What

This repository contains the Bayesian network models underlying our paper, ‘Comparing basal dendrite branches in human and mouse hippocampal CA1 pyramidal neurons with Bayesian networks’. There is detailed documentation on the models in the paper.

The models are bnlearn objects. You can use them for analyzing structure and conditional independencies as well as for probabilistic inference. The following is a plot of the Bayesian network for the morphology of terminal basal dendritic branches:

load('models/bn_term.rdata') 
bn <- bnlearn::bn.net(bn_term)
plot(bn)

The following shows that length is independent of diameter given species in the network.

bnlearn::dsep('length', 'diameter', 'species', bn = bn)

## [1] TRUE

The repository contains 9 different Bayesian networks. Namely, we learned networks from three different subsets of our data: (a) from terminal branches alone; (b) from non-terminal branches alone; and (c) from both terminal and non-terminal branches. Within each data subset (that is, (a), (b), and (c)), we learned a separate Bayesian network model for each species as well as a combined Bayesian network model for both species.

Install and load bnlearn

You will need the bnlearn R package.

# install.packages('bnlearn')

library(bnlearn)

Load the models

The Bayesian networks are located in the models folder, as R objects. The load() function makes them available in memory. Their name is the name of their file, minus the extension (e.g., the model of human terminal branches is contained in object bn_term_human).

Terminal branches

load('models/bn_term.rdata') # bn_term 
load('models/bn_term_human.rdata') # bn_term_human 
load('models/bn_term_mouse.rdata') # bn_term_mouse

Non-terminal branches

load('models/bn_nonterm.rdata') # bn_nonterm 
load('models/bn_nonterm_human.rdata') # bn_nonterm_human 
load('models/bn_nonterm_mouse.rdata') # bn_nonterm_mouse

All branches

load('models/bn.rdata') # bn
load('models/bn_human.rdata') # bn_human 
load('models/bn_mouse.rdata') # bn_mouse

Using the models

The models are bn.fit objects (see bnlearn documentation) which means that they can readily be used for inference and prediction. For example, we may sample from and then plot the marginal distribution of branch diameter in terminal branches of both species:

diam <- cpdist(bn_term, nodes = 'diameter', evidence = TRUE, n = 1e5)
hist(diam$diameter, breaks = 100)

There are two modes because human branches are thicker than mouse ones.

If we want to analyze the networks (e.g., plot the structure, assess conditional independencies with the d-separation criterion) we need to convert the bn.fit object into a bn object, with bn.net():

bn <- bnlearn::bn.net(bn_term)
dsep(bn, 'diameter', 'length')

## [1] FALSE

dsep(bn, 'diameter', 'length', 'species')

## [1] TRUE