baseOlig.error.step2 {LPE} | R Documentation |

## Evaluates LPE variance function of M for quantiles of A within and
experimental condition. It is based on the adaptive number of intervals.

### Description

Similar to baseOlig.error.step1 function, except that now the number of bins
are chosen adaptively instead of fixed 100.

### Usage

baseOlig.error.step2(y,baseOlig.error.step1.res, df=10, stats=median, 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. |

`baseOlig.error.step1.res` |
It is the result obtained from
baseOlig.error.step1 function, in which number of bins are fixed=100 |

`df` |
df stands for degrees of freedom. It is used in
smooth.spline function to interpolate the variances
of all genes. Default value is 10. |

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

`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[1:1000,2:7] <- preprocess(Ley[1:1000,2:7],data.type="MAS5")
# Finding the baseline distribution of subset of the data
# condition one (3 replicates)
var.1 <- baseOlig.error.step1(Ley[1:1000,2:4], q=0.01, df=10)
dim(var.1)
var.11 <- baseOlig.error.step2(Ley[1:1000,2:4], var.1, df=10)
# Returns a matrix of 1000 by 2 (A,M) format

[Package

*LPE* version 1.1.5

Index]