Clusters CAGE expression across multiple experiments, both at level of individual TSSs or entire clusters of TSSs.
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At which level should the expression clustering be done. Can be either
Only CTSSs or consensus clusters
Method to be used for expression clustering. Can be either
Expression clustering can be done at level of individual CTSSs, in which case the
feature vector used as input for clustering algorithm contains log-transformed and scaled
(divided by standard deviation) normalized CAGE signal at individual TSS across multiple
experiments. Only TSSs with normalized CAGE signal
>= tpmThreshold in at least
nrPassThreshold CAGE experiments are used for expression clustering. However, CTSSs
along the genome can be spatially clustered into tag clusters for each experiment separately
clusterCTSS function, and then aggregated across experiments into
consensus clusters using
aggregateTagClusters function. Once the consensus
clusters have been created, expression clustering at the level of these wider genomic regions
(representing entire promoters rather than individual TSSs) can be performed. In that case
the feature vector used as input for clustering algorithm contains normalized CAGE signal
within entire consensus cluster across multiple experiments, and threshold values in
nrPassThreshold are applied to entire consensus clusters.
what = "CTSS" the slots
CTSSexpressionClasses will be occupied, and if
what = "consensusClusters" the
consensusClustersExpressionClasses of the provided
CAGEset object will
be occupied with the results of expression clustering. Labels of expression classes (clusters)
can be retrieved using
expressionClasses function, and elements belonging to a
specific expression class can be selected using
Toronen et al. (1999) Analysis of gene expression data using self-organizing maps, FEBS Letters 451:142-146.
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