Addressing reproducibility in single-laboratory phenotyping experiments [Supplementary Materials]

There has been a growing concern
that preclinical research is failing to replicate experimental results. The
problem is especially relevant to phenotyping genetically engineered mouse
lines, a central strategy for discovering mammalian gene function and developing
animal models of disease. We introduce a statistical method to estimate whether
a single-laboratory discovery is likely to replicate in other laboratories,
based on previously-estimated variability of genotype × laboratory interaction
in large phenotyping databases. Future single-lab experiments can be used to
further update the estimation, making the proposed method a true community
effort. We validate the method by combining several datasets into the most
inclusive analysis conducted to date of across-laboratory replication in mouse
phenotyping, and estimate that with current single laboratory analysis 19%–41%
of non-replicable phenotypic differences between genotypes are still published
as “discoveries”. Applying our proposed method would reduce this rate to
3.3%–9%, close to the intended 0.05, both for testing and confidence intervals.

This fileset includes the analysis results, the R and Sweave scripts to reproduce them, the input datasets, output tables with the estimated variances, the phenotypic measures transformations  and the paper figure's source data.