dataSimHuman

Data generated from the known regulatory network of human cell signalling data.

datasets

We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. The algorithm proposed makes use of moment fractional Bayes factors (MFBF) and thus it is suitable for learning sparse graph. The algorithm is implemented by using Armadillo: an open-source C++ linear algebra library.

Davide Altomare

FBFsearch

Algorithm for Searching the Space of Gaussian Directed Acyclic
Graph Models Through Moment Fractional Bayes Factors

dataSimHuman function

<code>dataSimHuman</code> contains the following objects:<dl>
 <dt><code>Obs</code></dt>
<dd>List of 100 matrices (100x11) each of which with 100 observations simulated from the known regulatory network.</dd> <dt><code>Perms</code></dt>
<dd>List of 5 matrices (1x11) each of which with a permutation of the nodes.</dd> <dt><code>TDag</code></dt>
<dd>Matrix (11x11) with the adjacency matrix of the known regulatory network.</dd> 
</dl>

Format

Simulated cell signalling pathway data — dataSimHuman

<code>dataSimHuman</code> contains the following objects:<dl>
 <dt><code>Obs</code></dt>
<dd>List of 100 matrices (100x11) each of which with 100 observations simulated from the known regulatory network.</dd>

 <dt><code>Perms</code></dt>
<dd>List of 5 matrices (1x11) each of which with a permutation of the nodes.</dd>

 <dt><code>TDag</code></dt>
<dd>Matrix (11x11) with the adjacency matrix of the known regulatory network.</dd>

 
</dl>

dataSimHuman: Simulated cell signalling pathway data

Description

Usage

Arguments

Format

References