| Title: | Simultaneous Critical Values for t-Tests in Very High Dimensions |
|---|---|
| Description: | Implements the method developed by Cao and Kosorok (2011) for the significance analysis of thousands of features in high-dimensional biological studies. It is an asymptotically valid data-driven procedure to find critical values for rejection regions controlling the k-familywise error rate, false discovery rate, and the tail probability of false discovery proportion. |
| Authors: | Hongyuan Cao [aut], Michael Kosorok [aut], Shannon T. Holloway [aut, cre] |
| Maintainer: | Shannon T. Holloway <[email protected]> |
| License: | GPL-2 |
| Version: | 1.3 |
| Built: | 2024-11-16 02:47:16 UTC |
| Source: | https://github.com/cran/highTtest |
Implements the method developed by Cao and Kosorok (2011) for the significance analysis of thousands of features in high-dimensional biological studies. It is an asymptotically valid data-driven procedure to find critical values for rejection regions controlling the k-familywise error rate, false discovery rate, and the tail probability of false discovery proportion.
highTtest(dataSet1, dataSet2, gammas, compare = "BOTH", cSequence = NULL, tSequence = NULL)highTtest(dataSet1, dataSet2, gammas, compare = "BOTH", cSequence = NULL, tSequence = NULL)
dataSet1 |
data.frame or matrix containing the dataset for subset 1 for the two-sample t-test. |
dataSet2 |
data.frame or matrix containing the dataset for subset 2 for the two-sample t-test. |
gammas |
vector of significance levels at which feature significance is to be determined. |
compare |
one of ("ST", "BH", "Both", "None"). In addition to the Cao-Kosorok method, obtain feature significance indicators using the Storey-Tibshirani method (ST) (Storey and Tibshirani, 2003), the Benjamini-Hochberg method (BH), (Benjamini andHochberg, 1995), "both" the ST and the BH methods, or do not consider alternative methods (none). |
cSequence |
A vector specifying the values of c to be considered in estimating the proportion of alternative hypotheses. If no vector is provided, a default of seq(0.01,6,0.01) is used. See Section 2.3 of Cao and Kosorok (2011) for more information. |
tSequence |
A vector specifying the search space for the critical t value. If no vector is provided, a default of seq(0.01,6,0.01) is used. |
The Storey-Tibshirani (2003), ST, method implemented in highTtest is adapted from the implementation written by Alan Dabney and John D. Storey and available from
http://www.bioconductor.org/packages/release/bioc/html/qvalue.html.
The comparison capability is included only for convenience and reproducibility of the original manuscript. For a complete analysis based on the ST method, the user is referred to the qvalue package available through the bioconductor archive.
The following methods retrieve individual results from a highTtest object, x:
BH(x):
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(x):
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(x): Retrieves the
estimated proportion of alternative hypotheses
obtained by the Cao-Kosorok (2011) method.
pvalue(x): Retrieves the
vector of p-values calculated using the
two-sample t-statistic.
ST(x):
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.
A simple x-y plot comparing the number of significant features as a function of the level significance level can be generated using
plot(x,...): 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). Additional plot controls can be passed through the ellipsis.
When comparisons to the ST and BH methods are requested, Venn diagrams can be generated using provided that package colorfulVennPlot is installed.
vennD(x, gamma, ...): 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. Most control argument of the
colorfulVennPlot package can be passed through the ellipsis.
Returns an object of class highTtest.
Authors: Hongyuan Cao, Michael R. Kosorok, and Shannon T. Holloway <[email protected]> Maintainer: Shannon T. Holloway <[email protected]>
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.
set.seed(123) x1 <- matrix(c(runif(500),runif(500,0.25,1)),nrow=100) obj <- highTtest(dataSet1=x1[,1:5], dataSet2=x1[,6:10], gammas=seq(0.1,1,0.1), tSequence=seq(0.001,3,0.001)) #Print number of significant features identified in each method colSums(CK(obj)) colSums(ST(obj)) colSums(BH(obj)) #Plot the number of significant features identified in each method plot(obj, main="Example plot") ltry <- try(library(colorfulVennPlot),silent=TRUE) if( !is(ltry,"try-error") ) vennD(obj, 0.8, Title="Example vennD") #Proportion of alternative hypotheses pi_alt(obj) #p-values pvalue(obj)set.seed(123) x1 <- matrix(c(runif(500),runif(500,0.25,1)),nrow=100) obj <- highTtest(dataSet1=x1[,1:5], dataSet2=x1[,6:10], gammas=seq(0.1,1,0.1), tSequence=seq(0.001,3,0.001)) #Print number of significant features identified in each method colSums(CK(obj)) colSums(ST(obj)) colSums(BH(obj)) #Plot the number of significant features identified in each method plot(obj, main="Example plot") ltry <- try(library(colorfulVennPlot),silent=TRUE) if( !is(ltry,"try-error") ) vennD(obj, 0.8, Title="Example vennD") #Proportion of alternative hypotheses pi_alt(obj) #p-values pvalue(obj)
"highTtest"
Value object returned by call to highTtest().
This object should not be created by users.
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.
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.
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.
signature(x = "highTtest"): Retrieves the
estimated proportion of alternative hypotheses
obtained by the Cao-Kosorok (2011) method.
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)
signature(x = "highTtest"): Retrieves the
vector of p-values calculated using the
two-sample t-statistic.
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.
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.
Authors: Hongyuan Cao, Michael R. Kosorok, and Shannon T. Holloway <[email protected]> Maintainer: Shannon T. Holloway <[email protected]>
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.
showClass("highTtest")showClass("highTtest")
plot ~~Generates a simple x-y plot giving the number of significant features as a function of the level of significance. If comparisons to Storey-Tibshirani and Benjamini-Hochberg methods were requested by the user, these will automatically be included in the plot.
signature(x = "ANY")Plot method as implemented by other packages.
signature(x = "highTtest")Object returned by a call to highTtest().
vennD ~~Generates 2- or 3-dimensional Venn diagrams comparing the
features selected by the Cao-Kosorok method to those selected
by the Storey-Tibshirani (2003) method
and/or the Benjamini-Hoshberg (1995) method.
This S4 method is simply a wrapper
for plotVenn2d() and plotVenn3d() of
package colorfulVennPlot.
signature(x = "highTtest", gamma="numeric", ...)Object returned by a call to highTtest().
gamma is the level of significance. Additional control variables
for the methods of plotVenn2d() and plotVenn3d() of
package colorfulVennPlot can be passed through the ellipsis.