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preset: Filter species in addition to genes

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This commit is contained in:
Elias Projahn 2022-05-30 13:49:52 +02:00
parent 9e96c54f23
commit 3217c9bd29
4 changed files with 49 additions and 48 deletions
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24
R/method_neural.R
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@ -7,8 +7,6 @@
#' final score will be the mean of the result of applying the different
#' models. There should be at least two training sets. The analysis will only
#' work, if there is at least one reference gene per training set.
#' @param gene_requirement Minimum proportion of genes from the preset that a
#' species has to have in order to be included in the models.
#' @param control_ratio The proportion of random control genes that is included
#' in the training data sets in addition to the reference genes. This should
#' be a numeric value between 0.0 and 1.0.
@ -16,10 +14,7 @@
#' @return An object of class `geposan_method`.
#'
#' @export
neural <- function(seed = 180199,
n_models = 5,
gene_requirement = 0.5,
control_ratio = 0.5) {
neural <- function(seed = 180199, n_models = 5, control_ratio = 0.5) {
method(
id = "neural",
name = "Neural",
@ -37,7 +32,6 @@ neural <- function(seed = 180199,
reference_gene_ids,
seed,
n_models,
gene_requirement,
control_ratio
),
{ # nolint
@ -57,12 +51,6 @@ neural <- function(seed = 180199,
distances <- geposan::distances[species %chin% species_ids &
gene %chin% gene_ids]
# Only include species that have at least 25% of the included genes.
distances[, species_n_genes := .N, by = species]
distances <- distances[species_n_genes >=
gene_requirement * length(gene_ids)]
included_species <- distances[, unique(species)]
# Reshape data to put species into columns.
data <- dcast(
distances,
@ -72,7 +60,7 @@ neural <- function(seed = 180199,
# Replace values that are still missing with mean values for the
# species in question.
data[, (included_species) := lapply(included_species, \(species) {
data[, (species_ids) := lapply(species_ids, \(species) {
species <- get(species)
species[is.na(species)] <- mean(species, na.rm = TRUE)
species
@ -129,7 +117,7 @@ neural <- function(seed = 180199,
# Step 3: Create, train and apply neural network.
# -----------------------------------------------
data_matrix <- prepare_data(data, included_species)
data_matrix <- prepare_data(data, species_ids)
output_vars <- NULL
for (i in seq_along(networks)) {
@ -138,14 +126,14 @@ neural <- function(seed = 180199,
# Create a new model for each training session, because
# the model would keep its state across training
# sessions otherwise.
model <- create_model(length(included_species))
model <- create_model(length(species_ids))
# Train the model.
fit <- train_model(
model,
network$training_data,
network$validation_data,
included_species
species_ids
)
# Apply the model.
@ -180,7 +168,7 @@ neural <- function(seed = 180199,
details = list(
seed = seed,
n_models = n_models,
all_results = data[, !..included_species],
all_results = data[, !..species_ids],
networks = networks
)
)

44
R/preset.R
View file

@ -3,16 +3,19 @@
#' A preset is used to specify which methods and inputs should be used for an
#' analysis. Note that the genes to process should normally include the
#' reference genes to be able to assess the results later. The genes will be
#' filtered based on how many species have data for them. Genes which only have
#' orthologs for less than 25% of the input species will be excluded from the
#' preset and the analyis. See the different method functions for the available
#' methods: [clustering()], [correlation()], [neural()], [adjacency()] and
#' [species_adjacency()].
#' filtered based on how many species have data for them. Afterwards, species
#' that still have many missing genes will also be excluded. See the different
#' method functions for the available methods: [clustering()], [correlation()],
#' [neural()], [adjacency()] and [species_adjacency()].
#'
#' @param reference_gene_ids IDs of reference genes to compare to.
#' @param methods List of methods to apply.
#' @param species_ids IDs of species to include.
#' @param gene_ids IDs of genes to screen.
#' @param species_requirement The proportion of species a gene has to have
#' orthologs in in order for the gene to qualify.
#' @param gene_requirement The proportion of genes that a species has to have
#' in order for the species to be included in the analysis.
#'
#' @return The preset to use with [analyze()].
#'
@ -20,21 +23,32 @@
preset <- function(reference_gene_ids,
methods = all_methods(),
species_ids = geposan::species$id,
gene_ids = geposan::genes$id) {
# Count included species per gene.
genes_n_species <- geposan::distances[
species %chin% species_ids,
.(n_species = .N),
by = "gene"
gene_ids = geposan::genes$id,
species_requirement = 0.25,
gene_requirement = 0.5) {
# Prefilter distances.
distances <- geposan::distances[
species %chin% species_ids & gene %chin% gene_ids
]
# Filter out genes with less than 25% existing orthologs.
# Count included species per gene.
genes_n_species <- distances[, .(n_species = .N), by = "gene"]
# Filter out genes with less too few existing orthologs.
gene_ids_filtered <- genes_n_species[
gene %chin% gene_ids &
n_species >= 0.25 * length(species_ids),
n_species >= species_requirement * length(species_ids),
gene
]
# Count included genes per species.
species_n_genes <- geposan::distances[, .(n_genes = .N), by = "species"]
# Filter out species that have too few of the genes.
species_ids_filtered <- species_n_genes[
n_genes >= gene_requirement * length(gene_ids_filtered),
species
]
reference_gene_ids_excluded <- reference_gene_ids[
!reference_gene_ids %chin% gene_ids_filtered
]
@ -65,7 +79,7 @@ preset <- function(reference_gene_ids,
list(
reference_gene_ids = sort(reference_gene_ids_included),
methods = methods,
species_ids = sort(species_ids),
species_ids = sort(species_ids_filtered),
gene_ids = sort(gene_ids_filtered)
),
class = "geposan_preset"