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Reindent code to use just two spaces

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This commit is contained in:
Elias Projahn 2022-05-26 12:42:19 +02:00
parent a1e6147466
commit c04b6337e9
17 changed files with 1583 additions and 1582 deletions
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149
R/method_correlation.R
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@ -8,96 +8,97 @@
#'
#' @export
correlation <- function(summarize = stats::median) {
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$reference_gene_ids
method(
id = "correlation",
name = "Correlation",
description = "Correlation with reference genes",
function(preset, progress) {
species_ids <- preset$species_ids
gene_ids <- preset$gene_ids
reference_gene_ids <- preset$reference_gene_ids
cached(
"correlation",
c(species_ids, gene_ids, reference_gene_ids, summarize),
{ # nolint
# Prefilter distances by species.
distances <- geposan::distances[species %chin% species_ids]
cached(
"correlation",
c(species_ids, gene_ids, reference_gene_ids, summarize),
{ # 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")
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]
}
progress(0.66)
progress(0.66)
# Combine the correlation coefficients.
results[,
max_correlation := as.double(summarize(stats::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
]
# Combine the correlation coefficients.
results[,
max_correlation := as.double(summarize(stats::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
]
# Normalize scores.
results[,
score := (max_correlation - min(max_correlation)) /
(max(max_correlation) - min(max_correlation))
]
# Normalize scores.
results[
,
score := (max_correlation - min(max_correlation)) /
(max(max_correlation) - min(max_correlation))
]
# Normalize scores.
# Normalize scores.
results[, .(gene, score)]
results[, .(gene, score)]
result(
method = "correlation",
scores = results[, .(gene, score)],
details = list(all_correlations = results)
)
}
)
result(
method = "correlation",
scores = results[, .(gene, score)],
details = list(all_correlations = results)
)
}
)
)
}
)
}