bestClusterThresholds

For a given clustering, walks the walktrap result tree to find
a subtree with max(min(sens,spec)) for each cluster, where sens is sensitivity, spec is specificity

Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/conos>. The size of the 'conosPanel' package is approximately 12 MB.

Evan Biederstedt

conos

Clustering on Network of Samples

Viktor Petukhov

Nikolas Barkas

Peter Kharchenko

Weiliang Qiu

bestClusterThresholds function

<dl><dt>res</dt>
<dd>walktrap result object (igraph)</dd>
<dt>clusters</dt>
<dd>cluster factor</dd>
<dt>clmerges</dt>
<dd>integer matrix of cluster merges (default=NULL). If NULL, the function treeJaccard() performs calculation without it.</dd></dl>

Arguments

Find threshold of cluster detectability — bestClusterThresholds

<dl>

<dt>res</dt>
<dd>walktrap result object (igraph)</dd>


<dt>clusters</dt>
<dd>cluster factor</dd>


<dt>clmerges</dt>
<dd>integer matrix of cluster merges (default=NULL). If NULL, the function treeJaccard() performs calculation without it.</dd>

</dl>

bestClusterThresholds: Find threshold of cluster detectability

Description

Usage

Value

Arguments