Evidence map›Paper›PMID 38464974›Full record

ArticleBioinformatics advances2024

Making mouse transcriptomics deconvolution accessible with immunedeconv.

Lorenzo Merotto, Gregor Sturm, Alexander Dietrich, Markus List, Francesca Finotello

Abstract read
In one paragraph

Article in Bioinformatics advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

  1. Article
  2. Diagnostics (Basel, Switzerland) · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Lesional senescent CD4Frontiers in immunology · 2024
    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

5 authors.

Lorenzo MerottoDepartment of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, Innsbruck 6020, Austria.
Gregor SturmBiocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck 6020, Austria.
Alexander DietrichData Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany.
Markus ListData Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany.ORCID https://orcid.org/0000-0002-0941-4168
Francesca FinotelloDepartment of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, Innsbruck 6020, Austria.ORCID https://orcid.org/0000-0003-0712-4658

Funding

Austrian Science Fund FWF T 974
6 · The paper itself

Abstract

Summary: Transcriptome deconvolution has emerged as a reliable technique to estimate cell-type abundances from bulk RNA sequencing data. Unlike their human equivalents, methods to quantify the cellular composition of complex tissues from murine transcriptomics are sparse and sometimes not easy to use. We extended the immunedeconv R package to facilitate the deconvolution of mouse transcriptomics, enabling the quantification of murine immune-cell types using 13 different methods. Through immunedeconv, we further offer the possibility of tweaking cell signatures used by deconvolution methods, providing custom annotations tailored for specific cell types and tissues. These developments strongly facilitate the study of the immune-cell composition of mouse models and further open new avenues in the investigation of the cellular composition of other tissues and organisms. Availability and implementation: The R package and the documentation are available at https://github.com/omnideconv/immunedeconv.

Identifiers

PMID38464974
PMCPMC10924280

What Socratic 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 Socratic graph.