
| abstract_inverted_index.pathogenesis, | 16 |
| abstract_inverted_index.relationships | 79 |
| abstract_inverted_index.technologies, | 43 |
| abstract_inverted_index.transcriptome | 5, 88 |
| abstract_inverted_index.aforementioned | 57 |
| abstract_inverted_index.characterizing | 147 |
| abstract_inverted_index.interventions. | 172 |
| abstract_inverted_index.state-of-the-art | 123 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.91274399 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |