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Bioinformatics. 2016 Aug 1;32(15):2364-5. doi: 10.1093/bioinformatics/btw135. Epub 2016 Mar 9.

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

Author information

1
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA.

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.

PMID:
27153715
PMCID:
PMC4965632
DOI:
10.1093/bioinformatics/btw135
[Indexed for MEDLINE]
Free PMC Article

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