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arrayQC.R
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arrayQC.R
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## One and two channel QC - Dept. of Bioinformatics / Toxicogenomics - Maastricht University - the Netherlands
# Version: 1.3.2-DEV
# Last adjustment: 07-Nov-2013
###########################
## USER-DEFINED SETTINGS ##
###########################
## Enter the directories to be used, use forward slashes (/) or double backward slashed (\\) as separator
# and end with a forward slash
#datapath <- "/home/stan/Desktop/PipelinePaper/set3/"
datapath <- "/location/to/your/files"
#arrayQC.scriptpath <- "/home/stan/Desktop/SVN/r-packages/arrayQC/dev/"
arrayQC.scriptpath <- "http://svn.bigcat.unimaas.nl/r-packages/arrayQC/dev/"
dataformat <- "" # Select data format of the microarray data files; "fes" (Feature Extraction Software), "genepix" or "generic" are valid here
datatype <- "" # Describe what type of microarrays are used in your dataset; "two-channel", "red" or "green" are valid options
## If the microarray contains more than one block, please change the values below accordingly.
#
number.of.blocks <- NULL
nblock.row <- NULL
nblock.col <- NULL
## columnHeader file. Change this value to the name (and location) of the file containing the
# column headers that will be read in. For standard Agilent and GenePix runs this variable
# should be NULL.
columnHeaderFile <- NULL
## Give one or more values - if any - that you used as manual flagging values within the data set
# examples:
# NULL (default, no flagging for Genepix, all non-zero values are flags for Agilent FES)
# -1 (-1 as a value)
# c(-1,-10) (-1 and -10 as values, the c() is used to group the values)
# You can also use the words "neg" and "pos" for all negative or all positive values
# example: c("neg",10)
useAsManualFlags <- NULL
## For generic formats: please supply the controlType variable below with the value(s)
# corresponding with the positive and negative controls used in the ControlType column.
controlType.value <- NULL
## Variables that are set to run the script. Only when arrayQC.mode is set to "local"
## Nog even over nadenken hoe dit wordt aangepast door de webservice...
#if(arrayQC.mode == "local") {
plotVirtualImages <- TRUE ## Should we plot Virtual Images?
plotBoxplot <- TRUE ## Should we plot boxplots?
plotHeatmap <- TRUE ## Should we generate correlation heatmaps?
plotClust <- TRUE ## Should we generate cluster images?
plotPCA <- TRUE ## Should we generate a PCA plot?
plotCor <- TRUE ## Should we generate a Correlation Plot?
plotMvA <- TRUE ## Should we generate an MvA plot?
loesscurve <- TRUE ## Should we plot loess curve(s) in the MvA plot?
plotDensity <- TRUE ## Should we generate a density plot?
## Which normMethod do you choose in the end? Options are "bgcorrected", "loess", "quantile", "aquantile", "scaled"
if(datatype == "two-channel") { normMethod <- "loess" } else { normMethod <- "quantile" }
#}
## If you want to run arrayQC locally, download the files from the scriptpath mentioned above
# and refer to these files in the scriptpath variable. Recommended for experienced R programmers.
source(paste(arrayQC.scriptpath, "run_arrayQC.R", sep=""))
###########################
## TROUBLESHOOTING ##
###########################
## 1) I get a 'could not allocate memory vector' error. What should I do?
# Answer: increase your available memory in R by setting memory.limit(2000) prior to running the script
# (here, 2000 corresponds with 2Gb of physical RAM)