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The projack: a resampling approach to correct for ranking bias in high-throughput studies.

ABSTRACT: The problem of ranked inference arises in a number of settings, for which the investigator wishes to perform parameter inference after ordering a set of [Formula: see text] statistics. In contrast to inference for a single hypothesis, the ranking procedure introduces considerable bias, a problem known as the "winner's curse" in genetic association. We introduce the projack (for Prediction by Re- Ordered Jackknife and Cross-Validation, [Formula: see text]-fold). The projack is a resampling-based procedure that provides low-bias estimates of the expected ranked effect size parameter for a set of possibly correlated [Formula: see text] statistics. The approach is flexible, and has wide applicability to high-dimensional datasets, including those arising from genomics platforms. Initially, motivated for the setting where original data are available for resampling, the projack can be extended to the situation where only the vector of [Formula: see text] values is available. We illustrate the projack for correction of the winner's curse in genetic association, although it can be used much more generally.

SUBMITTER: Zhou YH

PROVIDER: S-EPMC4679068 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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The projack: a resampling approach to correct for ranking bias in high-throughput studies.

Zhou Yi-Hui YH Wright Fred A FA

Biostatistics (Oxford, England) 20150603 1

The problem of ranked inference arises in a number of settings, for which the investigator wishes to perform parameter inference after ordering a set of [Formula: see text] statistics. In contrast to inference for a single hypothesis, the ranking procedure introduces considerable bias, a problem known as the "winner's curse" in genetic association. We introduce the projack (for Prediction by Re- Ordered Jackknife and Cross-Validation, [Formula: see text]-fold). The projack is a resampling-based ...[more]

PMID: 26040912

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Project description:Because of concerns about the safety of a growing number of engineered nanomaterials (ENM), it is necessary to develop high-throughput screening and in silico data transformation tools that can speed up in vitro hazard ranking. Here, we report the use of a multiparametric, automated screening assay that incorporates sublethal and lethal cellular injury responses to perform high-throughput analysis of a batch of commercial metal/metal oxide nanoparticles (NP) with the inclusion of a quantum dot (QD1). The responses chosen for tracking cellular injury through automated epifluorescence microscopy included ROS production, intracellular calcium flux, mitochondrial depolarization, and plasma membrane permeability. The z-score transformed high volume data set was used to construct heat maps for in vitro hazard ranking as well as showing the similarity patterns of NPs and response parameters through the use of self-organizing maps (SOM). Among the materials analyzed, QD1 and nano-ZnO showed the most prominent lethality, while Pt, Ag, SiO2, Al2O3, and Au triggered sublethal effects but without cytotoxicity. In order to compare the in vitro with the in vivo response outcomes in zebrafish embryos, NPs were used to assess their impact on mortality rate, hatching rate, cardiac rate, and morphological defects. While QDs, ZnO, and Ag induced morphological abnormalities or interfered in embryo hatching, Pt and Ag exerted inhibitory effects on cardiac rate. Ag toxicity in zebrafish differed from the in vitro results, which is congruent with this material's designation as extremely dangerous in the environment. Interestingly, while toxicity in the initially selected QD formulation was due to a solvent (toluene), supplementary testing of additional QDs selections yielded in vitro hazard profiling that reflect the release of chalcogenides. In conclusion, the use of a high-throughput screening, in silico data handling and zebrafish testing may constitute a paradigm for rapid and integrated ENM toxicological screening.

Dataset Information

The projack: a resampling approach to correct for ranking bias in high-throughput studies.

Publications

The projack: a resampling approach to correct for ranking bias in high-throughput studies.

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