Dataset Information

InClust+: the deep generative framework with mask modules for multimodal data integration, imputation, and cross-modal generation.

ABSTRACT:

Background

With the development of single-cell technology, many cell traits can be measured. Furthermore, the multi-omics profiling technology could jointly measure two or more traits in a single cell simultaneously. In order to process the various data accumulated rapidly, computational methods for multimodal data integration are needed.

Results

Here, we present inClust+, a deep generative framework for the multi-omics. It's built on previous inClust that is specific for transcriptome data, and augmented with two mask modules designed for multimodal data processing: an input-mask module in front of the encoder and an output-mask module behind the decoder. InClust+ was first used to integrate scRNA-seq and MERFISH data from similar cell populations, and to impute MERFISH data based on scRNA-seq data. Then, inClust+ was shown to have the capability to integrate the multimodal data (e.g. tri-modal data with gene expression, chromatin accessibility and protein abundance) with batch effect. Finally, inClust+ was used to integrate an unlabeled monomodal scRNA-seq dataset and two labeled multimodal CITE-seq datasets, transfer labels from CITE-seq datasets to scRNA-seq dataset, and generate the missing modality of protein abundance in monomodal scRNA-seq data. In the above examples, the performance of inClust+ is better than or comparable to the most recent tools in the corresponding task.

Conclusions

The inClust+ is a suitable framework for handling multimodal data. Meanwhile, the successful implementation of mask in inClust+ means that it can be applied to other deep learning methods with similar encoder-decoder architecture to broaden the application scope of these models.

SUBMITTER: Wang L

PROVIDER: S-EPMC10809631 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

InClust+: the deep generative framework with mask modules for multimodal data integration, imputation, and cross-modal generation.

Wang Lifei L Nie Rui R Miao Xuexia X Cai Yankai Y Wang Anqi A Zhang Hanwen H Zhang Jiang J Cai Jun J

BMC bioinformatics 20240124 1

<h4>Background</h4>With the development of single-cell technology, many cell traits can be measured. Furthermore, the multi-omics profiling technology could jointly measure two or more traits in a single cell simultaneously. In order to process the various data accumulated rapidly, computational methods for multimodal data integration are needed.<h4>Results</h4>Here, we present inClust+, a deep generative framework for the multi-omics. It's built on previous inClust that is specific for transcri ...[more]

PMID: 38267858

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Dataset Information

InClust+: the deep generative framework with mask modules for multimodal data integration, imputation, and cross-modal generation.

Background

Results

Conclusions

Publications

InClust+: the deep generative framework with mask modules for multimodal data integration, imputation, and cross-modal generation.

Similar Datasets

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