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neural: Readd distances as input data
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parent
88d6837fee
commit
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2 changed files with 24 additions and 11 deletions
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@ -40,8 +40,11 @@ analyze <- function(preset, progress = NULL) {
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"correlation_positions" = function(...) {
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correlation(..., use_positions = TRUE)
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},
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"proximity" = proximity,
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"neural" = neural
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"neural" = neural,
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"neural_positions" = function(...) {
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neural(..., use_positions = TRUE)
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},
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"proximity" = proximity
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)
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results <- cached("analysis", preset, {
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28
R/neural.R
28
R/neural.R
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@ -1,14 +1,17 @@
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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(
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"neural",
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c(species_ids, gene_ids, reference_gene_ids),
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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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@ -28,10 +31,17 @@ neural <- function(preset, progress = NULL, seed = 448077) {
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# Make a column containing positions for each species.
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for (species_id in species_ids) {
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species_data <- distances[
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species == species_id,
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.(gene, position)
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]
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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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@ -48,11 +58,11 @@ neural <- function(preset, progress = NULL, seed = 448077) {
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# However, this will of course lessen the significance of
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# the results.
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mean_position <- round(species_data[, mean(position)])
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data[is.na(position), position := mean_position]
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mean_distance <- round(species_data[, 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, "position", species_id)
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setnames(data, "distance", species_id)
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}
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}
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