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

1 |

`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 |

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.

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 |

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.

Lin et al.

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.

1 2 3 4 5 6 7 8 9 10 11 | ```
## 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)
``` |

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