ArticleNature communications2022
Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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53 citing papers in PubMed, 93 citations in OpenAlex.
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- Integrated inference of cellular compositions and gene expression programs by deconvolution.Cell regeneration (London, England) · 2026Article
- iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data.PLoS computational biology · 2026Article
- Deep Learning-Based Multimodal Fusion of Whole-Slide Images and RNA Sequencing Identifies Survival-Relevant Glioblastoma Clusters.Cancer medicine · 2026Article
- Discovering reference-missing cell types in bulk transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samples.Nature biotechnology · 2026Article
- Statescope: an integrative deconvolution framework for discovering cell states in tumors.Nature communications · 2026Article
- Understanding the spatial determinants of the Oxford Classic prognostic signature for high-grade serous ovarian cancer.Communications medicine · 2026Article
- DADA-EV: domain-adaptive diffusion autoencoder for estimating tissue- and cell-type-specific origin in extracellular vesicle transcriptomes.Briefings in bioinformatics · 2026Article
- DECODE: deep learning-based common deconvolution framework for various omics data.Nature methods · 2026Article
- Deconvolving cell-type-specific gene expression profiles from bulk RNA-seq samples.PLoS computational biology · 2026Article
- Evaluating reference-mixture matching in cell-type deconvolution with single-cell RNA-seq references.Briefings in bioinformatics · 2026Article
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- scPER: A Rigorous Computational Approach to Determine Cellular Subtypes in Tumors Aligned With Cancer Phenotypes From Total RNA Sequencing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- IPGCA: A Comprehensive Single Cell Atlas of 1 074 127 Porcine Intestinal Cells Revealing Cellular Dynamics, Genetic Regulation, and Cross-Species Conservation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- OncoMark: a high-throughput neural multi-task learning framework for comprehensive cancer hallmark quantification.Communications biology · 2025Article
- MOADE: a multimodal autoencoder for dissociating bulk multi-omics data.Genome biology · 2025Article
- OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.Cell genomics · 2025Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA-sequencing has become a powerful tool to study biologically significant characteristics at explicitly high resolution. However, its application on emerging data is currently limited by its intrinsic techniques. Here, we introduce Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq to achieve precise deconvolution in a short time. By constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with popular methods on several datasets, TAPE has a better overall performance and comparable accuracy at cell type level. Additionally, it is more robust among different cell types, faster, and sensitive to provide biologically meaningful predictions. Moreover, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.
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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.