ArticleProceedings of the National Academy of Sciences of the United States of America2003
Expression deconvolution: a reinterpretation of DNA microarray data reveals dynamic changes in cell populations.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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Who cites it
70 citing papers in PubMed, 136 citations in OpenAlex.
- Unico: a unified model for cell-type resolution genomics from heterogeneous omics data.Genome biology · 2025Article
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- Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells.International journal of molecular sciences · 2023Article
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- Real age prediction from the transcriptome with RAPToR.Nature methods · 2022Article
- DURIAN: an integrative deconvolution and imputation method for robust signaling analysis of single-cell transcriptomics data.Briefings in bioinformatics · 2022Article
- Semi-deconvolution of bulk and single-cell RNA-seq data with application to metastatic progression in breast cancer.Bioinformatics (Oxford, England) · 2022Article
- A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders.NAR genomics and bioinformatics · 2021Article
- CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data.PLoS computational biology · 2019Article
- RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types.Cell reports · 2019Article
- BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference.Genome biology · 2018Article
- Detect tissue heterogeneity in gene expression data with BioQC.BMC genomics · 2017Article
- Enumerateblood - an R package to estimate the cellular composition of whole blood from Affymetrix Gene ST gene expression profiles.BMC genomics · 2017Article
- Convex Analysis of Mixtures for Separating Non-negative Well-grounded Sources.Scientific reports · 2016Article
- Regulatory complexity revealed by integrated cytological and RNA-seq analyses of meiotic substages in mouse spermatocytes.BMC genomics · 2016Article
- High-throughput genomic profiling of tumor-infiltrating leukocytes.Current opinion in immunology · 2016Review
- Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues.Scientific reports · 2016Article
- Estimation of Cell-Type Composition Including T and B Cell Subtypes for Whole Blood Methylation Microarray Data.Frontiers in genetics · 2016Article
- Article
- ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles.BMC bioinformatics · 2015Article
10 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Cells grow in dynamically evolving populations, yet this aspect of experiments often goes unmeasured. A method is proposed for measuring the population dynamics of cells on the basis of their mRNA expression patterns. The population's expression pattern is modeled as the linear combination of mRNA expression from pure samples of cells, allowing reconstruction of the relative proportions of pure cell types in the population. Application of the method, termed expression deconvolution, to yeast grown under varying conditions reveals the population dynamics of the cells during the cell cycle, during the arrest of cells induced by DNA damage and the release of arrest in a cell cycle checkpoint mutant, during sporulation, and following environmental stress. Using expression deconvolution, cell cycle defects are detected and temporally ordered in 146 yeast deletion mutants; six of these defects are independently experimentally validated. Expression deconvolution allows a reinterpretation of the cell cycle dynamics underlying all previous microarray experiments and can be more generally applied to study most forms of cell population dynamics.
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