plot_aug {VAExprs}R Documentation

Visualization for augmented data

Description

For augmented data, we can create plots for specific types of dimension reduction.

Usage

plot_aug(x, plot_fun, ...)

Arguments

x

result of the function "gen_exprs"

plot_fun

"PCA", "MDS", "TSNE", "UMAP", "NMF", or "DiffusionMap"

...

additional parameters for the reduced dimension plots such as "scater::runPCA"

Value

plot for augmented data

Author(s)

Dongmin Jung

See Also

SingleCellExperiment::SingleCellExperiment, scater::logNormCounts, scater::runPCA, scater::runMDS, scater::runTSNE, scater::runUMAP, scater::runNMF, scater::runDiffusionMap, scater::plotPCA, scater::plotMDS, scater::plotTSNE, scater::plotUMAP, scater::plotNMF, scater::plotDiffusionMap

Examples

if (keras::is_keras_available() & reticulate::py_available() & reticulate::py_module_available("rpytools")) {
    ### 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)
    # plot
    plot_aug(gen_sample_result, "PCA")
}

[Package VAExprs version 0.99.22 Index]