Description Usage Arguments Value Author(s) References See Also Examples

Genes are placed in bins/quantiles according to their average expression intensity. The function adjBaseOlig.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.

1 |

`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 |

`setMax` |
If T then all variances below the max variance in the ordered distribution of variances are set to the maximum variance. If F then variances are left as is (recommended) |

`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. |

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.

Carl Murie carl.murie@mcgill.ca, Nitin Jain nitin.jain@pfizer.com

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.

Jain et. al. (2005) *Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data*, BMC Bioinformatics, Vol 6, 187.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
# Loading the data from the LPE library
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 <- adjBaseOlig.error.step1(Ley[1:1000,2:4], setMax=FALSE, q=0.01)
dim(var.1)
# Returns a matrix of 1000 by 2 (A,M) format
``` |

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