baseOlig.error.step2: Evaluates LPE variance function of M for quantiles of A...

In LPE: Methods for analyzing microarray data using Local Pooled Error (LPE) method

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.

Author(s)

Nitin [e-mail]

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.

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.

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

LPE documentation built on Nov. 8, 2020, 5:25 p.m.