dynUGENE is an R package for the inference, simulation, and visualization of gene regulatory network dynamics from time-series expression data.
dynUGENE build off dynGENIE3, an algorithm to infer gene network architecture and dynamics given time-series or steady-state expression data.
dynUGENE provides several additional functionalities on top of dynGENIE3.
- Visualization of the inferred network as a heatmap.
- Simulation of the learned system given any initial condition.
- Stochastic simulations by accounting for uncertainty in the random forests’ predictions
- Model selection using a Pareto front by comparing model error with model complexity.
- Additional datasets (repressilator, Hodgkin-Huxley) both deterministic and stochastic.
The package is useful for those who wish to build explainable models of gene regulatory networks with varying degrees of complexity. The package is also useful for synthetic biologists who wish to design genetic circuits that match some desired dynamical or steady-state properties.
Install the development version with:
install.packages("devtools")
devtools::install_github("tianyu-lu/dynUGENE", build_vignettes = TRUE)
library("dynUGENE")The package is also available as a Shiny app on at tianyulu.shinyapps.io/dynUGENE/ or can be run locally with
dynUGENE::rundynUGENE()The Shiny app includes a tutorial and interactivity with the outputs of dynUGENE. In particular, useful features of the app include the following:
- Visualize each of the stepwise-tuned networks
- Select custom masks by clicking on the heatmap cells to be masked
- Interactive simulation plot allows zooming
library("dynUGENE")
ls("package:dynUGENE")
#> [1] "estimateDecayRates" "HodgkinHuxley"
#> [3] "inferNetwork" "inferSSNetwork"
#> [5] "plotTrajectory" "Repressilator"
#> [7] "rundynUGENE" "simulateUGENE"
#> [9] "StochasticHodgkinHuxley" "StochasticRepressilator"
#> [11] "tuneThreshold"
data(package="dynUGENE")The package file structure is illustrated below.
For detailed tutorials and descriptions of the provided datasets, see the vignette here:
browseVignettes("dynUGENE")
#> starting httpd help server ... donePackage author: Tianyu Lu.
With the exception of estimateDecayRates.R, the remaining files are
original code. inferNetwork.R is a re-implementation of the dynGENIE3
algorithm in R. The original implementation wraps around a random forest
implementation written in C. dynuGENE implements everything in R. The
randomForest package is used to train random forests. ramify is used
to obtain column-wise argmax of matrices. Plots for inferNetwork() are
made with ggplot2 and preprocessing done by reshape2. Plots for
inferSSNetwork() are made with gplots and colours specified by
RColorBrewer. The stats package is used to estimate mean and
variance from the random forests predictions and to sample from a
Gaussian.
Sources for code adapted from examples are provided near the code in question.
We can learn the architecture and simulate the dynamics of a
repressilator with inferNetwork():
We can perform a search over possible sparse network architectures with
tuneThreshold().
We can also learn the dynamics of a stochastic repressilator:
We can also visualize the learned networks for large, steady-state datasets:
Load and do inference with repressilator data:
library(dynUGENE)
## Infer network
ugene <- inferNetwork(Repressilator, mtry = 3L)
## Deterministic simulation of the inferred network dynamics
x0 <- Repressilator[1, 2:7]
trajectory <- simulateUGENE(ugene, x0)
plotTrajectory(trajectory, c("p3", "p2", "p1"))
## Stochastic simulation
trajectory <- simulateUGENE(ugene, x0, stochastic = TRUE)
plotTrajectory(trajectory, c("p3", "p2", "p1"))
inferNetwork() take about 30 seconds for automatic threshold tuning
for the Repressilator dataset.
simulateUGENE() takes about 3 minutes for simulating 5000 timesteps of
a 6 component system.
Tests: running the unit and integration tests takes about three minutes on a typical laptop.
dynUGENE welcomes
issues and other
contributions.
Logo made with Wix.
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