README.md

In cbg-ethz/MC-CBN: Large-scale inference of continuous time Conjunctive Bayesian Networks

MC-CBN

MC-CBN performs an efficient large-scale inference of the continuous time conjunctive Bayesian network (CBN). MC-CBN uses Monte Carlo expectation-maximization algorithm for inference of the CBN model.

Usage

In this section, we explain how to use the R package on a simple example. In this example, we first generate a random poset as the true poset and assign to each node a mutation rate. Then, N genotypes and corresponding sequencing times are simulated from this poset. Then, in the first task, we estimate the mutation rates for the given true poset. In the second task, we estimate the maximum likelihood (ML) poset for the observed data.

First, we load the necessary libraries and set the random seed for the reproducibility.

library(graph)
library(mccbn)

set.seed(10)

Then we set the number of mutations and genotypes

# number of mutations
p = 10
# number of observed genotypes
N = 100

We generate a random poset (partially ordered set) with p nodes using the function random_poset

# generate a random poset with p nodes
true_poset = random_poset(p)

This poset is our true poset in this example. Function plot_poset can be used to plot this poset.

# plot the poset
plot_poset(true_poset)

True mutation rates are randomly generated using a uniform distribution.

# Sequencing rate is set to one
lambda_s = 1

# generate p random mutation rates uniformly distributed between lambda_s/3 to 3lambda_s.  
lambdas = runif(p, 1/3*lambda_s, 3*lambda_s)

OT-CBN

N genotypes are simulated from the true CBN model i.e., poset and mutation rates using sample_genotypes

# Simulate genotypes and sequencing times consistent with poset and mutation rates
simGenotypes = sample_genotypes(N, true_poset, sampling_param = lambda_s, lambdas=lambdas)

Now we estimate mutation rates using estimate_mutation_rates

# estimate mutation rates for a fixed poset 
est_lambda = estimate_mutation_rates(true_poset, simGenotypes$obs_events, simGenotypes$T_sampling) 

We simply compare relative absoulte errors of estimates using the following command

abs(est_lambda$lambda - lambdas)/lambdas

learn_network can be used to perform poset learning using genotypes and sequencing times

# network learning on genotypes and sequencing times
# The following command takes around one minutes on a personal laptop
t1 = proc.time()
fit = learn_network(simGenotypes$obs_events, simGenotypes$T_sampling) 
proc.time()

In order to obtain MLE poset(network), we postprocess the output of learn_network as follows:

# MLE network
mle_index = which.max(fit$logliks)
plot_poset(fit$posets[[mle_index]])

H-CBN2

Now, we generate genotypes from the true CBN model, but in addition, we simulate noisy observations

simGenotypes = sample_genotypes(N, true_poset, sampling_param = lambda_s, lambdas=lambdas, eps=0.01)

We estimate model parameters using MCEM.hcbn

lambda0 = runif(p, 1/3*lambda_s, 3*lambda_s)
est_params = MCEM.hcbn(lambda0, true_poset, simGenotypes$obs_events, lambda.s=lambda_s, L=100, eps=0.01)

To learn the poset from the data, we use adaptive.simulated.annealing

# Initial poset
posets = candidate_posets(simGenotypes$obs_events, rep(1, N), 0.9)
poset0 = posets[[length(posets)]]

fit = adaptive.simulated.annealing(poset0, simGenotypes$obs_events, L=100,
                   max.iter.asa=10, seed=10L)

Installation

Dependencies

MC-CBN requires the following software:

• C++ compiler with support for c++11 and OpenMP
• Boost C++ library (tested with version 1.74.0)
• (optional) Intel Math Kernel Library (MKL)

and R-packages for Bioconductor: - graph - Rgraphviz

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(version="3.16")
BiocManager::install(c("graph", "Rgraphviz"))

To install MC-CBN, you can use the latest tarball release, as well as R-package remotes to install dependencies, e.g:

install.packages("remotes")
remotes::install_url("https://github.com/cbg-ethz/MC-CBN/releases/download/v2.1.0/mccbn_2.1.0.tar.gz", dependencies=TRUE)

To compile with MKL, necessary compiler and linker flags can be determined with the help of the Intel MKL Link Line Advisor, e.g.:

remotes::install_url("https://github.com/cbg-ethz/MC-CBN/releases/download/v2.1.0/mccbn_2.1.0.tar.gz", dependencies=TRUE,
                     configure.args="--with-mklcxxflags=\"-DMKL_ILP64 -m64 -I${MKLROOT}/include\" --with-mklldflags=\"-Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_ilp64.a ${MKLROOT}/lib/intel64/libmkl_sequential.a ${MKLROOT}/lib/intel64/libmkl_core.a -Wl,--end-group -lpthread -lm -ldl\"")

Installation from source

Requirements: - Autotools (Autoconf >= 2.69, Automake >= 1.15, Automake-archive) - R-package remotes

After cloning the repository, go to the corresponding directory and type:

autoreconf -vif

Then build the package:

R CMD build .
````

Finally, install the package and other dependencies using R-package `remotes`:

install.packages("remotes") remotes::install_local("mccbn_.tar.gz", dependencies=TRUE)

Once again to compile with MKL, the appropriate flags can be passed using `--configure-args`, e.g:

remotes::install_local("mccbn_.tar.gz", dependencies=TRUE, configure.args="--with-mklcxxflags=\"-DMKL_ILP64 -m64 -I${MKLROOT}/include\" --with-mklldflags=\"-Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_ilp64.a ${MKLROOT}/lib/intel64/libmkl_sequential.a ${MKLROOT}/lib/intel64/libmkl_core.a -Wl,--end-group -lpthread -lm -ldl\"")

### Contributions
- [Hesam Montazeri](https://www.bsse.ethz.ch/cbg/group/people/person-detail.html?persid=168604)
- [Susana Posada Cespedes](https://www.bsse.ethz.ch/cbg/group/people/person-detail.html?persid=192769)
- [Niko Beerenwinkel](http://www.bsse.ethz.ch/cbg/group/people/person-detail.html?persid=149417)

### Contact

Susana Posada Cespedes susana.posada (at) bsse.ethz.ch ```

cbg-ethz/MC-CBN documentation built on Dec. 15, 2022, 5:42 p.m.