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Implement all methods using positions additionally
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5 changed files with 222 additions and 167 deletions
195
R/neural.R
195
R/neural.R
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@ -1,114 +1,131 @@
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# Find genes by training a neural network on reference position data.
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#
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# @param seed A seed to get reproducible results.
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neural <- function(preset, progress = NULL, seed = 448077) {
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neural <- function(preset,
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use_positions = FALSE,
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progress = NULL,
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seed = 448077) {
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species_ids <- preset$species_ids
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gene_ids <- preset$gene_ids
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reference_gene_ids <- preset$reference_gene_ids
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cached("neural", c(species_ids, gene_ids, reference_gene_ids), {
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set.seed(seed)
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gene_count <- length(gene_ids)
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cached(
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"neural",
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c(species_ids, gene_ids, reference_gene_ids, use_positions),
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{ # nolint
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set.seed(seed)
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gene_count <- length(gene_ids)
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# Prefilter distances by species.
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distances <- geposan::distances[species %chin% species_ids]
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# Prefilter distances by species.
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distances <- geposan::distances[species %chin% species_ids]
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# Input data for the network. This contains the gene ID as an identifier
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# as well as the per-species gene distances as input variables.
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data <- data.table(gene = gene_ids)
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# Input data for the network. This contains the gene ID as an
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# identifier as well as the per-species gene distances as input
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# variables.
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data <- data.table(gene = gene_ids)
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# Buffer to keep track of species included in the computation. Species
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# from `species_ids` may be excluded if they don't have enough data.
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species_ids_included <- NULL
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# Buffer to keep track of species included in the computation.
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# Species from `species_ids` may be excluded if they don't have
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# enough data.
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species_ids_included <- NULL
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# Make a column containing distance data for each species.
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for (species_id in species_ids) {
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species_distances <- distances[
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species == species_id,
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.(gene, distance)
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# Make a column containing distance data for each species.
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for (species_id in species_ids) {
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species_data <- if (use_positions) {
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setnames(distances[
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species == species_id,
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.(gene, position)
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], "position", "distance")
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} else {
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distances[
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species == species_id,
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.(gene, distance)
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]
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}
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# Only include species with at least 25% known values.
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species_distances <- stats::na.omit(species_data)
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if (nrow(species_distances) >= 0.25 * gene_count) {
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species_ids_included <- c(species_ids_included, species_id)
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data <- merge(data, species_distances, all.x = TRUE)
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# Replace missing data with mean values. The neural network
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# can't handle NAs in a meaningful way. Choosing extreme
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# values here would result in heavily biased results.
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# Therefore, the mean value is chosen as a compromise.
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# However, this will of course lessen the significance of
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# the results.
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mean_distance <- round(species_distances[, mean(distance)])
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data[is.na(distance), distance := mean_distance]
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# Name the new column after the species.
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setnames(data, "distance", species_id)
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}
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}
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# Extract the reference genes.
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reference_data <- data[gene %chin% reference_gene_ids]
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reference_data[, neural := 1.0]
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# Take out random samples from the remaining genes. This is another
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# compromise with a negative impact on significance. Because there
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# is no information on genes with are explicitely *not* TPE-OLD
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# genes, we have to assume that a random sample of genes has a low
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# probability of including TPE-OLD genes.
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without_reference_data <- data[!gene %chin% reference_gene_ids]
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reference_samples <- without_reference_data[
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sample(
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nrow(without_reference_data),
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nrow(reference_data)
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)
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]
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# Only include species with at least 25% known values.
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reference_samples[, neural := 0.0]
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species_distances <- stats::na.omit(species_distances)
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# Merge training data. The training data includes all reference
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# genes as well as an equal number of random sample genes.
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training_data <- rbindlist(list(reference_data, reference_samples))
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if (nrow(species_distances) >= 0.25 * gene_count) {
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species_ids_included <- c(species_ids_included, species_id)
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data <- merge(data, species_distances, all.x = TRUE)
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# Construct and train the neural network.
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# Replace missing data with mean values. The neural network
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# can't handle NAs in a meaningful way. Choosing extreme values
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# here would result in heavily biased results. Therefore, the
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# mean value is chosen as a compromise. However, this will of
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# course lessen the significance of the results.
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nn_formula <- stats::as.formula(sprintf(
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"neural~%s",
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paste(species_ids_included, collapse = "+")
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))
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mean_distance <- round(species_distances[, mean(distance)])
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data[is.na(distance), distance := mean_distance]
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layer1 <- length(species_ids) * 0.66
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layer2 <- layer1 * 0.66
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layer3 <- layer2 * 0.66
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# Name the new column after the species.
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setnames(data, "distance", species_id)
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}
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}
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# Extract the reference genes.
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reference_data <- data[gene %chin% reference_gene_ids]
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reference_data[, neural := 1.0]
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# Take out random samples from the remaining genes. This is another
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# compromise with a negative impact on significance. Because there is
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# no information on genes with are explicitely *not* TPE-OLD genes, we
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# have to assume that a random sample of genes has a low probability of
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# including TPE-OLD genes.
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without_reference_data <- data[!gene %chin% reference_gene_ids]
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reference_samples <- without_reference_data[
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sample(
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nrow(without_reference_data),
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nrow(reference_data)
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nn <- neuralnet::neuralnet(
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nn_formula,
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training_data,
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hidden = c(layer1, layer2, layer3),
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linear.output = FALSE
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)
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]
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reference_samples[, neural := 0.0]
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if (!is.null(progress)) {
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# We do everything in one go, so it's not possible to report
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# detailed progress information. As the method is relatively
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# quick, this should not be a problem.
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progress(0.5)
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}
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# Merge training data. The training data includes all reference genes as
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# well as an equal number of random sample genes.
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training_data <- rbindlist(list(reference_data, reference_samples))
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# Apply the neural network.
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data[, score := neuralnet::compute(nn, data)$net.result]
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# Construct and train the neural network.
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if (!is.null(progress)) {
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# See above.
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progress(1.0)
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}
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nn_formula <- stats::as.formula(sprintf(
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"neural~%s",
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paste(species_ids_included, collapse = "+")
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))
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layer1 <- length(species_ids) * 0.66
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layer2 <- layer1 * 0.66
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layer3 <- layer2 * 0.66
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nn <- neuralnet::neuralnet(
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nn_formula,
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training_data,
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hidden = c(layer1, layer2, layer3),
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linear.output = FALSE
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)
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if (!is.null(progress)) {
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# We do everything in one go, so it's not possible to report
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# detailed progress information. As the method is relatively quick,
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# this should not be a problem.
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progress(0.5)
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data[, .(gene, score)]
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}
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# Apply the neural network.
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data[, score := neuralnet::compute(nn, data)$net.result]
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if (!is.null(progress)) {
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# See above.
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progress(1.0)
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}
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data[, .(gene, score)]
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})
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)
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}
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