baseOlig.error {LPE} | R Documentation |

## Evaluates LPE variance function of M for quantiles of A within and
experimental condition and then interpolates it for all genes.

### Description

Calls baseOlig.error.step1 and baseOlig.error.step2 functions in order to
calculate the baseline distribution.

### Usage

baseOlig.error(y, stats=median, q=0.01, min.genes.int=10,div.factor=1)

### Arguments

`y` |
y is a preprocessed matrix or data frame of expression intensities in which columns are expression intensities for a particular experimental
condition and rows are genes. |

`stats` |
It determines whether mean or median is to be used for the replicates |

`q` |
q is the quantile width; q=0.01 corresponds to 100 quantiles
i.e. percentiles. Bins/quantiles have equal number of genes and
are split according to the average intensity A. |

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

`div.factor` |
Determines the factor by which sigma needs to be divided for
selecting adaptive intervals. |

### Value

Returns object of class baseOlig comprising a data frame with 2 columns: A
and var M, and rows for each quantile specified. The A column contains
the median values of A for each quantile/bin and the M columns contains
the pooled variance of the replicate chips for genes within each quantile/bin.

### 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.

### See Also

`lpe`

### Examples

# Loading the library and the data
library(LPE)
data(Ley)
dim(Ley)
# Gives 12488 by 7
Ley[1:3,]
# Returns
# ID c1 c2 c3 t1 t2 t3
# 1 AFFX-MurIL2_at 4.06 3.82 4.28 11.47 11.54 11.34
# 2 AFFX-MurIL10_at 4.56 2.79 4.83 4.25 3.72 2.94
# 3 AFFX-MurIL4_at 5.14 4.10 4.59 4.67 4.71 4.67
Ley[,2:7] <- preprocess(Ley[,2:7],data.type="MAS5")
subset <- 1:1000
Ley.subset <- Ley[subset,]
# Finding the baseline distribution of subset of the data
# condition one (3 replicates)
var.1 <- baseOlig.error(Ley.subset[,2:4], q=0.01)
dim(var.1)
# Returns a matrix of 1000 by 2 (A,M) format

[Package

*LPE* version 1.1.5

Index]