Hi Habil,

I performed consensus WGCNA analysis to find conserved modules across 5 phases of a disease. 

Now, as a next step, I would like to use the eigengenes from these conserved modules and make a Bayesian network similar to what you have made in your papers and use the disorders to see how the different phases (control, Episode1, etc) are associated with different modules.

My questions are:

1) How to use pigengene for pre-calculated eigengenes for consensus modules?

2) If I use your pipeline, you have shown only two conditions i.e aml and mds but I have 5 conditions, can pigengene work with that?

3) can I perform logistic regression with eigengenes and different phases of disease which are coded as 0 or 1?

4)I want to see the BN network of consensus eigengenes and how they change in different phases of depression.

Thanks in advance for your kind help

Ram

The paper that you mentioned is a general introduction to Pigengene. Our following paper is more focused on Bayesian networks (BNs): Agrahari, Rupesh, et al. "Applications of Bayesian network models in predicting types of hematological malignancies." Scientific Reports 8.1 (2018): 6951. I answer your questions below:

1) How to use pigengene for pre-calculated eigengenes for consensus modules?
This can be done if you inspect the one.step.pigengene() function. Specifically, 
- Use check.pigengene.input() for QC:
c1 <- check.pigengene.input(Data=Data, Labels=Labels, na.rm=TRUE)
Data <- c1$Data
Labels <- c1$Labels
- Use compute.pigengene() to compute a pigengene object.
- Train your model using learn.bn().

2) If I use your pipeline, you have shown only two conditions i.e aml and mds but I have 5 conditions, can pigengene work with that?
Yes, e.g., I made a mock example from the compute.pigengene() example:
library(Pigengene)
data(aml)
data(mds)
data(eigengenes33)
d1 <- rbind(aml,mds)
Labels <- c(rep("AML",nrow(aml)),rep("MDS",nrow(mds)))
names(Labels) <- rownames(d1)
## Let's add another condition:
Labels[1:60] <- "Mock"
modules33 <- eigengenes33$modules[colnames(d1)]
## Computing:
pigengene <- compute.pigengene(Data=d1, Labels=Labels, modules=modules33,
   saveFile="pigengene.RData", doPlot=TRUE, verbose=3)
class(pigengene)
plot(pigengene, DiseaseColors=1:3, fontsize=12)
Fitting a BN is also possible. I checked the learn.bn() example using the above mock Labels.

3) Can I perform logistic regression with eigengenes and different phases of disease which are coded as 0 or 1?
Yes. in the paper, we showed that eigengenes are informative features (biomarkers), which can be used efficiently in different predictive models including decision trees, BNs, etc. Logistic regression is not an exception.

How many modules do you have? If you have many modules (20-30) and few samples (5-10), then you do not have some idea on which modules to use, then using all modules can lead to overfitting. See “Rule of Ten”.

4) I want to see the BN network of consensus eigengenes and how they change in different phases of depression.
You can use the draw.bn() function to plot a BN. However, training a BN need many (at least hundreds) of samples and I guess you have only tens of samples. Then, training a BN for a specific phase of depression would be even more difficult because of the limited number of samples. I recommend you use all your samples to train ONE BN and then the information you are looking for will be in the dependency table of the Disease variable. Specifically, I recommend you set
use.Disease=TRUE and use.Effect=FALSE. This is the opposite of the approach in out Scientific Report paper (see “The BN design” in Supplementary Note S1). 

- The input to the compute.pigengene() function includes data and labels, NOT the eigengenes. They are computed by this function.

- The parameters are explained in the manual of each function, e.g., see ?learn.bn. Is there a particular parameter you have a question on?

- After you have the BN structure learned (see examplelearn.bn)), you can fit it to the data using:

fit <- bnlearn::bn.fit(x=learnt$consensus1$BN, data=learnt$consensus1$Data,method="bayes",iss=10)

And now fit$Disease is the conditional probability table. Also, see the tables corresponding to children of this node, e.g., fit$ME9.

1- In the learn.bn function, Labels is a named vector of characters. It can have "0" and "1" values if you prefer that. I am not sure I get your question right. "Disease" is just the name of the variable that has Labels values. The name of the variable does not matter. Modules can be a child of this node.

2- In Fig3. a in the paper you referred to, the to each case 3 different conditions (i.e., relevant AD traits) are associated: 1) β-amyloid, 2) tau tangles and 3) cognitive decline. These conditions are modeled using 3 nodes in the BN: Amyloid, tau, and CogDec, correspondingly. You have only 1 condition which can get 2 values: schizophrenia or control. 

3- What are the ranges in column ME9?
The value of each eigengene is discretized into 3 levels because use.Hartemink is TRUE by default. See the manual.

4- The only parent of ME9 is Desease therefore, based on the Markov property, given the disease type, the probability of each level of ME9 is determined independently of the rest of the network. The fit$ME9 is the conditional probability table, e.g., if the disease is AML, the levels of the ME9 eigengene are observed with probabilities: 0.14, 0.25, 0.61, respectively. 

5- To fit a BN to data, we start from a random structure and try to maximize a score. We repeat this process bnNum times. So, the more the better because there will be more chances that you find a "good" network. For 10-20 modules, bnNum=1000 should be OK, and <100 might be too few.

6- A reference paper which can give a simple "Bayesian network for Dummies" kind of explanation?
Have a look at the wikipedia page on BNs, and if it is not simple enough let me know to see if I can find something simpler.

7- Is there a way I can bring and edit the graph in Cytoscape or any such program?
No unless you are willing to have a look at the draw.bn() code, learn how to get the network data, and use one of the many tools that are developed for plotting fancy graphs.