quartile.normalize: Normalization based on quartile range

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

Description

Does Normalization based on quartile range

Usage

  quartile.normalize(x, percent=50)

Arguments

x	x is a matrix or data.frame on which normalization has to be performed.
percent	Percentage for which normalization is needed

Value

Returns the normalized data based on quartile normalization

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

  library(LPE)
  # Loading the LPE library

  data(Ley) 
 
 dim(Ley)
 # Gives 12488*7
 # First column is ID.

  subset <- 1:1000
  Ley[subset,2:7] <- quartile.normalize(Ley[subset,2:7],percent=50)

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