🔬 Introduction
BioGA is an R package implementing a genetic algorithm
(GA) designed for multi-objective optimization on genomic data. It can
be used to:
- select gene signatures
- optimize biomarker panels
- balance objectives like expression distance, sparsity, or custom criteria
This vignette walks through an example using real-world-like gene expression data and demonstrates:
✅ GA initialization
✅ Evolution over generations
✅ Fitness convergence
✅ Visualizations of population diversity
📈 Prepare data
We’ll simulate realistic gene expression data:
set.seed(123)
n_genes <- 50
n_samples <- 20
# Simulate two classes: cases and controls
class_labels <- rep(c("Control", "Case"), each = 10)
genomic_data <- matrix(rnorm(n_genes * n_samples, mean = 5, sd = 2),
nrow = n_genes, ncol = n_samples)
# Introduce differential expression in 10 genes for "Case"
genomic_data[1:10, class_labels == "Case"] <-
genomic_data[1:10, class_labels == "Case"] + 3
rownames(genomic_data) <- paste0("Gene", 1:n_genes)
colnames(genomic_data) <- paste0("Sample", 1:n_samples)
pheatmap(genomic_data,
cluster_rows = TRUE, cluster_cols = TRUE,
main = "Simulated Gene Expression Data")
⚙ Run the genetic algorithm
We define parameters and run BioGA:
result <- bioga_main_cpp(
genomic_data = genomic_data,
population_size = 30,
num_generations = 50,
crossover_rate = 0.9,
eta_c = 20.0,
mutation_rate = 0.1,
num_parents = 20,
num_offspring = 20,
num_to_replace = 10,
weights = c(1.0, 0.5),
seed = 42
)
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#> Current front size: 1📊 Visualize convergence of fitness
Let’s compute and plot how the best fitness evolved.
# Since BioGA currently does not return fitness history,
# let’s re-run manually to track convergence:
track_fitness <- function(...) {
pop <- initialize_population_cpp(...)
best_fit <- c()
for (g in 1:50) {
fit <- evaluate_fitness_cpp(genomic_data, pop, weights = c(1.0, 0.5))
best_fit <- c(best_fit, min(fit[, 1])) # Track expression objective
parents <- selection_cpp(pop, fit, 20)
offspring <- crossover_cpp(parents, 20)
mutated <- mutation_cpp(offspring, 0.1, g, 50)
fit_off <- evaluate_fitness_cpp(genomic_data, mutated, c(1.0, 0.5))
pop <- replacement_cpp(pop, mutated, fit, fit_off, 10)
}
best_fit
}
fitness_trace <- track_fitness(genomic_data, 30, 42)
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#> Warning: No non-dominated individuals found. Using full population for selection.
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qplot(1:50, fitness_trace, geom = "line") +
labs(x = "Generation", y = "Best Fitness (Expression Diff.)",
title = "Convergence of Best Fitness Across Generations")
🎨 Population diversity heatmap
Visualize population at final generation
pheatmap(result$population,
main = "Final Population of Individuals",
cluster_rows = TRUE, cluster_cols = TRUE)
🧬 Compare gene selection frequency
gene_selection_freq <- colMeans(result$population != 0)
barplot(gene_selection_freq, las = 2,
main = "Frequency of Gene Selection in Final Population",
ylab = "Selection Frequency")
🌐 Network constraint example (optional)
If you had a network, e.g. co-expression network:
network_mat <- matrix(runif(n_genes^2, 0, 1), nrow = n_genes)
diag(network_mat) <- 0
mutated_with_net <- mutation_cpp(result$population,
mutation_rate = 0.1,
iteration = 10,
max_iterations = 50,
network = network_mat)
pheatmap(mutated_with_net,
main = "Population After Mutation with Network Constraint")
📌 Summary
In this vignette, we:
- simulated realistic genomic data
- ran
BioGAfor multi-objective optimization - tracked fitness convergence
- visualized population diversity and gene selection
💡 Final tip
For larger datasets, we recommend:
- using
RcppParallel::setThreadOptions()to set threads - saving intermediate generations if you want reproducibility or debugging
✉ Want help improving this further?
👉 Contact me: danymukesha@gmail.com
Session Info
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