Evidence map›Paper›PMID 38238687›Full record

SynthesisBMC genomics2024

PancrESS - a meta-analysis resource for understanding cell-type specific expression in the human pancreas.

David Sturgill, Li Wang, H Efsun Arda

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. International journal of molecular sciences · 2025
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

David SturgillLaboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA.
Li WangLaboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA.
H Efsun ArdaLaboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA. efsun.arda@nih.gov.
Center for Cancer Research · US

Funding

Identification of genomic regulatory elements in pancreas cellsZIABC011798 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI ARDA, HATICE · 2018 to 2025
$10.5M
Intramural NIH HHS ZIA BC011798NIH HHS ZIA BC011798
6 · The paper itself

Abstract

backgroundThe human pancreas is composed of specialized cell types producing hormones and enzymes critical to human health. These specialized functions are the result of cell type-specific transcriptional programs which manifest in cell-specific gene expression. Understanding these programs is essential to developing therapies for pancreatic disorders. Transcription in the human pancreas has been widely studied by single-cell RNA technologies, however the diversity of protocols and analysis methods hinders their interpretability in the aggregate.

resultsIn this work, we perform a meta-analysis of pancreatic single-cell RNA sequencing data. We present a database for reference transcriptome abundances and cell-type specificity metrics. This database facilitates the identification and definition of marker genes within the pancreas. Additionally, we introduce a versatile tool which is freely available as an R package, and should permit integration into existing workflows. Our tool accepts count data files generated by widely-used single-cell gene expression platforms in their original format, eliminating an additional pre-formatting step. Although we designed it to calculate expression specificity of pancreas cell types, our tool is agnostic to the biological source of count data, extending its applicability to other biological systems.

conclusionsOur findings enhance the current understanding of expression specificity within the pancreas, surpassing previous work in terms of scope and detail. Furthermore, our database and tool enable researchers to perform similar calculations in diverse biological systems, expanding the applicability of marker gene identification and facilitating comparative analyses.

Indexed as

Pancreatic DiseasesSoftwareGene Expression ProfilingHumansPancreasSequence Analysis, RNASingle-Cell AnalysisTranscriptomeExpression specificityMeta-analysisPancreasscRNA-Seq

Identifiers

PMID38238687
PMCPMC10797729
OpenAlexW4390972410

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.