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Restructure classes and their responsibilities

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This commit is contained in:
Elias Projahn 2021-12-16 13:01:44 +01:00
parent 01ec301d6d
commit e2b93babe5
27 changed files with 974 additions and 634 deletions
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151
R/correlation.R
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@ -1,88 +1,101 @@
# Compute the mean correlation coefficient comparing gene distances with a set
# of reference genes.
correlation <- function(preset, progress = NULL) {
species_ids <- preset$species_ids
gene_ids <- preset$gene_ids
reference_gene_ids <- preset$reference_gene_ids
#' Compute the mean correlation coefficient comparing gene distances with a set
#' of reference genes.
#'
#' @return An object of class `geposan_method`.
#'
#' @export
correlation <- function() {
method(
id = "correlation",
name = "Correlation",
description = "Correlation with reference genes",
function(preset, progress) {
species_ids <- preset$species_ids
gene_ids <- preset$gene_ids
reference_gene_ids <- preset$reference_gene_ids
cached(
"correlation", c(species_ids, gene_ids, reference_gene_ids), {
# Prefilter distances by species.
distances <- geposan::distances[species %chin% species_ids]
cached(
"correlation",
c(species_ids, gene_ids, reference_gene_ids),
{ # nolint
# Prefilter distances by species.
distances <- geposan::distances[species %chin% species_ids]
# Tranform data to get species as rows and genes as columns. We
# construct columns per species, because it requires fewer
# iterations, and transpose the table afterwards.
# Tranform data to get species as rows and genes as columns.
# We construct columns per species, because it requires
# fewer iterations, and transpose the table afterwards.
data <- data.table(gene = gene_ids)
data <- data.table(gene = gene_ids)
# Make a column containing distance data for each species.
for (species_id in species_ids) {
species_data <- distances[
species == species_id,
.(gene, distance)
]
# Make a column containing distance data for each species.
for (species_id in species_ids) {
species_data <- distances[
species == species_id,
.(gene, distance)
]
data <- merge(data, species_data, all.x = TRUE)
setnames(data, "distance", species_id)
}
data <- merge(data, species_data, all.x = TRUE)
setnames(data, "distance", species_id)
}
# Transpose to the desired format.
data <- transpose(data, make.names = "gene")
# Transpose to the desired format.
data <- transpose(data, make.names = "gene")
if (!is.null(progress)) progress(0.33)
progress(0.33)
# Take the reference data.
reference_data <- data[, ..reference_gene_ids]
# Take the reference data.
reference_data <- data[, ..reference_gene_ids]
# Perform the correlation between all possible pairs.
results <- stats::cor(
data[, ..gene_ids],
reference_data,
use = "pairwise.complete.obs",
method = "spearman"
)
# Perform the correlation between all possible pairs.
results <- stats::cor(
data[, ..gene_ids],
reference_data,
use = "pairwise.complete.obs",
method = "spearman"
)
results <- data.table(results, keep.rownames = TRUE)
setnames(results, "rn", "gene")
results <- data.table(results, keep.rownames = TRUE)
setnames(results, "rn", "gene")
# Remove correlations between the reference genes themselves.
for (reference_gene_id in reference_gene_ids) {
column <- quote(reference_gene_id)
results[gene == reference_gene_id, eval(column) := NA]
}
# Remove correlations between the reference genes
# themselves.
for (reference_gene_id in reference_gene_ids) {
column <- quote(reference_gene_id)
results[gene == reference_gene_id, eval(column) := NA]
}
if (!is.null(progress)) progress(0.66)
progress(0.66)
# Compute the final score as the mean of known correlation scores.
# Negative correlations will correctly lessen the score, which will
# be clamped to zero as its lower bound. Genes with no possible
# correlations at all will be assumed to have a score of 0.0.
# Compute the final score as the mean of known correlation
# scores. Negative correlations will correctly lessen the
# score, which will be clamped to zero as its lower bound.
# Genes with no possible correlations at all will be assumed
# to have a score of 0.0.
compute_score <- function(scores) {
score <- mean(scores, na.rm = TRUE)
compute_score <- function(scores) {
score <- mean(scores, na.rm = TRUE)
if (is.na(score) | score < 0.0) {
score <- 0.0
if (is.na(score) | score < 0.0) {
score <- 0.0
}
score
}
results[,
score := compute_score(as.matrix(.SD)),
.SDcols = reference_gene_ids,
by = gene
]
results[, .(gene, score)]
result(
method = "correlation",
scores = results[, .(gene, score)],
details = list(all_correlations = results)
)
}
score
}
results[,
score := compute_score(as.matrix(.SD)),
.SDcols = reference_gene_ids,
by = gene
]
results[, .(gene, score)]
structure(
list(
results = results[, .(gene, score)],
all_correlations = results
),
class = "geposan_method_results"
)
}
)