Evidence map›Paper›PMID 36755865›Full record

Trial reportPeerJ2023

Data processing choices can affect findings in differential methylation analyses: an investigation using data from the LIMIT RCT.

Jennie Louise, Andrea R Deussen, Jodie M Dodd

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. What is the vibration of effects?BMJ evidence-based medicine · 2025
    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.

Jennie LouiseDiscipline of Obstetrics & Gynaecology and The Robinson Research Institute, The University of Adelaide, Adelaide, Australia.
Andrea R DeussenDiscipline of Obstetrics & Gynaecology and The Robinson Research Institute, The University of Adelaide, Adelaide, Australia.
Jodie M DoddDiscipline of Obstetrics & Gynaecology and The Robinson Research Institute, The University of Adelaide, Adelaide, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: A wide array of methods exist for processing and analysing DNA methylation data. We aimed to perform a systematic comparison of the behaviour of these methods, using cord blood DNAm from the LIMIT RCT, in relation to detecting hypothesised effects of interest (intervention and pre-pregnancy maternal BMI) as well as effects known to be spurious, and known to be present. Methods: DNAm data, from 645 cord blood samples analysed using Illumina 450K BeadChip arrays, were normalised using three different methods (with probe filtering undertaken pre- or post- normalisation). Batch effects were handled with a supervised algorithm, an unsupervised algorithm, or adjustment in the analysis model. Analysis was undertaken with and without adjustment for estimated cell type proportions. The effects estimated included intervention and BMI (effects of interest in the original study), infant sex and randomly assigned groups. Data processing and analysis methods were compared in relation to number and identity of differentially methylated probes, rankings of probes by p value and log-fold-change, and distributions of Results: There were differences corresponding to each of the processing and analysis choices. Importantly, some combinations of data processing choices resulted in a substantial number of spurious 'significant' findings. We recommend greater emphasis on replication and greater use of sensitivity analyses.

Indexed as

AlgorithmsDNA MethylationFamilyFetal BloodHumansInfantBioinformaticsDifferential methylationDNA methylationReproducibility

Identifiers

PMID36755865
PMCPMC9901304

What OpenQuestion holds

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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.