Autoencoders with shared and specific embeddings for multi-omics data integration
Abstract Background In cancer research, different levels of high-dimensional data are often collected for the same subjects. Effective integration of these data by considering the shared and specific information from each data source can help us better understand different types of cancer. Results I...
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| Language: | English |
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BMC
2025-08-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06245-7 |
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| author | Chao Wang Michael J. O’Connell |
| author_facet | Chao Wang Michael J. O’Connell |
| author_sort | Chao Wang |
| collection | DOAJ |
| description | Abstract Background In cancer research, different levels of high-dimensional data are often collected for the same subjects. Effective integration of these data by considering the shared and specific information from each data source can help us better understand different types of cancer. Results In this study we propose a novel autoencoder (AE) structure with explicitly defined orthogonal loss between the shared and specific embeddings to integrate different data sources. We compare our model with previously proposed AE structures based on simulated data and real cancer data from The Cancer Genome Atlas. Using simulations with different proportions of differentially expressed genes, we compare the performance of AE methods for subsequent classification tasks. We also compare the model performance with a commonly used dimension reduction method, joint and individual variance explained (JIVE). In terms of reconstruction loss, our proposed AE models with orthogonal constraints have a slightly better reconstruction loss. All AE models achieve higher classification accuracy than the original features, demonstrating the usefulness of the embeddings extracted by the model. Conclusions We show that the proposed models have consistently high classification accuracy on both training and testing sets. In comparison, the recently proposed MOCSS model that imposes an orthogonality penalty in the post-processing step has lower classification accuracy that is on par with JIVE. |
| format | Article |
| id | doaj-art-84401cc432874bd9a04cf0e1f7bf2999 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-84401cc432874bd9a04cf0e1f7bf29992025-08-24T11:54:37ZengBMCBMC Bioinformatics1471-21052025-08-0126111610.1186/s12859-025-06245-7Autoencoders with shared and specific embeddings for multi-omics data integrationChao Wang0Michael J. O’Connell1Ben May Department for Cancer Research, University of ChicagoDepartment of Statistics, Miami UniversityAbstract Background In cancer research, different levels of high-dimensional data are often collected for the same subjects. Effective integration of these data by considering the shared and specific information from each data source can help us better understand different types of cancer. Results In this study we propose a novel autoencoder (AE) structure with explicitly defined orthogonal loss between the shared and specific embeddings to integrate different data sources. We compare our model with previously proposed AE structures based on simulated data and real cancer data from The Cancer Genome Atlas. Using simulations with different proportions of differentially expressed genes, we compare the performance of AE methods for subsequent classification tasks. We also compare the model performance with a commonly used dimension reduction method, joint and individual variance explained (JIVE). In terms of reconstruction loss, our proposed AE models with orthogonal constraints have a slightly better reconstruction loss. All AE models achieve higher classification accuracy than the original features, demonstrating the usefulness of the embeddings extracted by the model. Conclusions We show that the proposed models have consistently high classification accuracy on both training and testing sets. In comparison, the recently proposed MOCSS model that imposes an orthogonality penalty in the post-processing step has lower classification accuracy that is on par with JIVE.https://doi.org/10.1186/s12859-025-06245-7Multi-omicsData integrationAutoencodersShared and specific information |
| spellingShingle | Chao Wang Michael J. O’Connell Autoencoders with shared and specific embeddings for multi-omics data integration BMC Bioinformatics Multi-omics Data integration Autoencoders Shared and specific information |
| title | Autoencoders with shared and specific embeddings for multi-omics data integration |
| title_full | Autoencoders with shared and specific embeddings for multi-omics data integration |
| title_fullStr | Autoencoders with shared and specific embeddings for multi-omics data integration |
| title_full_unstemmed | Autoencoders with shared and specific embeddings for multi-omics data integration |
| title_short | Autoencoders with shared and specific embeddings for multi-omics data integration |
| title_sort | autoencoders with shared and specific embeddings for multi omics data integration |
| topic | Multi-omics Data integration Autoencoders Shared and specific information |
| url | https://doi.org/10.1186/s12859-025-06245-7 |
| work_keys_str_mv | AT chaowang autoencoderswithsharedandspecificembeddingsformultiomicsdataintegration AT michaeljoconnell autoencoderswithsharedandspecificembeddingsformultiomicsdataintegration |