baseOlig.error.step1 {LPE} | R Documentation |

## Evaluates LPE variance function of M for quantiles of A within and experimental condition by divinding the A in 100 intervals.

### Description

Genes are placed in bins/quantiles according to their average
expression intensity. The function baseOlig.error calculates a
pooled variance of M for genes within these bins/quantiles of A
for the replicates of the experimental condition contained in y.
Here the assumption is that variance of the genes in each interval
is similar.

### Usage

baseOlig.error.step1(y, stats=median, q=0.01, df=10)

### 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. |

`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. |

### Value

Returns object of class baseOlig, comprising a data frame with 2 columns: A
and var M. The A column contains the median values of each gene
and the M columns contains the corresponding variance. Number of
rows of the data-frame is same as that of the number of genes.

### 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)
dim(var.1)
# Returns a matrix of 1000 by 2 (A,M) format

[Package

*LPE* version 1.1.5

Index]