IsoGene (version 1.0-24)

IsoTestSAM: Obtaining the list of significant genes using the SAM procedure

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

IsoTestSAM(x, y, fudge, niter, FDR, stat)

Arguments

x

numeric vector containing the dose levels

y

data frame of the gene expression with Probe ID as row names

fudge

option used for calculating the fudge factor in the SAM test statistic, either "pooled" (fudge factor will be automatically computed in the function), or "none" if no fudge factor is used

niter

number of permutations to use

FDR

choose the desired FDR to control

stat

choose one of the five test statistics to use

Value

A list with components

sign.genes1

a list of genes declared significant using the SAM procedure in a matrix of 5 columns. The first colomn is the probe id, the second column is the corresponding row number of the probe in the dataset, and the third column is the ordered test statistic values, and the fourth column is the q-values of the SAM procedure. The last two columns are raw p-values based on permutations and BH adjusted p-values.

qqstat

output of Isoqqstat

allfdr

output of Isoallfdr

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, Isofudge, IsoGenemSAM, Isoqqstat, Isoallfdr,Isoqval, IsoSAMPlot

Examples

  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  
SAM.obj <- IsoTestSAM(x, y, fudge="pooled", niter=50, FDR=0.05, stat="E2") 

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