resamp.adj {LPE} | R Documentation |

## Resampling based fdr adjustment

### Description

Adjusts the fdr based on rank invariant genes

### Usage

resamp.adj(x,y, q=0.01, iterations=5, min.genes.int=10)

### Arguments

`x` |
Replicated data from first experimental condition (as matrix
or data-frame) |

`y` |
Replicated data from second experimental condition (as matrix
or data-frame) |

`q` |
q is the quantile width; q=0.01 corresponds to 100 quantiles |

`iterations` |
Number of iterations to be performed to obtain critical z-statistics |

`min.genes.int` |
Determines the minimum number of genes in a subinterval for selecting the adaptive intervals. |

### Details

Returns the z-statistics for the null distribution, obtained from resampling
the rank invariant genes within each quantile. These z-statistic values are
compared with z-statiscs from the original data, and fdr is calculated.

### References

J.K. Lee and M.O.Connell(2003). *An S-Plus library for the analysis of differential expression*. In The Analysis of Gene Expression Data: Methods and Software. Edited by G. Parmigiani, ES Garrett, RA Irizarry ad SL Zegar. Springer, NewYork.

Jain et. al. (2003) *Local pooled error test for identifying
differentially expressed genes with a small number of replicated microarrays*, Bioinformatics, 1945-1951.

### Examples

# Loading the library and the data
library(LPE)
data(Ley)
dim(Ley)
# Gives 12488*7
# First column is ID.
# Subsetting the data
subset.Ley <- Ley[1:1000,]
subset.Ley[,2:7] <- preprocess(subset.Ley[,2:7],data.type="MAS5")
# Finding the baseline distribution of condition 1 and 2.
var.1 <- baseOlig.error(subset.Ley[,2:4], q=0.01)
var.2 <- baseOlig.error(subset.Ley[,5:7], q=0.01)
# Applying LPE
lpe.result <- lpe(subset.Ley[,2:4],subset.Ley[,5:7], var.1, var.2,
probe.set.name=subset.Ley[,1])
z.stats.null <- resamp.adj(subset.Ley[,2:4], subset.Ley[,5:7], q=0.01, iterations=2,min.genes.int=10 )

[Package

*LPE* version 1.1.5

Index]