Bayesian Networks in the Presence of Temporal Information - Bayesian Networks in R: With Applications in Systems Biology - page 83

3.2. Consider the arth800 data set from the GeneNet package, which we analyzed
in Sects. 3.5.2 and 3.5.3 .
(a) Load the data set from the GeneNet package. The time series expression of
the 800 genes is included in a data set called arth800.expr . Investigate its
properties using the exploratory analysis techniques covered in Chap. 1 .
(b) For this practical exercise, we will work on a subset of variables (one for each
gene) having a large variance. Compute the variance of each of the 800 variables,
plot the various variance values in decreasing order, and create a data set with
the variables greater than 2.
(c) Can you fit a VAR process with usual approach from this data set?
(d) Which alternative approaches can be used to fit a VAR process from this data
set?
(e) Estimate a dynamic Bayesian network with each of the alternative approaches
presented in this chapter.
3.3. Consider the dimension reduction approaches used in the previous exercise and
the arth800 data set from the GeneNet package.
(a) For a comparative analysis of the different approaches, select the top 50 edges
for each approach (function BuildEdges from the G1DBN package can be
used to that end).
(b) Plot the four inferred networks with the function plot from package G1DBN .
(c) How many edges are common to the four inferred networks?
(d) Are the top 50 edges of each inferred network similar? What can you conclude?