Evidence map›Paper›PMID 33987226›Full record

ArticleAnnals of translational medicine2021

countfitteR: efficient selection of count distributions to assess DNA damage.

Jarosław Chilimoniuk, Alicja Gosiewska, Jadwiga Słowik, Romano Weiss, P Markus Deckert, Stefan Rödiger, Michał Burdukiewicz

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.5field-weighted citation impact, top 14% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Challenges and opportunities in processing NanoString nCounter data.Computational and structural biotechnology journal · 2024
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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

7 authors at 5 institutions in 2 countries.

Jarosław ChilimoniukDepartment of Bioinformatics and Genomics, Faculty of Biotechnology, University of Wrocław, Wrocław, Poland.
Alicja GosiewskaFaculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.
Jadwiga SłowikFaculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.
Romano WeissFaculty of Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany.
P Markus DeckertFaculty of Medicine and Psychology, Brandenburg Medical School Theodor Fontane, and Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, Brandenburg, Germany.
Stefan RödigerFaculty of Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany.
Michał BurdukiewiczFaculty of Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany.
Brandenburg University of Technology Cottbus-Senftenberg · DEWarsaw University of Technology · PLMedical University of Białystok · PLMedizinische Hochschule Brandenburg Theodor Fontane · DEUniversity of Wrocław · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDNA double-strand breaks can be counted as discrete foci by imaging techniques. In personalized medicine and pharmacology, the analysis of counting data is relevant for numerous applications, e.g., for cancer and aging research and the evaluation of drug efficacy. By default, it is assumed to follow the Poisson distribution. This assumption, however, may lead to biased results and faulty conclusions in datasets with excess zero values (zero-inflation), a variance larger than the mean (overdispersion), or both. In such cases, the assumption of a Poisson distribution would skew the estimation of mean and variance, and other models like the negative binomial (NB), zero-inflated Poisson or zero-inflated NB distributions should be employed. The model chosen has an influence on the parameter estimation (mean value and confidence interval). Yet the choice of the suitable distribution model is not trivial.

methodsTo support, simplify and objectify this process, we have developed the countfitteR software as an R package. We used a Bayesian approach for distribution model selection and the shiny web application framework for interactive data analysis.

resultsWe show the application of our software based on examples of DNA double-strand break count data from phenotypic imaging by multiplex fluorescence microscopy. In analyzing numerous datasets of molecular pharmacological markers (phosphorylated histone H2AX and p53 binding protein), countfitteR demonstrated an equal or superior statistical performance compared to the usually employed two-step procedure, with an overall power of up to 98%. In addition, it still gave information in cases with no result at all from the two-step procedure. In our data sample we found that the NB distribution was the most frequent, with the Poisson distribution taking second place.

conclusionscountfitteR can perform an automated distribution model selection and thus support the data analysis and lead to objective statistically verifiable estimated values. Originally designed for the analysis of foci in biomedical image data, countfitteR can be used in a variety of areas where non-Poisson distributed counting data is prevalent.

Indexed as

count dataDNA damageoverdispersionzero-inflation

Identifiers

PMID33987226
PMCPMC8105836
OpenAlexW3133816347

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.