normalizeCel: Batch normalization of Affymetrix chips on cluster
In tgirke/longevityTools: Analysis tools for longevity research

Description Usage Arguments Details Value Author(s) Examples

Function to processe large numbers of CEL files from different chip types separately using the MAS5 normalization algorithm. To improve time performance, the processing is parallelized with BiocParallel to run on a computer cluster with a scheduler (e.g. Torque).

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normalizeCel(chiptype_list, rerun = TRUE)

combineResults(chiptype_dir, rerun = TRUE)

`chiptype_list`	`list` of character vectors specifying CEL file names from same chip type. The list components are named after the chip types.
`chiptype_dir`	Paths to CEL file directory specified in `character` vector.
`rerun`	If `rerun` set to `TRUE`, the function will be executed, while `FALSE` returns a usage message.

The normalizeCel function will write the results to sub-directories within the data directory. The sub-directories will be named after the chip types. Each one of them will contain three files containing the MAS5 normalized intensities, the corresponding P/M/A calls and the Wilcoxon rank p-values.

The function combineResults will write the combined results to the same directories as normalizeCel.

Note: more detailed information should be added to this help file.

Files written to disk.

Thomas Girke

## Not run: 
## Normalization of CEL files
library(BiocParallel); library(BatchJobs); library(affy) 
chiptype_list <- split(names(chiptype), as.character(chiptype))
normalizeCel(chiptype_list, rerun=FALSE)

## Combine results from same chip type in single data.frame
chiptype_dir <- unique(readRDS("./data/chiptype.rds"))
combineResults(chiptype_dir, rerun=FALSE)


## End(Not run)