aroma.affymetrix 1.5.0
aroma.cn 0.4.4
What's new?
This section describes global options that you set in order change the default behavior of the aroma framework.
All settings specific to the aroma packages are stored in the R object 'aromaSettings'. An overview of the current settings can be obtained as:
> str(as.list(aromaSettings))
List of 4
$ memory:List of 2
..$ ram : num 1
..$ gcArrayFrequency: num 50
$ rules :List of 1
..$ allowAsciiCdfs: logi FALSE
$ output:List of 2
..$ checksum : logi FALSE
..$ timestampsThreshold: num 500
$ models:List of 1
..$ RmaPlm:List of 2
.. ..$ medianPolishThreshold: num [1:2] 500 6
.. ..$ skipThreshold : num [1:2] 5000 1
After changing some of the aroma settings, they can be saved to disk (default ~/.aromaSettings) such that they will be loaded automatically next time an aroma.* package is loaded. In order to do this, do:
saveAnywhere(aromaSettings)
Value: A positive double.
Default: 1.0
Get: getOption(aromaSettings, "memory/ram")
Set: setOption(aromaSettings, "memory/ram", numeric)
Applies to: Methods processing data in chunks of cells or units, e.g. probe-level summarization.
Description: A scale factor controlling the size of each chunk read into memory and processed in each iteration. On systems with very limited amount of memory it may be set to a smaller value than 1.0. On systems with a lot of memory, it may be set to a value greater than 1.0 to allow more data to be processed in each chunk, which may decrease the relative overhead from the file I/O.
See also: How to 'Improve processing time'.
Value: A positive integer.
Default: 50
Get: getOption(aromaSettings, "memory/gcArrayFrequency")
Set: setOption(aromaSettings, "memory/gcArrayFrequency", integer)
Applies to: Methods processing data in chunks.
Description: When processing data in chunks temporary variables are allocated and discarded. The built in garbage collector (GC) of the R engine will automatically clean up after this when memory is needed. However, it may still be the case that the memory will become too fragmented and one wish to take a precautious approach and cleaning up data more frequently. This settings specifies how many iterations is done before calling the GC.
Warning: This settings will be defined at some stage. /HB 2009-12-04
Value: Two positive integers c(nbrOfCells, nbrOfArrays)
Default: c(500, 6)
Get: getOption(aromaSettings, "models/RmaPlm/medianPolishThreshold")
Set: setOption(aromaSettings, "models/RmaPlm/medianPolishThreshold", c(integer, integer))
Applies to: Fitting an RmaPlm model.
Description: This setting specifies when the median polish estimator is used instead of the robust linear model estimator. The median polish is forced to be used if the number of arrays analyzed is (strictly) greater than 'nbrOfCells' and the number of cells in the probeset (unit group) is (strictly) greater than 'nbrOfCells'.
Motivation: When using robust linear model estimators (the default) for RmaPlm, the fitting time of a probeset will grow exponentially with the number of samples. It will also grow, but not as dramatically with the number of cells in the probeset. When the numbers samples is very large this will be too expensive. An alternative is then to use the median polish estimator instead, whose processing time is linear.
Value: Two positive integers c(nbrOfCells, nbrOfArrays)
Default: c(5000, 1)
Get: getOption(aromaSettings, "models/RmaPlm/skipThreshold")
Set: setOption(aromaSettings, "models/RmaPlm/skipThreshold", c(integer, integer))
Applies to: Fitting an RmaPlm model.
Description: This setting specifies when a probeset is skipped. A probeset (unit group) is not fitted if the number of arrays analyzed is (strictly) greater than 'nbrOfCells' and the number of cells in the unit is (strictly) greater than 'nbrOfCells'.
Motivation: For some CDFs there exists probesets with an extremely large number of cells and that will take a long time to fit. Such probesets have often no biological meaning, e.g. they contain cells that did not map to the genome or map to multiple places. This setting provides a convenient way to skip such probesets.
Value: TRUE or FALSE.
Default: FALSE
Get: getOption(aromaSettings, "rules/allowAsciiCdfs")
Set: setOption(aromaSettings, "rules/allowAsciiCdfs", logical)
Applies to: Using/setting a CDF of an AffymetrixCelSet.
Description: This setting is used to prevent the usage of ASCII CDFs, because they are really slow to work with and the memory overhead is large. When it is FALSE (default), only binary CDFs are accepted and an error will be thrown if an ASCII CDF is used. If TRUE, ASCII CDFs are accepted.
Comment: Do not use ASCII CDFs unless really necessary. Instead, convert existing ASCII CDFs into binary ones.
Value: TRUE or FALSE.
Default: FALSE
Get: getOption(aromaSettings, "output/checksum")
Set: setOption(aromaSettings, "output/checksum", logical)
Description: NOT IMPLEMENTED
Value: TRUE or FALSE.
Default: TRUE
Get: getOption(aromaSettings, "output/path")
Set: setOption(aromaSettings, "output/path", logical)
Description: NOT IMPLEMENTED
Value: TRUE or FALSE.
Default: TRUE
Get: getOption(aromaSettings, "output/ram")
Set: setOption(aromaSettings, "output/ram", logical)
Description: NOT IMPLEMENTED
Value: An integer (including Inf).
Default: 500
Get: getOption(aromaSettings, "output/timestampsThreshold")
Set: setOption(aromaSettings, "output/timestampsThreshold", integer)
Applies: To the print() output of an AffymetrixCelSet.
Description: When calling print() on an AffymetrixCelSet, the range of time stamps of all CEL files is reported. This requires that the header of each CEL file is queried, which might takes a lot of time if the data set is large. This setting allows you to specify the maximum number of arrays for which the time stamp range should be reported. If the data set contains more arrays, the time stamps are neither queried nor reported, which will be much faster for large data sets.