This variable may be used to test the filtering and visualisation methods
implemented in the package. It contains the output of
successively applying the commands
AlvMac_results = GO_analyse(eSet=AlvMac, f="Treatment") and
AlvMac_results.pVal = pValue_GO(AlvMac_results, N=100,
ranked.by=result$rank.by, rank.by='P') to the toy input data
A list of 9 slots summarising the input and results of the analysis:
GO contains a table ranking all GO terms related to genes in
the expression dataset based on the average ability of their related
genes to cluster the samples according to the predefined grouping
mapping contains the table mapping genes present in the
dataset to GO terms.
genes contains a table ranking all genes present in the
expression dataset based on their ability to cluster the samples
according to the predefined grouping factor (see 'factor' below).
factor contains the grouping factor analysed.
method contains the statistical framework used.
subset contains the filters used to select a subset of
samples from the original
ExpressionSet for analysis.
rank.by contains the metric used to rank the scoring tables.
ntree contains number of trees built during the randomForest
mtry contains the number of features randomly sampled as
candidates at each split in each tree built during the randomForest
p.iterations contains the number of permutations performed
to compute the P-value in the
Running the above command again, you might obtain slightly different scores and ranks due to the stochastic process of sampling used by the random forest algorithm. However, the ranking metric was found to be robust and stable across run, given adequate number of trees and predictor variables sampled.
To produce reproducible results, use the
set.seed() function prior
to running any randomising or sampling function.
Source data are part of a publication in review.
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