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This study introduces a new statistical model to assess the reproducibility and replicability of scientific findings from multiple experiments. The method helps identify discordance sources, enhancing the reliability of scientific discoveries.

Keywords:
copulahigh‐throughput experimentirreproducible discovery ratemixture modelsreplicabilityreproducibility

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Area of Science:

  • Biostatistics
  • Genomics
  • Computational Biology

Background:

  • Scientific discovery relies on reproducible and replicable high-throughput experiments.
  • Quantifying reproducibility and identifying sources of irreproducibility are critical for data quality control and integration.

Purpose of the Study:

  • To introduce a novel statistical model for measuring reproducibility and replicability in multi-source studies.
  • To provide a coherent framework for quantifying these measures simultaneously for each candidate finding.

Main Methods:

  • Development of a nested copula mixture model.
  • Characterization of interdependence between replication experiments across and within sources.
  • Application to simulation studies, ENCODE ChIP-seq, and SEQC RNA-seq datasets.

Main Results:

  • The proposed method effectively quantifies reproducibility and replicability.
  • The model successfully diagnoses sources of discordance in experimental findings.
  • Demonstrated improvement in the reliability of scientific discoveries.

Conclusions:

  • The novel statistical model offers a robust approach to assessing scientific findings' reliability.
  • This method aids in identifying and addressing issues leading to irreproducibility.
  • Enhances confidence in scientific discoveries derived from high-throughput experiments.