ArticleNature communications2024
scPair: Boosting single cell multimodal analysis by leveraging implicit feature selection and single cell atlases.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- scDAU: a disentangled representation learning method for cross-modal translation in single-cell multi-omics data.Bioinformatics (Oxford, England) · 2026Article
- Review
- Predicting emergent phenotypes from single cell populations using CELLECTION.bioRxiv : the preprint server for biology · 2025Article
- scDCT: a conditional diffusion-based deep learning model for high-fidelity single-cell cross-modality translation.Briefings in bioinformatics · 2025Article
- annATAC: automatic cell type annotation for scATAC-seq data based on language model.BMC biology · 2025Article
- New horizons at the interface of artificial intelligence and translational cancer research.Cancer cell · 2025Review
- scPair: Boosting single cell multimodal analysis by leveraging implicit feature selection and single cell atlases.Nature communications · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Multimodal single-cell assays profile multiple sets of features in the same cells and are widely used for identifying and mapping cell states between chromatin and mRNA and linking regulatory elements to target genes. However, the high dimensionality of input features and shallow sequencing depth compared to unimodal assays pose challenges in data analysis. Here we present scPair, a multimodal single-cell data framework that overcomes these challenges by employing an implicit feature selection approach. scPair uses dual encoder-decoder structures trained on paired data to align cell states across modalities and predict features from one modality to another. We demonstrate that scPair outperforms existing methods in accuracy and execution time, and facilitates downstream tasks such as trajectory inference. We further show scPair can augment smaller multimodal datasets with larger unimodal atlases to increase statistical power to identify groups of transcription factors active during different stages of neural differentiation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.