FunChisq (version 2.3.1)

FunChisq-package: Chi-Square and Exact Tests for Non-Parametric Functional Dependencies

Description

Statistical hypothesis testing methods for non-parametric functional dependencies using asymptotic chi-square or exact statistics. These tests were motivated to reveal evidence for causality based on functional dependencies (Simon and Rescher, 1966). The package implements asymptotic functional chi-square tests (Zhang and Song, 2013; Zhang, 2014), an exact functional test (Zhong, 2014), a comparative functional chi-square test (Zhang, 2014), and also a comparative chi-square test (Song et al., 2014; Zhang et al., 2015). The tests require data from two or more variables be formatted as a contingency table. Continuous variables need to be discretized first, for example, using the R package Ckmeans.1d.dp. The normalized functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges (Hill et al., 2016). For continuous data, these tests offer an advantage over regression analysis when a parametric form cannot be reliably assumed for the underlying function. For categorical data, they provide a novel means to assess directional dependencies impossible with classical Pearson's chi-square or Fisher's exact tests.

Details

Package:

FunChisq

Type:

Package

Version:

2.3.1

Initial version:

1.0

Initial date:

2014-03-08

License:

LGPL (>= 3)

References

Hill, S. M., Heiser, L. M., Cokelaer, T., Unger, M., Nesser, N. K., Carlin, D. E., Zhang, Y., Sokolov, A., Paull, E. O., Wong, C. K., Graim, K., Bivol, A., Wang, H., Zhu, F., Afsari, B., Danilova, L. V., Favorov, A. V., Lee, W. S., Taylor, D., Hu, C. W., Long, B. L., Noren, D. P., Bisberg, A. J., HPN-DREAM Consortium, Mills, G. B., Gray, J. W., Kellen, M., Norman, T., Friend, S., Qutub, A. A., Fertig, E. J., Guan, Y., Song, M., Stuart, J. M., Spellman, P. T., Koeppl, H., Stolovitzky, G., Saez-Rodriguez, J. and Mukherjee, S. (2016) Inferring causal molecular networks: empirical assessment through a community-based effort. Nature Methods 13(4), 310-318.

Simon, H. A. and Rescher, N. (1966) Cause and counterfactual. Philosophy of Science 33(4), 323-340.

Song M., Zhang, Y., Katzaroff, A. J., Edgar, B. A. and Buttitta, L. (2014). Hunting complex differential gene interaction patterns across molecular contexts. Nucleic Acids Research 42(7), e57. Retrieved from http://nar.oxfordjournals.org/content/42/7/e57.long

Zhang, Y. (2014) Nonparametric Statistical Methods for Biological Network Inference. Unpublished doctoral dissertation, Department of Computer Science, New Mexico State University, Las Cruces, USA.

Zhang, Y., Liu, Z. L. and Song, M. (2015) ChiNet uncovers rewired transcription subnetworks in tolerant yeast for advanced biofuels conversion. Nucleic Acids Research 43(9), 4393-4407. Retrieved from http://nar.oxfordjournals.org/content/43/9/4393.long

Zhang, Y. and Song, M. (2013) Deciphering interactions in causal networks without parametric assumptions. arXiv Molecular Networks, arXiv:1311.2707, http://arxiv.org/abs/1311.2707

Zhong, H. (2014) An Exact and Fast Statistical Test for Nonparametric Functional Dependencies. Unpublished M.S. thesis, Department of Computer Science, New Mexico State University, Las Cruces, USA.

See Also

The Ckmeans.1d.dp package.