Evidence map›Paper›PMID 38012163›Full record

ArticleScientific reports2023

Benefit of using interaction effects for the analysis of high-dimensional time-response or dose-response data for two-group comparisons.

Julia C Duda, Carolin Drenda, Hue Kästel, Jörg Rahnenführer, Franziska Kappenberg

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.5field-weighted citation impact, top 17% 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

11 citing papers in PubMed, 10 citations in OpenAlex.

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  11. Deciphering the proteome ofComputational and structural biotechnology journal · 2025
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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 at 1 institution in 1 country.

Julia C DudaDepartment of Statistics, TU Dortmund University, Vogelpothsweg 87, 44227, Dortmund, Germany. duda@statistik.tu-dortmund.de.
Carolin DrendaDepartment of Statistics, TU Dortmund University, Vogelpothsweg 87, 44227, Dortmund, Germany.
Hue KästelDepartment of Statistics, TU Dortmund University, Vogelpothsweg 87, 44227, Dortmund, Germany.
Jörg RahnenführerDepartment of Statistics, TU Dortmund University, Vogelpothsweg 87, 44227, Dortmund, Germany.
Franziska KappenbergDepartment of Statistics, TU Dortmund University, Vogelpothsweg 87, 44227, Dortmund, Germany.
TU Dortmund University · DE

Funding

German Research Foundation 427806116
6 · The paper itself

Abstract

High throughput RNA sequencing experiments are widely conducted and analyzed to identify differentially expressed genes (DEGs). The statistical models calculated for this task are often not clear to practitioners, and analyses may not be optimally tailored to the research hypothesis. Often, interaction effects (IEs) are the mathematical equivalent of the biological research question but are not considered for different reasons. We fill this gap by explaining and presenting the potential benefit of IEs in the search for DEGs using RNA-Seq data of mice that receive different diets for different time periods. Using an IE model leads to a smaller, but likely more biologically informative set of DEGs compared to a common approach that avoids the calculation of IEs.

Indexed as

Gene Expression ProfilingTranscriptomeAnimalsHigh-Throughput Nucleotide SequencingMiceRNA-SeqSequence Analysis, RNA

Identifiers

PMID38012163
PMCPMC10682470
OpenAlexW4389049540

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.