fdrma: Functional Data Robust Microarray Analysis

In AliciaNieto/fdaRNA: RNA-Seq and Microarray Preprocessing

Description

This function performs robust microarray analysis for a sample of microarray cell files from a microarray experiment with Affymetric chips.

Usage

fdrma(
  slc0cel,
  method = c("fdn", "myrma"),
  mod = c("MP", "MM", "LS"),
  no.cores = floor(detectCores())
)

Arguments

slc0cel	Cell files from Affy micrarray experiment.
method	Normalization method: either "fdn" (funcional data normalization) or "qn" quantile normalization.
mod	one of the following: "MP"= median polish, "MM"= Tukey's biweight M-estimator,"M"= Huber M-estimator,"LS"= Least squares.
no.cores	default is floor(detectCores())-1

Details

functional data normalization instead of quantile normalization. In addition the user may specify a robust regression M-estimator of regression ("M" or "MM") instead of one step median polish that was used in the original RMA. MM-estimator is an M-estimator that uses the psi function given by Tukey's biweight function. The M-estimator uses Huber's psi function.

Value

This function returns an array of gene expressions with G genes by n samples, that summarizes the probes of the original datasets.

Author(s)

A. Nieto and J. Cabrera

Examples

## Not run: 
library(affy)
# Path to file with cel files from experiment
fns <- list.celfiles(path="cel_filesday0",full.names=TRUE)
# Read Affy files into R
slc0cel <- ReadAffy(filenames=fns)

# Get rma file and fdn files with gene expressions
slc0.fdn= fdrma(slc0cel, method = "fdn", mod = "MM")
slc0.rma= fdrma(slc0cel, method = "rma")
# Compare the functional forms of the gene expressions
plot(sort(slc0.rma[,1]),sort(slc0.fdn[,1]),type="l",col=2)

## End(Not run)

AliciaNieto/fdaRNA documentation built on May 29, 2020, 11:58 a.m.