Description Usage Arguments Details Value Author(s)
This function takes as input a square similarity matrix and searches for clusters of samples with strong associations and extracts the sub matrix with the closely related sampless. Only positive correlations are considered here.
1 2 | filt.hierClust(mat.rho, hclust.method = "ward", side.col.c = NULL,
side.col.r = NULL, size = 10, plot = TRUE, filt = 0.5)
|
mat.rho |
: square correlation matrix with ids (can be used for also other than just samples) |
hclust.method |
: the hierarchical clustering method, by default it is the ward method |
side.col.c |
: a vector of colors to be applied in the columns, usually depincting a class |
side.col.r |
: a vector of colors to be applied in the rows, usually depincting a class |
size |
: the number of samples in the resulting ordered matrix |
plot |
: logical default TRUE. It will plot the heatmap of the similarity with the hierchical clustering |
filt |
: default is 0.5 and is the filtering threshold to be applied |
filt.hierClust
it will return a matrix with samples in rows and their closely related ones on the columns along with the correlation score.
Emmanuelle Le Chatelier & Edi Prifti
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