Evidence map›Paper›PMID 24764462›Full record

ArticleBioinformatics (Oxford, England)2014

On non-detects in qPCR data.

Matthew N McCall, Helene R McMurray, Hartmut Land, Anthony Almudevar

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
102citing papers in PubMed, 1 pooled it
–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

102 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Replication Study:eLife · 2018
    Pooled it
  2. Trial
  3. Trial
  4. Trial
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  6. Accounting for temporal variation and correlation in environmental DNA sampling can improve ecological inferences.Ecological applications : a publication of the Ecological Society of America · 2026
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  12. The longitudinal expression ofMicrobiology (Reading, England) · 2026
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  14. Development of a TK6-derived cell line expressing four human cytochrome P450s for genotoxicity testing.Toxicology in vitro : an international journal published in association with BIBRA · 2025
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  15. Review
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  19. A simplified MyProstateScore2.0 for high-grade prostate cancer.Cancer biomarkers : section A of Disease markers · 2025
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  20. Article

42 more citing papers are in PubMed but not listed here.

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

4 authors.

Matthew N McCallDepartment of Biostatistics and Computational Biology, Department of Biomedical Genetics and James P Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USA.
Helene R McMurrayDepartment of Biostatistics and Computational Biology, Department of Biomedical Genetics and James P Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USA.
Hartmut LandDepartment of Biostatistics and Computational Biology, Department of Biomedical Genetics and James P Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USADepartment of Biostatistics and Computational Biology, Department of Biomedical Genetics and James P Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USA.
Anthony AlmudevarDepartment of Biostatistics and Computational Biology, Department of Biomedical Genetics and James P Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USA.

Funding

CANCER RESEARCH TRAINING-EXPERIMENTAL THERAPY MODELST32CA009363 · NCI · UNIVERSITY OF ROCHESTER · PI LAND, HARTMUT · 1985 to 2013
$2.9M
Gene Networks Essential to Colon Cancer PhenotypeR01CA138249 · NCI · UNIVERSITY OF ROCHESTER · PI LAND, HARTMUT · 2008 to 2012
$2.6M
Statistical Methods for Estimation of Gene Regulatory NetworksK99HG006853 · NHGRI · UNIVERSITY OF ROCHESTER · PI MCCALL, MATTHEW NICHOLSON · 2013 to 2013
$80k
NCI NIH HHS CA009363NCI NIH HHS CA138249NCI NIH HHS R01 CA138249NCI NIH HHS T32 CA009363NHGRI NIH HHS HG006853NHGRI NIH HHS K99 HG006853
6 · The paper itself

Abstract

motivationQuantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization and statistical analysis, little attention has been given to qPCR non-detects-those reactions failing to produce a minimum amount of signal.

resultsWe show that the common methods of handling qPCR non-detects lead to biased inference. Furthermore, we show that non-detects do not represent data missing completely at random and likely represent missing data occurring not at random. We propose a model of the missing data mechanism and develop a method to directly model non-detects as missing data. Finally, we show that our approach results in a sizeable reduction in bias when estimating both absolute and differential gene expression. AVAILABILITY AND IMPLEMENTATION: The proposed algorithm is implemented in the R package, nondetects. This package also contains the raw data for the three example datasets used in this manuscript. The package is freely available at http://mnmccall.com/software and as part of the Bioconductor project.

Indexed as

AlgorithmsAnimalsGene Expression ProfilingMiceModels, TheoreticalReal-Time Polymerase Chain ReactionSoftware

Identifiers

PMID24764462
PMCPMC4133581

What OpenQuestion holds

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LicenceCC BY-NC
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.