Dataset Information

Cit: hypothesis testing software for mediation analysis in genomic applications.

ABSTRACT:

Motivation

The challenges of successfully applying causal inference methods include: (i) satisfying underlying assumptions, (ii) limitations in data/models accommodated by the software and (iii) low power of common multiple testing approaches.

Results

The causal inference test (CIT) is based on hypothesis testing rather than estimation, allowing the testable assumptions to be evaluated in the determination of statistical significance. A user-friendly software package provides P-values and optionally permutation-based FDR estimates (q-values) for potential mediators. It can handle single and multiple binary and continuous instrumental variables, binary or continuous outcome variables and adjustment covariates. Also, the permutation-based FDR option provides a non-parametric implementation.

Conclusion

Simulation studies demonstrate the validity of the cit package and show a substantial advantage of permutation-based FDR over other common multiple testing strategies.

Availability and implementation

The cit open-source R package is freely available from the CRAN website (https://cran.r-project.org/web/packages/cit/index.html) with embedded C?++?code that utilizes the GNU Scientific Library, also freely available (http://www.gnu.org/software/gsl/).

Contact

joshua.millstein@usc.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Millstein J

PROVIDER: S-EPMC4965632 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Publications

cit: hypothesis testing software for mediation analysis in genomic applications.

Millstein Joshua J Chen Gary K GK Breton Carrie V CV

Bioinformatics (Oxford, England) 20160309 15

<h4>Motivation</h4>The challenges of successfully applying causal inference methods include: (i) satisfying underlying assumptions, (ii) limitations in data/models accommodated by the software and (iii) low power of common multiple testing approaches.<h4>Results</h4>The causal inference test (CIT) is based on hypothesis testing rather than estimation, allowing the testable assumptions to be evaluated in the determination of statistical significance. A user-friendly software package provides P-va ...[more]

PMID: 27153715

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Project description:BACKGROUND: In complex large-scale experiments, in addition to simultaneously considering a large number of features, multiple hypotheses are often being tested for each feature. This leads to a problem of multi-dimensional multiple testing. For example, in gene expression studies over ordered categories (such as time-course or dose-response experiments), interest is often in testing differential expression across several categories for each gene. In this paper, we consider a framework for testing multiple sets of hypothesis, which can be applied to a wide range of problems. RESULTS: We adopt the concept of the overall false discovery rate (OFDR) for controlling false discoveries on the hypothesis set level. Based on an existing procedure for identifying differentially expressed gene sets, we discuss a general two-step hierarchical hypothesis set testing procedure, which controls the overall false discovery rate under independence across hypothesis sets. In addition, we discuss the concept of the mixed-directional false discovery rate (mdFDR), and extend the general procedure to enable directional decisions for two-sided alternatives. We applied the framework to the case of microarray time-course/dose-response experiments, and proposed three procedures for testing differential expression and making multiple directional decisions for each gene. Simulation studies confirm the control of the OFDR and mdFDR by the proposed procedures under independence and positive correlations across genes. Simulation results also show that two of our new procedures achieve higher power than previous methods. Finally, the proposed methodology is applied to a microarray dose-response study, to identify 17 ?-estradiol sensitive genes in breast cancer cells that are induced at low concentrations. CONCLUSIONS: The framework we discuss provides a platform for multiple testing procedures covering situations involving two (or potentially more) sources of multiplicity. The framework is easy to use and adaptable to various practical settings that frequently occur in large-scale experiments. Procedures generated from the framework are shown to maintain control of the OFDR and mdFDR, quantities that are especially relevant in the case of multiple hypothesis set testing. The procedures work well in both simulations and real datasets, and are shown to have better power than existing methods.