plot_vae: Visualization for the variational autoencoder

In dongminjung/VAExprs: Genearting Samples of Gene Expression Data with Variational Autoencoders

Description

You can create a plot of the VAE model. This plot can help you check that the model is connected the way you intended. The node colors indicate the components of the VAE.

Usage

plot_vae(x, node_color = list(encoder_col = "tomato",
                            mean_vector_col = "orange",
                            stddev_vector_col = "lavender",
                            latent_vector_col = "lightblue",
                            decoder_col = "palegreen",
                            condition_col = "gray"))

Arguments

x	VAE model
node_color	node colors for encoder(default: tomato), mean vector(default: orange), standard deviation vector(default: lavender), latent_vector(default: lightblue), decoder(default: palegreen), and condition(default: gray)

Value

plot for the model architecture

Author(s)

Dongmin Jung

See Also

purrr::map, purrr::map_chr, purrr::pluck, purrr::imap_dfr, DiagrammeR::grViz

Examples

### simulate differentially expressed genes
set.seed(1)
g <- 3
n <- 100
m <- 1000
mu <- 5
sigma <- 5
mat <- matrix(rnorm(n*m*g, mu, sigma), m, n*g)
rownames(mat) <- paste0("gene", seq_len(m))
colnames(mat) <- paste0("cell", seq_len(n*g))
group <- factor(sapply(seq_len(g), function(x) { 
    rep(paste0("group", x), n)
}))
names(group) <- colnames(mat)
mu_upreg <- 6
sigma_upreg <- 10
deg <- 100
for (i in seq_len(g)) {
    mat[(deg*(i-1) + 1):(deg*i), group == paste0("group", i)] <- 
        mat[1:deg, group==paste0("group", i)] + rnorm(deg, mu_upreg, sigma_upreg)
}
# positive expression only
mat[mat < 0] <- 0
x_train <- as.matrix(t(mat))


### model
batch_size <- 32
original_dim <- 1000
intermediate_dim <- 512
epochs <- 2
# VAE
vae_result <- fit_vae(x_train = x_train,
                    encoder_layers = list(layer_input(shape = c(original_dim)),
                                        layer_dense(units = intermediate_dim,
                                                    activation = "relu")),
                    decoder_layers = list(layer_dense(units = intermediate_dim,
                                                    activation = "relu"),
                                        layer_dense(units = original_dim,
                                                    activation = "sigmoid")),
                    epochs = epochs, batch_size = batch_size,
                    validation_split = 0.5,
                    use_generator = FALSE,
                    callbacks = keras::callback_early_stopping(
                        monitor = "val_loss",
                        patience = 10,
                        restore_best_weights = TRUE))
# plot
plot_vae(vae_result$model)

dongminjung/VAExprs documentation built on Dec. 20, 2021, 12:13 a.m.