Tools to simplify management of clusters via 'snow' package and large dataset handling through the 'bigmemory' package.
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arguments to be passed to
Some methods in the oligo/crlmm packages, like
rma can use a cluster
(set through 'snow' package). The use of cluster features is
conditioned on the availability of the 'bigmemory' (used to
provide shared objects across compute nodes) and 'snow' packages.
To use a cluster, 'oligo/crlmm' checks for three requirements: 1) 'ff' is loaded; 2) 'snow' is loaded; and 3) the 'cluster' option is set (e.g., via options(cluster=makeCluster(...)) or setCluster(...)).
If only the 'ff' package is available and loaded (in addition to the caller package - 'oligo' or 'crlmm'), these methods will allow the user to analyze datasets that would not fit in RAM at the expense of performance.
In the situations above (large datasets and cluster), oligo/crlmm uses the
ocProbesets to limit the
amount of RAM used by the machine(s). For example, if ocSamples is
set to 100, steps like background correction and normalization process
(in RAM) 100 samples simultaneously on each compute node. If
ocProbesets is set to 10K, then summarization processes 10K
probesets at a time on each machine.
In both scenarios (large dataset and/or cluster use), there is a penalty in performance because data are written to disk (to either minimize memory footprint or share data across compute nodes).
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