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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.18655 |
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| _version_ | 1866909212653649920 |
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| author | Hsieh, Ping-Han Hsiao, Ru-Xiu Ferenc, Katalin Mathelier, Anthony Burkholz, Rebekka Chen, Chien-Yu Sandve, Geir Kjetil Belova, Tatiana Kuijjer, Marieke Lydia |
| author_facet | Hsieh, Ping-Han Hsiao, Ru-Xiu Ferenc, Katalin Mathelier, Anthony Burkholz, Rebekka Chen, Chien-Yu Sandve, Geir Kjetil Belova, Tatiana Kuijjer, Marieke Lydia |
| contents | Paired single-cell sequencing technologies enable the simultaneous measurement of complementary modalities of molecular data at single-cell resolution. Along with the advances in these technologies, many methods based on variational autoencoders have been developed to integrate these data. However, these methods do not explicitly incorporate prior biological relationships between the data modalities, which could significantly enhance modeling and interpretation. We propose a novel probabilistic learning framework that explicitly incorporates conditional independence relationships between multi-modal data as a directed acyclic graph using a generalized hierarchical variational autoencoder. We demonstrate the versatility of our framework across various applications pertinent to single-cell multi-omics data integration. These include the isolation of common and distinct information from different modalities, modality-specific differential analysis, and integrated cell clustering. We anticipate that the proposed framework can facilitate the construction of highly flexible graphical models that can capture the complexities of biological hypotheses and unravel the connections between different biological data types, such as different modalities of paired single-cell multi-omics data. The implementation of the proposed framework can be found in the repository https://github.com/kuijjerlab/CAVACHON. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18655 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data Hsieh, Ping-Han Hsiao, Ru-Xiu Ferenc, Katalin Mathelier, Anthony Burkholz, Rebekka Chen, Chien-Yu Sandve, Geir Kjetil Belova, Tatiana Kuijjer, Marieke Lydia Machine Learning Artificial Intelligence Genomics Paired single-cell sequencing technologies enable the simultaneous measurement of complementary modalities of molecular data at single-cell resolution. Along with the advances in these technologies, many methods based on variational autoencoders have been developed to integrate these data. However, these methods do not explicitly incorporate prior biological relationships between the data modalities, which could significantly enhance modeling and interpretation. We propose a novel probabilistic learning framework that explicitly incorporates conditional independence relationships between multi-modal data as a directed acyclic graph using a generalized hierarchical variational autoencoder. We demonstrate the versatility of our framework across various applications pertinent to single-cell multi-omics data integration. These include the isolation of common and distinct information from different modalities, modality-specific differential analysis, and integrated cell clustering. We anticipate that the proposed framework can facilitate the construction of highly flexible graphical models that can capture the complexities of biological hypotheses and unravel the connections between different biological data types, such as different modalities of paired single-cell multi-omics data. The implementation of the proposed framework can be found in the repository https://github.com/kuijjerlab/CAVACHON. |
| title | CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data |
| topic | Machine Learning Artificial Intelligence Genomics |
| url | https://arxiv.org/abs/2405.18655 |