Knowledge-based inductive bias and domain adaptation for cell type annotation
Abstract Measurement techniques often result in domain gaps among batches of cellular data from a specific modality. The effectiveness of cross-batch annotation methods is influenced by inductive bias, which refers to a set of assumptions that describe the behavior of model predictions. Different an...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-024-07171-9 |
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| _version_ | 1850194811981660160 |
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| author | Zhenchao Tang Guanxing Chen Shouzhi Chen Haohuai He Linlin You Calvin Yu-Chian Chen |
| author_facet | Zhenchao Tang Guanxing Chen Shouzhi Chen Haohuai He Linlin You Calvin Yu-Chian Chen |
| author_sort | Zhenchao Tang |
| collection | DOAJ |
| description | Abstract Measurement techniques often result in domain gaps among batches of cellular data from a specific modality. The effectiveness of cross-batch annotation methods is influenced by inductive bias, which refers to a set of assumptions that describe the behavior of model predictions. Different annotation methods possess distinct inductive biases, leading to varying degrees of generalizability and interpretability. Given that certain cell types exhibit unique functional patterns, we hypothesize that the inductive biases of cell annotation methods should align with these biological patterns to produce meaningful predictions. In this study, we propose KIDA, Knowledge-based Inductive bias and Domain Adaptation. The knowledge-based inductive bias constrains the prediction rules learned from the reference dataset, composed of multiple batches, to functional patterns relevant to biology, thereby enhancing the generalization of the model to unseen batches. Since the query dataset also contains gaps from multiple batches, KIDA’s domain adaptation employs pseudo labels for self-knowledge distillation, effectively narrowing the distribution gap between model predictions and the query dataset. Benchmark experiments demonstrate that KIDA is capable of achieving accurate cross-batch cell type annotation. |
| format | Article |
| id | doaj-art-9896a52e9c4c4caf9ae7fc54eecfb257 |
| institution | OA Journals |
| issn | 2399-3642 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-9896a52e9c4c4caf9ae7fc54eecfb2572025-08-20T02:13:55ZengNature PortfolioCommunications Biology2399-36422024-11-017111010.1038/s42003-024-07171-9Knowledge-based inductive bias and domain adaptation for cell type annotationZhenchao Tang0Guanxing Chen1Shouzhi Chen2Haohuai He3Linlin You4Calvin Yu-Chian Chen5Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen UniversityArtificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen UniversityArtificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen UniversityArtificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen UniversityArtificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen UniversityAI for Science (AI4S)-Preferred Program, School of Electronic and Computer Engineering, Peking University Shenzhen Graduate SchoolAbstract Measurement techniques often result in domain gaps among batches of cellular data from a specific modality. The effectiveness of cross-batch annotation methods is influenced by inductive bias, which refers to a set of assumptions that describe the behavior of model predictions. Different annotation methods possess distinct inductive biases, leading to varying degrees of generalizability and interpretability. Given that certain cell types exhibit unique functional patterns, we hypothesize that the inductive biases of cell annotation methods should align with these biological patterns to produce meaningful predictions. In this study, we propose KIDA, Knowledge-based Inductive bias and Domain Adaptation. The knowledge-based inductive bias constrains the prediction rules learned from the reference dataset, composed of multiple batches, to functional patterns relevant to biology, thereby enhancing the generalization of the model to unseen batches. Since the query dataset also contains gaps from multiple batches, KIDA’s domain adaptation employs pseudo labels for self-knowledge distillation, effectively narrowing the distribution gap between model predictions and the query dataset. Benchmark experiments demonstrate that KIDA is capable of achieving accurate cross-batch cell type annotation.https://doi.org/10.1038/s42003-024-07171-9 |
| spellingShingle | Zhenchao Tang Guanxing Chen Shouzhi Chen Haohuai He Linlin You Calvin Yu-Chian Chen Knowledge-based inductive bias and domain adaptation for cell type annotation Communications Biology |
| title | Knowledge-based inductive bias and domain adaptation for cell type annotation |
| title_full | Knowledge-based inductive bias and domain adaptation for cell type annotation |
| title_fullStr | Knowledge-based inductive bias and domain adaptation for cell type annotation |
| title_full_unstemmed | Knowledge-based inductive bias and domain adaptation for cell type annotation |
| title_short | Knowledge-based inductive bias and domain adaptation for cell type annotation |
| title_sort | knowledge based inductive bias and domain adaptation for cell type annotation |
| url | https://doi.org/10.1038/s42003-024-07171-9 |
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