gen_exprs: Generate samples with expression data

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

Description

This function generate expression data by drawing samples from the latent vectors following the standard multivariate Gaussian distribution (the standard multivariate normal distribution) for convenience. However, this assumption for the prior may not be appropriate because there may be underlying distinctions between groups of samples. Any density function can be modeled by the Gaussian mixture model. Here, by using the library "mclust", the finite Gaussian mixture is applied for such sampling. Note that the Gaussian mixture model is not used for fitting in the function "fit_vae".

Usage

gen_exprs(x, num_samples,
        batch_size, use_generator = FALSE)

Arguments

x	result of the function "fit_vae"
num_samples	number of samples to be generated
batch_size	batch size
use_generator	use data generator if TRUE (default: FALSE)

Value

x_gen	generated expression data, where each row is a cell and each column is a gene
y_gen	geneated labels
x_train	real expression data, where each row is a cell and each column is a gene
y_train	real labels
latent_vector	latent vector from real expression data

Author(s)

Dongmin Jung

See Also

mclust::mclustBIC, mclust::mclustModel, mclust::sim, DeepPINCS::multiple_sampling_generator, gradDescent::minmaxDescaling, CatEncoders::inverse.transform

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)


### generate samples
set.seed(1)
gen_sample_result <- gen_exprs(vae_result, num_samples = 100)

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