filt.hierClust: filt.hierClust

Description Usage Arguments Details Value Author(s)

Description

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.

Usage

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filt.hierClust(mat.rho, hclust.method = "ward", margins = c(6, 6),
  side.col.c = NULL, side.col.r = NULL, size = 10, plot = TRUE,
  filt = 0.5)

Arguments

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.D method

margins

: change margins of the graph (default = c(6, 6))

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

Details

filt.hierClust

Value

it will return a matrix with samples in rows and their closely related ones on the columns along with the correlation score.

Author(s)

Emmanuelle Le Chatelier & Edi Prifti


eprifti/momr documentation built on May 16, 2019, 8:20 a.m.