n.genes.adaptive.int: Calcuates the number of genes in various intervals...

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

Description

Instead of dividing the genes equally in 100 intervals, this function divides them adaptively based on three rules: a) min. number of genes (default =10), b) max. number of genes = total/100; c) based on Median + fraction(SD) from the starting gene of each interval

Usage

  n.genes.adaptive.int(baseOlig.error.step1.res,
  		min.genes.int=10, div.factor=1)

Arguments

baseOlig.error.step1.res	It is the result from baseOlig.error.step1 function.
min.genes.int	It is the minimum number of genes in the interval, default=10.
div.factor	(1/div.factor) is the fraction of Standard Deviation which we wish to include in each interval to calculate number of genes in each interval

Value

Returns a vector respresenting the number of genes in each interval.

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)
  dim(var.1)
  # Returns a matrix of 1000 by 2 (A,M) format
  n.genes.subint <- n.genes.adaptive.int(var.1, min.genes.int=10, div.factor=1)

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