propagateLabelsDiffusion

Estimate labeling distribution for each vertex, based on provided labels using Random Walk

internal

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

propagateLabelsDiffusion function

<dl><dt>graph</dt>
<dd>input graph</dd>
<dt>labels</dt>
<dd>vector of factor or character labels, named by cell names</dd>
<dt>max.iters</dt>
<dd>maximal number of iterations (default=100)</dd>
<dt>tol</dt>
<dd>numeric Absolute tolerance as a stopping criteria (default=0.025)</dd>
<dt>verbose</dt>
<dd>boolean Verbose mode (default=TRUE)</dd>
<dt>fixed.initial.labels</dt>
<dd>boolean Prohibit changes of initial labels during diffusion (default=TRUE)</dd></dl>

Arguments

Estimate labeling distribution for each vertex, based on provided labels using Random Walk — propagateLabelsDiffusion

<dl>

<dt>graph</dt>
<dd>input graph</dd>


<dt>labels</dt>
<dd>vector of factor or character labels, named by cell names</dd>


<dt>max.iters</dt>
<dd>maximal number of iterations (default=100)</dd>


<dt>tol</dt>
<dd>numeric Absolute tolerance as a stopping criteria (default=0.025)</dd>


<dt>verbose</dt>
<dd>boolean Verbose mode (default=TRUE)</dd>


<dt>fixed.initial.labels</dt>
<dd>boolean Prohibit changes of initial labels during diffusion (default=TRUE)</dd>

</dl>

propagateLabelsDiffusion: Estimate labeling distribution for each vertex, based on provided labels using Random Walk

Description

Usage

Arguments