Evidence map›Paper›PMID 28800759›Full record

ArticleBMC systems biology2017

Quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments.

Wenfei Zhang, Ying Liu, Mindy Zhang, Cheng Zhu, Yuefeng Lu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC systems biology, 2017. 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
–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

2 citing papers in PubMed.

  1. Article
  2. 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.

Wenfei ZhangSanofi, Framingham, MA, USA. wenfei.zhang@sanofi.com.
Ying LiuDepartment of Biostatistics, Columbia University, New York, NY, USA.
Mindy ZhangSanofi, Framingham, MA, USA.
Cheng ZhuSanofi, Framingham, MA, USA.
Yuefeng LuSanofi, Framingham, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh-throughput assays are widely used in biological research to select potential targets. One single high-throughput experiment can efficiently study a large number of candidates simultaneously, but is subject to substantial variability. Therefore it is scientifically important to performance quantitative reproducibility analysis to identify reproducible targets with consistent and significant signals across replicate experiments. A few methods exist, but all have limitations.

methodsIn this paper, we propose a new method for identifying reproducible targets. Considering a Bayesian hierarchical model, we show that the test statistics from replicate experiments follow a mixture of multivariate Gaussian distributions, with the one component with zero-mean representing the irreproducible targets.

resultsA target is thus classified as reproducible or irreproducible based on its posterior probability belonging to the reproducible components. We study the performance of our proposed method using simulations and a real data example.

conclusionThe proposed method is shown to have favorable performance in identifying reproducible targets compared to other methods.

Indexed as

Models, BiologicalOligonucleotide Array Sequence AnalysisBayes TheoremBayesian classificationEM algorithmEmpirical BayesGaussian mixtureHigh-throughput experimentReproducibility

Identifiers

PMID28800759
PMCPMC5553769

What OpenQuestion holds

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LicenceCC BY
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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.