highTtest-class {highTtest}

Class "highTtest"

Description

Value object returned by call to highTtest().

Objects from the Class

This object should not be created by users.

Slots

CK:

Object of class matrix or NULL. A matrix of logical values. The rows correspond to features, ordered as provided in input dataSet1. The columns correspond to levels of significance. Matrix elements are TRUE if feature was determined to be significant by the Cao-Kosorok method. The significance value associated with each column is dictated by the input gammas.

pi1:

Object of class numeric or NULL. The estimated proportion of alternative hypotheses calculated using the Cao-Kosorok method.

pvalue:

Object of class numeric. The vector of p-values calculated using the two-sample t-statistic.

ST:

Object of class matrix or NULL. If requested, a matrix of logical values. The rows correspond to features, ordered as provided in input dataSet1. The columns correspond to levels of significance. Matrix elements are TRUE if feature was determined to be significant by the Storey-Tibshirani (2003) method. The significance value associated with each column is dictated by the input gammas.

BH:

Object of class matrix or NULL If requested, A matrix of logical values. The rows correspond to features, ordered as provided in input dataSet1. The columns correspond to levels of significance. Matrix elements are TRUE if feature was determined to be significant by the Benjamini-Hochberg (1995) method. The significance value associated with each column is dictated by the input gammas.

gammas:

Object of class numeric. Vector of significant values provided as input for the calculation.

Methods

BH

signature(x = "highTtest"): Retrieves a matrix of logical values. The rows correspond to features, the columns to levels of significance. Matrix elements are TRUE if feature was determined to be significant by the Benjamini-Hochberg (1995) method.

CK

signature(x = "highTtest"): Retrieves a matrix of logical values. The rows correspond to features, the columns to levels of significance. Matrix elements are TRUE if feature was determined to be significant by the Cao-Kosorok (2011) method.

pi_alt

signature(x = "highTtest"): Retrieves the estimated proportion of alternative hypotheses obtained by the Cao-Kosorok (2011) method.

plot

signature(x = "highTtest"): Generates a plot of the number of significant features as a function of the level of significance as calculated for each method (CK,BH, and/or ST)

pvalue

signature(x = "highTtest"): Retrieves the vector of p-values calculated using the two-sample t-statistic.

ST

signature(x = "highTtest"): Retrieves a matrix of logical values. The rows correspond to features, the columns to levels of significance. Matrix elements are TRUE if feature was determined to be significant by the Storey-Tibshirani (2003) method.

vennD

signature(x = "highTtest"): Generates two- and three-dimensional Venn diagrams comparing the features selected by each method. Implements methods of package colorfulVennPlot. In addition to the highTtest object, the level of significance, gamma, must also be provided.

Author(s)

Authors: Hongyuan Cao, Michael R. Kosorok, and Shannon T. Holloway <[e-mail]> Maintainer: Shannon T. Holloway <[e-mail]>

References

Cao, H. and Kosorok, M. R. (2011). Simultaneous critical values for t-tests in very high dimensions. Bernoulli, 17, 347–394. PMCID: PMC3092179.

Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57, 289–300.

Storey, J. and Tibshirani, R. (2003). Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences, USA, 100, 9440–9445.

Examples

showClass("highTtest")