ArticleCell genomics2025
OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.
Article in Cell genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Spatial Proteomics as a Potential Decision-Support Layer for Early Melanoma: A Narrative Review.International journal of dermatology · 2026Review
- DeOPUS: cellular deconvolution via optimized power-transformed unmixing with shrinkage.Briefings in bioinformatics · 2026Article
- Graph representation and learning for drug administration prediction.BMC medical informatics and decision making · 2026Article
- Early prediction and risk assessment of adverse drug combinations using ensemble learning.Scientific reports · 2026Article
- Integrating fuzzy logic and neural networks with multi-criteria decision-making for intelligent evaluation of drug compound design attributes.Scientific reports · 2026Article
- SA2E: spatial-aware auto-encoder for cell type deconvolution of spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- Integration of Bulk and Single-Cell RNA Sequencing Analyses in Biomedicine.International journal of molecular sciences · 2026Review
- Uncertainty-aware hybrid deep generative framework for robust and explainable drug discovery.BMC bioinformatics · 2026Article
- A domain-adaptive deep contrastive network for magnetic resonance imaging-driven bladder cancer classification.NPJ digital medicine · 2026Article
- Pan-cancer gene set discovery via scRNA-seq for optimal deep learning based downstream tasks.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Cell deconvolution estimates cell type proportions from bulk omics data, enabling insights into tissue microenvironments and disease. However, practical applications are often hindered by batch effects between bulk data and referenced single-cell data, a challenge that is frequently overlooked. To address this discrepancy, we developed OmicsTweezer, a distribution-independent cell deconvolution model. By integrating optimal transport with deep learning, OmicsTweezer aligns simulated and real data in a shared latent space, effectively mitigating data shifts and inter-omics distribution differences. OmicsTweezer is versatile, capable of deconvolving bulk RNA-seq, bulk proteomics, and spatial transcriptomics. Extensive evaluations on simulated and real-world datasets demonstrate its robustness and accuracy. Furthermore, applications in prostate and colon cancer showcase OmicsTweezer's ability to identify biologically meaningful cell types. As a unified deconvolution framework for multi-omics data, OmicsTweezer offers an efficient and powerful tool for studying disease microenvironments.
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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 Socratic graph.