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Add species adjacency method

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Elias Projahn 2022-01-17 20:11:07 +01:00
parent c8f1e522f9
commit 53f955f3da
6 changed files with 202 additions and 14 deletions
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#' Score genes based on their adjacency to the reference genes within species.
#'
#' For each gene and species, the method will first combine the gene's distances
#' to the reference genes within that species. Afterwards, the results are
#' summarized across species and determine the gene's score.
#'
#' @param distance_estimate Function for combining the distance differences
#' within one species.
#' @param summarize Function for summarizing the distance values across species.
#'
#' @return An object of class `geposan_method`.
#'
#' @seealso [adjacency()]
#'
#' @export
species_adjacency <- function(distance_estimate = min,
summarize = stats::median) {
method(
id = "species_adjacency",
name = "Species adj.",
description = "Species adjacency",
function(preset, progress) {
species_ids <- preset$species_ids
gene_ids <- preset$gene_ids
reference_gene_ids <- preset$reference_gene_ids
cached(
"species_adjacency",
c(
species_ids,
gene_ids,
reference_gene_ids,
distance_estimate,
summarize
),
{ # nolint
# Prefilter distances.
data <- geposan::distances[
species %chin% species_ids & gene %chin% gene_ids
]
progress_state <- 0.0
progress_step <- 0.9 / length(species_ids)
# Iterate through all species and find the distance
# estimates within that species.
for (species_id in species_ids) {
# For all genes, compute the distance to one reference
# gene at a time in one go.
for (reference_gene_id in reference_gene_ids) {
comparison_distance <- data[
species == species_id &
gene == reference_gene_id,
distance
]
column <- quote(reference_gene_id)
if (length(comparison_distance) != 1) {
# If we don't have a comparison distance, we
# can't compute a difference. This happens, if
# the species doesn't have the reference gene.
data[
species == species_id &
gene %chin% gene_ids,
eval(column) := NA_integer_
]
} else {
data[
species == species_id &
gene %chin% gene_ids,
eval(column) :=
abs(distance - comparison_distance)
]
}
}
# Combine the distances to the different reference genes
# into one value using the provided function.
data[
species == species_id &
gene %chin% gene_ids,
combined_distance := as.numeric(
distance_estimate(na.omit(
# Convert the data.table subset into a
# vector to get the correct na.omit
# behavior.
as.matrix(.SD)[1, ]
))
),
.SDcols = reference_gene_ids,
by = gene
]
progress_state <- progress_state + progress_step
progress(progress_state)
}
progress(0.9)
# Remove the distances between the reference genes.
for (reference_gene_id in reference_gene_ids) {
column <- quote(reference_gene_id)
data[gene == reference_gene_id, eval(column) := NA]
}
# Recompute the combined distance for the reference genes.
data[
gene %chin% reference_gene_ids,
combined_distance := as.numeric(
distance_estimate(na.omit(as.matrix(.SD)[1, ]))
),
.SDcols = reference_gene_ids,
by = list(species, gene)
]
# Combine the distances into one value.
results <- data[,
.(
summarized_distances = as.numeric(
summarize(na.omit(combined_distance))
)
),
by = gene
]
# Compute the final score by normalizing the difference.
results[
,
score := 1 - summarized_distances /
max(summarized_distances)
]
progress(1.0)
result(
method = "species_adjacency",
scores = results[, .(gene, score)],
details = list(
data = data,
results = results
)
)
}
)
}
)
}