IsoRawp

Description

The function calculates the raw one-sided and two-sided p-values for each test statistic using permutations.

Usage

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IsoRawp(x, y, niter, progressBar=TRUE)

Arguments

x

numeric vector containing the dose levels

y

a data frame of the gene expression with Probe IDs as row names

niter

number of permutations to use

progressBar

to enable or disable progress bar; default is TRUE and setting it as FALSE, when problems with tcltk package dependecy occurs, solves the problem

Details

The number of permutations to use can be chosen based on the number of possible permutations of samples. If the possible number is too big, usually >5000 permutations can be sufficient.

Value

A list of components

raw.p.one

returns the one-sided p-value matrix for the five test statisticsin in 6 columns: the first column is the probe ID, the second to the last columns contain the raw p-values for each test statistic

raw.p.two

returns the two-sided p-value matrix for the five test statistics in 6 columns: the first column is the probe ID, the second to the last columns contain the raw p-values for each test statistic

rawp.up

returns the one-sided p-value matrix testing increasing alternative for the five test statistics in 6 columns: the first column is the probe ID, the second to the last columns contain the raw p-values for each test statistic

rawp.dn

returns the one-sided p-value matrix testing decreasing alternative for the five test statistics in 6 columns: the first column is the probe ID, the second to the last columns contain the raw p-values for each test statistic

Note

For each gene, the one-sided p-values are calculated from min(p^Up, p^Down) and the two sided p-values are calculated from min{2 * min(p^Up, p^Down), 1}, where p^Up and p^Down are the p-values calculated for each ordered alternative.

Author(s)

Lin et al.

References

Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R, Lin D., Shkedy Z., Yekutieli D., Amaratunga D., and Bijnens, L. (editors), (2012), Springer.

Testing for Trend in Dose-Response Microarray Experiments: a Comparison of Testing Procedures, Multiplicity, and Resampling-Based Inference, Lin et al. 2007, Stat. App. in Gen. & Mol. Bio., 6(1), article 26.

IsoGene: An R Package for Analyzing Dose-response Studies in Microarray Experiments, Pramana S., Lin D., Haldermans P., Shkedy Z., Verbeke T., De Bondt A., Talloen W., Goehlmann H., Bijnens L. 2010, R Journal 2/1.

See Also

IsoTestBH

Examples

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## Not run: 
  set.seed(1234)
  x <- c(rep(1,3),rep(2,3),rep(3,3))
  y1 <- matrix(rnorm(90, 1,1),10,9)  # 10 genes with no trends
  y2 <- matrix(c(rnorm(30, 1,1), rnorm(30,2,1),
                 rnorm(30,3,1)), 10, 9)  # 10 genes with increasing trends
  y <- data.frame(rbind(y1, y2))  # y needs to be a data frame 
  rp <- IsoRawp(x, y, niter = 1000)
  rp

## End(Not run)