ArticlePLoS computational biology2016
Exploiting Gene-Expression Deconvolution to Probe the Genetics of the Immune System.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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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
7 citing papers in PubMed.
- Review
- IFITM3 deficiency drives SARS-CoV-2 adaptation while preserving variant-specific traits.Nature communications · 2026Article
- Cytotoxicity and Autophagy Induced by Ivermectin via AMPK/mTOR Signaling Pathway in RAW264.7 Cells.Molecules (Basel, Switzerland) · 2023Article
- RORγt inhibitors block both IL-17 and IL-22 conferring a potential advantage over anti-IL-17 alone to treat severe asthma.Respiratory research · 2021Article
- Characterization of disease-specific cellular abundance profiles of chronic inflammatory skin conditions from deconvolution of biopsy samples.BMC medical genomics · 2019Article
- BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference.Genome biology · 2018Article
- Cell-type deconvolution from DNA methylation: a review of recent applications.Human molecular genetics · 2017Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sequence variation can affect the physiological state of the immune system. Major experimental efforts targeted at understanding the genetic control of the abundance of immune cell subpopulations. However, these studies are typically focused on a limited number of immune cell types, mainly due to the use of relatively low throughput cell-sorting technologies. Here we present an algorithm that can reveal the genetic basis of inter-individual variation in the abundance of immune cell types using only gene expression and genotyping measurements as input. Our algorithm predicts the abundance of immune cell subpopulations based on the RNA levels of informative marker genes within a complex tissue, and then provides the genetic control on these predicted immune traits as output. A key feature of the approach is the integration of predictions from various sets of marker genes and refinement of these sets to avoid spurious signals. Our evaluation of both synthetic and real biological data shows the significant benefits of the new approach. Our method, VoCAL, is implemented in the freely available R package ComICS.
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Registered trials
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