ArticleGenome biology2023
Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- lute: estimating the cell composition of heterogeneous tissue with varying cell sizes using gene expression.BMC genomics · 2025Article
- Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex.Genome biology · 2025Article
- Probe set selection for targeted spatial transcriptomics.Nature methods · 2024Article
- Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals immune suppression subtypes and establishes a novel signature for determining the prognosis in lung adenocarcinoma.Cellular oncology (Dordrecht, Netherlands) · 2024Article
- Article
- Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets.Genome biology · 2023Review
- Influence of Alzheimer's disease related neuropathology on local microenvironment gene expression in the human inferior temporal cortex.GEN biotechnology · 2023Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
We define and identify a new class of control genes for next-generation sequencing called total RNA expression genes (TREGs), which correlate with total RNA abundance in cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single-cell RNA sequencing data, allowing the estimation of total amount of RNA when restricted to quantifying a limited number of genes. We demonstrate our method in postmortem human brain using multiplex single-molecule fluorescent in situ hybridization and compare candidate TREGs against classic housekeeping genes. We identify AKT3 as a top TREG across five brain regions.
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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 OpenQuestion graph.