Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasets
Abstract As single-cell sequencing technology became widely used, scientists found that single-modality data alone could not fully meet the research needs of complex biological systems. To address this issue, researchers began simultaneously collect multi-modal single-cell omics data. But different...
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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/s12859-025-06239-5 |
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| author | Zihao Wang Zeyu Wu Minghua Deng |
| author_facet | Zihao Wang Zeyu Wu Minghua Deng |
| author_sort | Zihao Wang |
| collection | DOAJ |
| description | Abstract As single-cell sequencing technology became widely used, scientists found that single-modality data alone could not fully meet the research needs of complex biological systems. To address this issue, researchers began simultaneously collect multi-modal single-cell omics data. But different sequencing technologies often result in datasets where one or more data modalities are missing. Therefore, mosaic datasets are more common when we analyze. However, the high dimensionality and sparsity of the data increase the difficulty, and the presence of batch effects poses an additional challenge. To address these challenges, we proposes a flexible integration framework based on Variational Autoencoder called scGCM. The main task of scGCM is to integrate single-cell multimodal mosaic data and eliminate batch effects. This method was conducted on multiple datasets, encompassing different modalities of single-cell data. The results demonstrate that, compared to state-of-the-art multimodal data integration methods, scGCM offers significant advantages in clustering accuracy and data consistency. The source code of scGCM can be accessed at https://github.com/closmouz/scCGM . |
| format | Article |
| id | doaj-art-9eed07cdc94c4fa59f1c4d923d16f811 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-9eed07cdc94c4fa59f1c4d923d16f8112025-08-20T03:46:15ZengBMCBMC Bioinformatics1471-21052025-08-0126111310.1186/s12859-025-06239-5Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasetsZihao Wang0Zeyu Wu1Minghua Deng2Biomedical Interdisciplinary Research Center, Peking UniversitySchool of Mathematical Sciences, Peking UniversityBiomedical Interdisciplinary Research Center, Peking UniversityAbstract As single-cell sequencing technology became widely used, scientists found that single-modality data alone could not fully meet the research needs of complex biological systems. To address this issue, researchers began simultaneously collect multi-modal single-cell omics data. But different sequencing technologies often result in datasets where one or more data modalities are missing. Therefore, mosaic datasets are more common when we analyze. However, the high dimensionality and sparsity of the data increase the difficulty, and the presence of batch effects poses an additional challenge. To address these challenges, we proposes a flexible integration framework based on Variational Autoencoder called scGCM. The main task of scGCM is to integrate single-cell multimodal mosaic data and eliminate batch effects. This method was conducted on multiple datasets, encompassing different modalities of single-cell data. The results demonstrate that, compared to state-of-the-art multimodal data integration methods, scGCM offers significant advantages in clustering accuracy and data consistency. The source code of scGCM can be accessed at https://github.com/closmouz/scCGM .https://doi.org/10.1186/s12859-025-06239-5Mosaic intergrateSingle-cell multimodalBatch effect |
| spellingShingle | Zihao Wang Zeyu Wu Minghua Deng Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasets BMC Bioinformatics Mosaic intergrate Single-cell multimodal Batch effect |
| title | Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasets |
| title_full | Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasets |
| title_fullStr | Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasets |
| title_full_unstemmed | Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasets |
| title_short | Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasets |
| title_sort | semi supervised contrastive learning variational autoencoder integrating single cell multimodal mosaic datasets |
| topic | Mosaic intergrate Single-cell multimodal Batch effect |
| url | https://doi.org/10.1186/s12859-025-06239-5 |
| work_keys_str_mv | AT zihaowang semisupervisedcontrastivelearningvariationalautoencoderintegratingsinglecellmultimodalmosaicdatasets AT zeyuwu semisupervisedcontrastivelearningvariationalautoencoderintegratingsinglecellmultimodalmosaicdatasets AT minghuadeng semisupervisedcontrastivelearningvariationalautoencoderintegratingsinglecellmultimodalmosaicdatasets |