Obtaining the list of significant genes using the SAM procedure

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Description

The function obtains the list of significant genes using the SAM procedure for the five test statistics (the global likelihood test, Williams, Marcus, M, and the modified M).

Usage

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Isoqval(delta, allfdr, qqstat, stat)

Arguments

delta

the delta value as cut-off to find the number of significant genes

allfdr

the delta table obtained from function Isoallfdr

qqstat

output from function Isoqqstat containing the test statistics of permutations

stat

choose one of the five test statistics to use

Value

A list of components

res

returns the list genes with descending q-values of the SAM procedure in three columns: the first column is the row number of the genes, the second column is the observed test statistic values, and the last column is the q-values

sign.list

returns the list of significant genes found by the defined delta value with descending p-values in three columns: the first column is the row number of the genes, the second column is the observed test statistic values, and the last column is the q-values

Note

This function obtains the list of significant genes using the SAM procedure for the five test statistics. To use the SAM procedure, the number of genes in the dataset is preferably larger than 500.

Author(s)

Lin et al.

References

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

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

isoreg, Isoqqstat, Isoallfdr, IsoTestSAM, IsoSAMPlot

Examples

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  set.seed(1234)
 
  x <- c(rep(1,3),rep(2,3),rep(3,3))
  y1 <- matrix(rnorm(4500, 1,1),500,9) ## 500 genes with no trends
  y2 <- matrix(c(rnorm(1500, 1,1),rnorm(1500,2,1),
    rnorm(1500,3,1)),500,9) ## 500 genes with increasing trends
  y <- data.frame(rbind(y1, y2)) ##y needs to be a data frame
  qqstat <- Isoqqstat(x, y, fudge="pooled", niter=50)
  allfdr <- Isoallfdr(qqstat, ,stat="E2")
  qval <- Isoqval(delta=0.2, allfdr, qqstat, stat="E2")