geposan/R/neural.R

# Find genes by training a neural network on reference position data.
#
# @param seed A seed to get reproducible results.
neural <- function(preset, progress = NULL, seed = 448077) {
    species_ids <- preset$species_ids
    reference_gene_ids <- preset$reference_gene_ids

    set.seed(seed)
    gene_count <- length(preset$gene_ids)

    # Prefilter distances by species.
    distances <- geposan::distances[species %chin% species_ids]

    # Input data for the network. This contains the gene ID as an identifier
    # as well as the per-species gene distances as input variables.
    data <- data.table(gene = preset$gene_ids)

    # Buffer to keep track of species included in the computation. Species
    # from `species_ids` may be excluded if they don't have enough data.
    species_ids_included <- NULL

    # Make a column containing distance data for each species.
    for (species_id in species_ids) {
        species_distances <- distances[species == species_id, .(gene, distance)]

        # Only include species with at least 25% known values.

        species_distances <- stats::na.omit(species_distances)

        if (nrow(species_distances) >= 0.25 * gene_count) {
            species_ids_included <- c(species_ids_included, species_id)
            data <- merge(data, species_distances, all.x = TRUE)

            # Replace missing data with mean values. The neural network can't
            # handle NAs in a meaningful way. Choosing extreme values here
            # would result in heavily biased results. Therefore, the mean value
            # is chosen as a compromise. However, this will of course lessen the
            # significance of the results.

            mean_distance <- round(species_distances[, mean(distance)])
            data[is.na(distance), distance := mean_distance]

            # Name the new column after the species.
            setnames(data, "distance", species_id)
        }
    }

    # Extract the reference genes.

    reference_data <- data[gene %chin% reference_gene_ids]
    reference_data[, neural := 1.0]

    # Take out random samples from the remaining genes. This is another
    # compromise with a negative impact on significance. Because there is no
    # information on genes with are explicitely *not* TPE-OLD genes, we have to
    # assume that a random sample of genes has a low probability of including
    # TPE-OLD genes.

    without_reference_data <- data[!gene %chin% reference_gene_ids]

    reference_samples <- without_reference_data[
        sample(
            nrow(without_reference_data),
            nrow(reference_data)
        )
    ]

    reference_samples[, neural := 0.0]

    # Merge training data. The training data includes all reference genes as
    # well as an equal number of random sample genes.
    training_data <- rbindlist(list(reference_data, reference_samples))

    # Construct and train the neural network.

    nn_formula <- stats::as.formula(sprintf(
        "neural~%s",
        paste(species_ids_included, collapse = "+")
    ))

    layer1 <- length(species_ids) * 0.66
    layer2 <- layer1 * 0.66
    layer3 <- layer2 * 0.66

    nn <- neuralnet::neuralnet(
        nn_formula,
        training_data,
        hidden = c(layer1, layer2, layer3),
        linear.output = FALSE
    )

    if (!is.null(progress)) {
        # We do everything in one go, so it's not possible to report detailed
        # progress information. As the method is relatively quick, this should
        # not be a problem.
        progress(0.5)
    }

    # Apply the neural network.
    data[, score := neuralnet::compute(nn, data)$net.result]

    if (!is.null(progress)) {
        # See above.
        progress(1.0)
    }

    data[, .(gene, score)]
}