fixbounds.predict.smooth.spline: Makes the predicted variance non negative

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

Description

Makes the predicted variance non negative

Usage

fixbounds.predict.smooth.spline(object,x, deriv=0)

Arguments

object	variance from baseOlig.error function
x	vector for which variance needs to be predicted
deriv	derivative of the vector required, default =0

Value

Returns the predicted variance for the given vector based on the baseline error distribution. Maximum and minimum predicted values for the vector are same as those of baseline error distribution

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.

Examples

# Loading the library and the data
 library(LPE)
 data(Ley)
 
 dim(Ley)
 # Gives 12488*7 
 # First column is ID.


 # Subsetting the data
 subset.Ley <- Ley[1:1000,]
  
  subset.Ley[,2:7] <- preprocess(subset.Ley[,2:7],data.type="MAS5")
  # preprocess the data
  
 # Finding the baseline distribution of condition 1 and 2.
 var.1 <- baseOlig.error(subset.Ley[,2:4], q=0.01)
  median.x <- apply(subset.Ley[,2:4], 1, median)

 sf.x <- smooth.spline(var.1[, 1], var.1[, 2], df = 10)
  
 var.test <- fixbounds.predict.smooth.spline(sf.x, median.x)$y

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