scCLIP: A contrastive masked-reconstruction framework for paired single-cell multi-omics integration.
Where this comes from
- Record sourced from PubMed, PMID 42419494.
- Also identified by DOI 10.1016/j.jbi.2026.105078.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Paired biomedical assays increasingly measure different molecular or clinical views from the same sample. The statistical problem is simple to state but hard to solve: the views often have different dimensions, noise models, and dynamic ranges, yet downstream analysis requires a common representation. Single-cell CITE-seq is a useful example because transcript counts and surface-protein abundances are observed in the same cell. Existing paired-omics methods, including probabilistic models, matrix-factorization approaches, and neural fusion models, have addressed this setting with different assumptions. Fewer studies, however, have asked whether a symmetric contrastive objective can align the two views while retaining modality-specific signal through reconstruction. We present scCLIP, a contrastive masked-reconstruction framework for paired single-cell multi-omics integration. scCLIP trains RNA and ADT branches jointly with a bidirectional cross-modal contrastive loss and masked reconstruction losses. The branches use the same architectural template but retain separate input/output adapters, encoder-decoder parameters, and projection heads; the projected embeddings are compared in an ℓ<sub>2</sub>-normalized space with a learnable logit scale. We evaluate scCLIP on five paired RNA-protein datasets, where it is compared against TotalVI, BREMSC, jointDIMMSC, scMM, and SCOIT and achieves the highest ARI and FMI on every dataset (with TotalVI second-best overall and marginally higher NMI on three of the five datasets), and on the larger NeurIPS 2021 BMMC CITE-seq benchmark (90,261 cells, 134 proteins, 45 cell types) where scCLIP scales without architectural change and produces strong batch mixing on a 12-batch dataset. We additionally provide direct evidence of RNA-ADT alignment through retrieval and distance-based alignment metrics, and report standard batch-mixing scores on the NeurIPS embedding. The results support scCLIP as a reusable paired-view representation-learning template, with RNA-protein integration serving as the primary empirical testbed. The source code and an end-to-end tutorial for applying scCLIP to new CITE-seq data are publicly available at https://github.com/xubohao39-cyber/scCLIP.