ArticleBMC bioinformatics2011
Cell subset prediction for blood genomic studies.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Mycophenolate Mofetil Treatment of Systemic Sclerosis Reduces Myeloid Cell Numbers and Attenuates the Inflammatory Gene Signature in Skin.The Journal of investigative dermatology · 2018Trial
- Immunological Profile in Atypical Kawasaki Disease: A Case Report Highlighting the Diagnostic Utility of Cytokine Analysis by qRT-PCR.Pediatric reports · 2025Article
- Whole-Transcriptome Analysis Reveals the Regulatory Network of Immune Response in Dapulian Pig.Animals : an open access journal from MDPI · 2024Article
- Changes in inflammatory responses and autophagy during apheresis platelet preservation and their correlation with platelet transfusion refractoriness in patients with acute lymphoblastic leukemia.Biomolecules & biomedicine · 2023Article
- Interferon-λ3 rs12979860 can regulate inflammatory cytokines production in pulmonary fibrosis.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2023Article
- miR-146a-5p Promotes the Inflammatory Response in PBMCs Induced by Microcystin-Leucine-Arginine.Journal of inflammation research · 2023Article
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- Integrated investigation of DNA methylation, gene expression and immune cell population revealed immune cell infiltration associated with atherosclerotic plaque formation.BMC medical genomics · 2022Article
- The Gene Expression Deconvolution Interactive Tool (GEDIT): accurate cell type quantification from gene expression data.GigaScience · 2021Article
- Lower peripheral helper T cell levels in the synovium are associated with a better response to anti-TNF therapy in rheumatoid arthritis.Arthritis research & therapy · 2020Article
- alona: a web server for single-cell RNA-seq analysis.Bioinformatics (Oxford, England) · 2020Article
- Association Analysis of Peripheral and CSF Biomarkers in Late Mild Cognitive Impairment.Frontiers in genetics · 2020Article
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- Transcriptome Profiling and Molecular Therapeutic Advances in Cystic Fibrosis: Recent Insights.Genes · 2019Review
- Allergen-induced activation of natural killer cells represents an early-life immune response in the development of allergic asthma.The Journal of allergy and clinical immunology · 2018Article
- Metabolic pathways and immunometabolism in rare kidney diseases.Annals of the rheumatic diseases · 2018Article
- A new method for constructing tumor specific gene co-expression networks based on samples with tumor purity heterogeneity.Bioinformatics (Oxford, England) · 2018Article
- Fetal-sex dependent genomic responses in the circulating lymphocytes of arsenic-exposed pregnant women in New Hampshire.Reproductive toxicology (Elmsford, N.Y.) · 2017Article
- The mechanistic implications of gene expression studies in SSc: Insights from Systems Biology.Current treatment options in rheumatology · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundGenome-wide transcriptional profiling of patient blood samples offers a powerful tool to investigate underlying disease mechanisms and personalized treatment decisions. Most studies are based on analysis of total peripheral blood mononuclear cells (PBMCs), a mixed population. In this case, accuracy is inherently limited since cell subset-specific differential expression of gene signatures will be diluted by RNA from other cells. While using specific PBMC subsets for transcriptional profiling would improve our ability to extract knowledge from these data, it is rarely obvious which cell subset(s) will be the most informative.
resultsWe have developed a computational method (Subset Prediction from Enrichment Correlation, SPEC) to predict the cellular source for a pre-defined list of genes (i.e. a gene signature) using only data from total PBMCs. SPEC does not rely on the occurrence of cell subset-specific genes in the signature, but rather takes advantage of correlations with subset-specific genes across a set of samples. Validation using multiple experimental datasets demonstrates that SPEC can accurately identify the source of a gene signature as myeloid or lymphoid, as well as differentiate between B cells, T cells, NK cells and monocytes. Using SPEC, we predict that myeloid cells are the source of the interferon-therapy response gene signature associated with HCV patients who are non-responsive to standard therapy.
conclusionsSPEC is a powerful technique for blood genomic studies. It can help identify specific cell subsets that are important for understanding disease and therapy response. SPEC is widely applicable since only gene expression profiles from total PBMCs are required, and thus it can easily be used to mine the massive amount of existing microarray or RNA-seq data.
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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.