build_tree_graph

Build a tree graph from a set of clusterings, metadata and associated
aesthetics

internal

Deciding what resolution to use can be a difficult question when
approaching a clustering analysis. One way to approach this problem is to
look at how samples move as the number of clusters increases. This package
allows you to produce clustering trees, a visualisation for interrogating
clusterings as resolution increases.

Luke Zappia

clustree

Visualise Clusterings at Different Resolutions

Alicia Oshlack

Andrea Rau

Paul Hoffman

build_tree_graph function

<dl><dt>clusterings</dt>
<dd>numeric matrix containing clustering information, each
column contains clustering at a separate resolution</dd>
<dt>prefix</dt>
<dd>string indicating columns containing clustering information</dd>
<dt>count_filter</dt>
<dd>count threshold for filtering edges in the clustering
graph</dd>
<dt>prop_filter</dt>
<dd>in proportion threshold for filtering edges in the
clustering graph</dd>
<dt>metadata</dt>
<dd>data.frame containing metadata on each sample that can be
used as node aesthetics</dd>
<dt>node_aes_list</dt>
<dd>nested list containing node aesthetics</dd></dl>

Arguments

Build tree graph — build_tree_graph

<dl>

<dt>clusterings</dt>
<dd>numeric matrix containing clustering information, each
column contains clustering at a separate resolution</dd>


<dt>prefix</dt>
<dd>string indicating columns containing clustering information</dd>


<dt>count_filter</dt>
<dd>count threshold for filtering edges in the clustering
graph</dd>


<dt>prop_filter</dt>
<dd>in proportion threshold for filtering edges in the
clustering graph</dd>


<dt>metadata</dt>
<dd>data.frame containing metadata on each sample that can be
used as node aesthetics</dd>


<dt>node_aes_list</dt>
<dd>nested list containing node aesthetics</dd>

</dl>

build_tree_graph: Build tree graph

Description

Usage

Value

Arguments