profileClusters {chroGPS} | R Documentation |
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.
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.
# 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(d@d),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))