Phenotype prediction from single-cell RNA-seq data using attention-based neural networks Article Swipe
Yuzhen Mao
,
Yen‐Yi Lin
,
Nelson K.Y. Wong
,
Stanislav Volik
,
Funda Sar
,
Colin C. Collins
,
Martin Ester
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btae067
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btae067
Motivation A patient’s disease phenotype can be driven and determined by specific groups of cells whose marker genes are either unknown or can only be detected at late-stage using conventional bulk assays such as RNA-Seq technology. Recent advances in single-cell RNA sequencing (scRNA-seq) enable gene expression profiling in cell-level resolution, and therefore have the potential to identify those cells driving the disease phenotype even while the number of these cells is small. However, most existing methods rely heavily on accurate cell type detection, and the number of available annotated samples is usually too small for training deep learning predictive models. Results Here, we propose the method ScRAT for phenotype prediction using scRNA-seq data. To train ScRAT with a limited number of samples of different phenotypes, such as coronavirus disease (COVID) and non-COVID, ScRAT first applies a mixup module to increase the number of training samples. A multi-head attention mechanism is employed to learn the most informative cells for each phenotype without relying on a given cell type annotation. Using three public COVID datasets, we show that ScRAT outperforms other phenotype prediction methods. The performance edge of ScRAT over its competitors increases as the number of training samples decreases, indicating the efficacy of our sample mixup. Critical cell types detected based on high-attention cells also support novel findings in the original papers and the recent literature. This suggests that ScRAT overcomes the challenge of missing marker genes and limited sample number with great potential revealing novel molecular mechanisms and/or therapies. Availability and implementation The code of our proposed method ScRAT is published at https://github.com/yuzhenmao/ScRAT.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btae067
- https://academic.oup.com/bioinformatics/article-pdf/40/2/btae067/56792973/btae067.pdf
- OA Status
- gold
- Cited By
- 21
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392103664
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392103664Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/bioinformatics/btae067Digital Object Identifier
- Title
-
Phenotype prediction from single-cell RNA-seq data using attention-based neural networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-01Full publication date if available
- Authors
-
Yuzhen Mao, Yen‐Yi Lin, Nelson K.Y. Wong, Stanislav Volik, Funda Sar, Colin C. Collins, Martin EsterList of authors in order
- Landing page
-
https://doi.org/10.1093/bioinformatics/btae067Publisher landing page
- PDF URL
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https://academic.oup.com/bioinformatics/article-pdf/40/2/btae067/56792973/btae067.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/bioinformatics/article-pdf/40/2/btae067/56792973/btae067.pdfDirect OA link when available
- Concepts
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Artificial neural network, Phenotype, Computer science, RNA-Seq, Artificial intelligence, Computational biology, Deep neural networks, Software, Machine learning, Data mining, Pattern recognition (psychology), Biology, Gene, Genetics, Transcriptome, Gene expression, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
21Total citation count in OpenAlex
- Citations by year (recent)
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2025: 14, 2024: 7Per-year citation counts (last 5 years)
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31Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.outperforms | 178 |
| abstract_inverted_index.patient’s | 3 |
| abstract_inverted_index.performance | 184 |
| abstract_inverted_index.phenotypes, | 125 |
| abstract_inverted_index.resolution, | 50 |
| abstract_inverted_index.single-cell | 40 |
| abstract_inverted_index.technology. | 36 |
| abstract_inverted_index.Availability | 250 |
| abstract_inverted_index.conventional | 30 |
| abstract_inverted_index.high-attention | 212 |
| abstract_inverted_index.implementation | 252 |
| abstract_inverted_index.https://github.com/yuzhenmao/ScRAT. | 263 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.97523328 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |