ArticleCells2019
An Efficient and Flexible Method for Deconvoluting Bulk RNA-Seq Data with Single-Cell RNA-Seq Data.
Article in Cells, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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
14 citing papers in PubMed.
- Unraveling CD4Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- A hybrid neighborhood enhanced contrastive learning and self-knowledge distillation method for scRNA-seq data clustering analysis.Bioinformatics (Oxford, England) · 2026Article
- omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data.Genome biology · 2026Article
- Deconer: An Evaluation Toolkit for Reference-based Deconvolution Methods Using Gene Expression Data.Genomics, proteomics & bioinformatics · 2025Article
- Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.Nucleic acids research · 2024Review
- Challenges and perspectives in computational deconvolution of genomics data.Nature methods · 2024Review
- Understanding virus-host interactions in tissues.Nature microbiology · 2023Review
- Review
- scFASTCORMICS: A Contextualization Algorithm to Reconstruct Metabolic Multi-Cell Population Models from Single-Cell RNAseq Data.Metabolites · 2022Article
- Unraveling Root Development Through Single-Cell Omics and Reconstruction of Gene Regulatory Networks.Frontiers in plant science · 2021Review
- Identifying Differentially Expressed Genes of Zero Inflated Single Cell RNA Sequencing Data Using Mixed Model Score Tests.Frontiers in genetics · 2021Article
- Deconvolution of Bulk Gene Expression Profiles with Single-Cell Transcriptomics to Develop a Cell Type Composition-Based Prognostic Model for Acute Myeloid Leukemia.Frontiers in cell and developmental biology · 2021Article
- Whole-Tissue Deconvolution and scRNAseq Analysis Identify Altered Endometrial Cellular Compositions and Functionality Associated With Endometriosis.Frontiers in immunology · 2021Article
- Pathologic evolution-related Gene Analysis based on both single-cell and bulk transcriptomics in Colorectal Cancer.Journal of Cancer · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Estimating cell type compositions for complex diseases is an important step to investigate the cellular heterogeneity for understanding disease etiology and potentially facilitate early disease diagnosis and prevention. Here, we developed a computationally statistical method, referring to Multi-Omics Matrix Factorization (MOMF), to estimate the cell-type compositions of bulk RNA sequencing (RNA-seq) data by leveraging cell type-specific gene expression levels from single-cell RNA sequencing (scRNA-seq) data. MOMF not only directly models the count nature of gene expression data, but also effectively accounts for the uncertainty of cell type-specific mean gene expression levels. We demonstrate the benefits of MOMF through three real data applications, i.e., Glioblastomas (GBM), colorectal cancer (CRC) and type II diabetes (T2D) studies. MOMF is able to accurately estimate disease-related cell type proportions, i.e., oligodendrocyte progenitor cells and macrophage cells, which are strongly associated with the survival of GBM and CRC, respectively.
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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.