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.

MOTIVATION: Copy number abbreviation (CNA) is one type of genomic aberration that is often induced by genome instability and is associated with diseases such as cancer. Determination of the genome-wide CNA profile is an important step in identifying the underlying mutation mechanisms. Genomic data based on next-generation sequencing technology are particularly suitable for determination of high-quality CNA profile. Now is an important time to reevaluate the use of sequencing techniques for CNA analysis, especially with the rapid growth of the different targeted genome and whole-genome sequencing strategies. RESULTS: In this study, we provide a comparison of resequencing strategies, with regard to their utility, applied to the same hepatocellular carcinoma sample for copy number determination. These strategies include whole-genome, exome and restriction site-associated DNA (RAD) sequencing. The last of these strategies is a targeted sequencing technique that involves cutting the genome with a restriction enzyme and isolating the targeted sequences. Our data demonstrate that RAD sequencing is an efficient and comprehensive strategy that allows the cost-effective determination of CNAs. Further investigation of RAD sequencing data led to the finding that a precise measurement of the allele frequency would be a helpful complement to the read depth for CNA analysis for two reasons. First, knowledge of the allele frequency helps to resolve refined calculations of allele-specific copy numbers, which, in turn, identify the functionally important CNAs that are under natural selection on the parental alleles. Second, this knowledge enables deconvolution of CNA patterns in complex genomic regions.