Description Usage Arguments Value Author(s) See Also Examples
The function computes the ratio between the proportion of epigenetic mark presence in the clusters given as input and that observed for all elements. Results are returned as a numerical matrix, easily visualized in the shape of a classical heatmap.
1  profileClusters(x, uniqueCount = TRUE, weights, clus, i, minpoints, merged = FALSE, log2 = TRUE, plt = FALSE)

x 
Genes * Factors matrix or data frame used for generating epigene clusters, indicating 1 for binding of factor j in gene i, 0 otherwise. 
uniqueCount 
Logical value to indicate if clusters come from epigenes (identical rows
in x are merged into a single one) or genes (every row in x is
mantained). See help for 
weights 
Named vector analog to that on 
clus 

i 
Clustering entry from which cluster profiling is to be computed. 
minpoints 
(Optional). Ignore clusters with fewer than minpoints, deprecated. 
merged 
(Optional). If clusters provided have been previously merged or not, deprecated. 
log2 
Logical to indicate if enrichment/depletion proportions are returned in log2 scale. Defaults to TRUE. 
plt 
Deprecated. 
A numerical matrix with the enrichment/depletion profile of the
epigenetic marks for each cluster provided in the clusGPS
object. Easy to visualize for instance with a heatmap plot.
Oscar Reina.
distGPS
for computing pairwise distances between epigenetic
elements. clusGPS
for computing epigenetic clusters.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  # Not run
# data(s2)
# # Computing distances
# d < distGPS(s2.tab,metric='tanimoto',uniqueRows=TRUE)
# # Creating MDS object
# mds1 < mds(d,type='isoMDS')
# mds1
# plot(mds1)
# Precomputing clustering
# h < hclust(as.dist([email protected]),method='average')
# # Calculating densities (contours and probabilities), takes a while
# clus < clusGPS(d,mds1,preMerge=TRUE,k=max(cutree(h,h=0.5)))
# Computing cluster profiles
# p1 < profileClusters(s2.tab, uniqueCount = TRUE, clus, i=125, minpoints=30, log2 = TRUE, plt = FALSE)
# Requires gplots
# heatmap.2(p1,col=redblue(100))

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