ArticleBioinformatics (Oxford, England)2010
Probabilistic analysis of gene expression measurements from heterogeneous tissues.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Comparative evaluation of reference-free transcriptomic deconvolution highlights the importance of biological validation in astrocytes across Alzheimer's disease.Frontiers in bioinformatics · 2026Article
- An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data.Nature communications · 2025Article
- Exploring Prognostic Immune Microenvironment-Related Genes in Head and Neck Squamous Cell Carcinoma from the TCGA Database.Journal of Cancer · 2024Article
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- Tumor microenvironment: barrier or opportunity towards effective cancer therapy.Journal of biomedical science · 2022Review
- Estimating cell type-specific differential expression using deconvolution.Briefings in bioinformatics · 2022Article
- CDSeqR: fast complete deconvolution for gene expression data from bulk tissues.BMC bioinformatics · 2021Article
- Nonlinear ridge regression improves cell-type-specific differential expression analysis.BMC bioinformatics · 2021Article
- stochprofML: stochastic profiling using maximum likelihood estimation in R.BMC bioinformatics · 2021Article
- Computational deconvolution to estimate cell type-specific gene expression from bulk data.NAR genomics and bioinformatics · 2021Article
- Deconvolution of heterogeneous tumor samples using partial reference signals.PLoS computational biology · 2020Article
- Article
- DeconPeaker, a Deconvolution Model to Identify Cell Types Based on Chromatin Accessibility in ATAC-Seq Data of Mixture Samples.Frontiers in genetics · 2020Article
- CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data.PLoS computational biology · 2019Article
- Development of a predictive model for stromal content in prostate cancer samples to improve signature performance.The Journal of pathology · 2019Article
- Dissecting differential signals in high-throughput data from complex tissues.Bioinformatics (Oxford, England) · 2019Article
- Integrative analysis identifies lincRNAs up- and downstream of neuroblastoma driver genes.Scientific reports · 2019Article
- Article
- Deblender: a semi-/unsupervised multi-operational computational method for complete deconvolution of expression data from heterogeneous samples.BMC bioinformatics · 2018Article
- Enter the Matrix: Factorization Uncovers Knowledge from Omics.Trends in genetics : TIG · 2018Review
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Authors and funding
6 authors.
Funding
Abstract
motivationTissue heterogeneity, arising from multiple cell types, is a major confounding factor in experiments that focus on studying cell types, e.g. their expression profiles, in isolation. Although sample heterogeneity can be addressed by manual microdissection, prior to conducting experiments, computational treatment on heterogeneous measurements have become a reliable alternative to perform this microdissection in silico. Favoring computation over manual purification has its advantages, such as time consumption, measuring responses of multiple cell types simultaneously, keeping samples intact of external perturbations and unaltered yield of molecular content.
resultsWe formalize a probabilistic model, DSection, and show with simulations as well as with real microarray data that DSection attains increased modeling accuracy in terms of (i) estimating cell-type proportions of heterogeneous tissue samples, (ii) estimating replication variance and (iii) identifying differential expression across cell types under various experimental conditions. As our reference we use the corresponding linear regression model, which mirrors the performance of the majority of current non-probabilistic modeling approaches. AVAILABILITY AND SOFTWARE: All codes are written in Matlab, and are freely available upon request as well as at the project web page http://www.cs.tut.fi/∼erkkila2/. Furthermore, a web-application for DSection exists at http://informatics.systemsbiology.net/DSection. CONTACT: timo.p.erkkila@tut.fi; harri.lahdesmaki@tut.fi
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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.