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Implement all methods using positions additionally

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This commit is contained in:
Elias Projahn 2021-11-05 19:49:54 +01:00
parent 9cbc127177
commit cfc5e7a6bf
5 changed files with 222 additions and 167 deletions
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20
R/analyze.R
View file

@ -35,7 +35,19 @@ analyze <- function(preset, progress = NULL) {
"clusteriness" = clusteriness,
"correlation" = correlation,
"proximity" = proximity,
"neural" = neural
"neural" = neural,
"clusteriness_positions" = function(...) {
clusteriness(..., use_positions = TRUE)
},
"correlation_positions" = function(...) {
correlation(..., use_positions = TRUE)
},
"proximity_positions" = function(...) {
proximity(..., use_positions = TRUE)
},
"neural_positions" = function(...) {
neural(..., use_positions = TRUE)
}
)
results <- cached("analysis", preset, {
@ -50,7 +62,11 @@ analyze <- function(preset, progress = NULL) {
}
}
method_results <- methods[[method_id]](preset, method_progress)
method_results <- methods[[method_id]](
preset,
progress = method_progress
)
setnames(method_results, "score", method_id)
results <- merge(

12
R/clusteriness.R
View file

@ -36,11 +36,11 @@ clusteriness_priv <- function(data, height = 1000000) {
}
# Process genes clustering their distance to telomeres.
clusteriness <- function(preset, progress = NULL) {
clusteriness <- function(preset, use_positions = FALSE, progress = NULL) {
species_ids <- preset$species_ids
gene_ids <- preset$gene_ids
cached("clusteriness", c(species_ids, gene_ids), {
cached("clusteriness", c(species_ids, gene_ids, use_positions), {
results <- data.table(gene = gene_ids)
# Prefilter the input data by species.
@ -54,7 +54,13 @@ clusteriness <- function(preset, progress = NULL) {
# Perform the cluster analysis for one gene.
compute <- function(gene_id) {
score <- clusteriness_priv(distances[gene_id, distance])
data <- if (use_positions) {
distances[gene_id, position]
} else {
distances[gene_id, distance]
}
score <- clusteriness_priv(data)
if (!is.null(progress)) {
genes_done <<- genes_done + 1

143
R/correlation.R
View file

@ -1,79 +1,90 @@
# Compute the mean correlation coefficient comparing gene distances with a set
# of reference genes.
correlation <- function(preset, progress = NULL) {
correlation <- function(preset, use_positions = FALSE, progress = NULL) {
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, use_positions),
{ # 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_distances <- distances[
species == species_id,
.(gene, distance)
]
data <- merge(data, species_distances, all.x = TRUE)
setnames(data, "distance", species_id)
}
# Transpose to the desired format.
data <- transpose(data, make.names = "gene")
if (!is.null(progress)) progress(0.33)
# 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"
)
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]
}
if (!is.null(progress)) 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_score <- function(scores) {
score <- mean(scores, na.rm = TRUE)
if (is.na(score) | score < 0.0) {
score <- 0.0
# Make a column containing distance data for each species.
for (species_id in species_ids) {
species_data <- if (use_positions) {
setnames(distances[
species == species_id,
.(gene, position)
], "position", "distance")
} else {
distances[
species == species_id,
.(gene, distance)
]
}
}
score
data <- merge(data, species_data, all.x = TRUE)
setnames(data, "distance", species_id)
# Transpose to the desired format.
data <- transpose(data, make.names = "gene")
if (!is.null(progress)) progress(0.33)
# 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"
)
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]
}
if (!is.null(progress)) 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_score <- function(scores) {
score <- mean(scores, na.rm = TRUE)
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)]
}
results[,
score := compute_score(as.matrix(.SD)),
.SDcols = reference_gene_ids,
by = gene
]
results[, .(gene, score)]
})
)
}

195
R/neural.R
View file

@ -1,114 +1,131 @@
# Find genes by training a neural network on reference position data.
#
# @param seed A seed to get reproducible results.
neural <- function(preset, progress = NULL, seed = 448077) {
neural <- function(preset,
use_positions = FALSE,
progress = NULL,
seed = 448077) {
species_ids <- preset$species_ids
gene_ids <- preset$gene_ids
reference_gene_ids <- preset$reference_gene_ids
cached("neural", c(species_ids, gene_ids, reference_gene_ids), {
set.seed(seed)
gene_count <- length(gene_ids)
cached(
"neural",
c(species_ids, gene_ids, reference_gene_ids, use_positions),
{ # nolint
set.seed(seed)
gene_count <- length(gene_ids)
# Prefilter distances by species.
distances <- geposan::distances[species %chin% species_ids]
# Prefilter distances by species.
distances <- geposan::distances[species %chin% species_ids]
# Input data for the network. This contains the gene ID as an identifier
# as well as the per-species gene distances as input variables.
data <- data.table(gene = gene_ids)
# Input data for the network. This contains the gene ID as an
# identifier as well as the per-species gene distances as input
# variables.
data <- data.table(gene = gene_ids)
# Buffer to keep track of species included in the computation. Species
# from `species_ids` may be excluded if they don't have enough data.
species_ids_included <- NULL
# Buffer to keep track of species included in the computation.
# Species from `species_ids` may be excluded if they don't have
# enough data.
species_ids_included <- NULL
# Make a column containing distance data for each species.
for (species_id in species_ids) {
species_distances <- distances[
species == species_id,
.(gene, distance)
# Make a column containing distance data for each species.
for (species_id in species_ids) {
species_data <- if (use_positions) {
setnames(distances[
species == species_id,
.(gene, position)
], "position", "distance")
} else {
distances[
species == species_id,
.(gene, distance)
]
}
# Only include species with at least 25% known values.
species_distances <- stats::na.omit(species_data)
if (nrow(species_distances) >= 0.25 * gene_count) {
species_ids_included <- c(species_ids_included, species_id)
data <- merge(data, species_distances, all.x = TRUE)
# Replace missing data with mean values. The neural network
# can't handle NAs in a meaningful way. Choosing extreme
# values here would result in heavily biased results.
# Therefore, the mean value is chosen as a compromise.
# However, this will of course lessen the significance of
# the results.
mean_distance <- round(species_distances[, mean(distance)])
data[is.na(distance), distance := mean_distance]
# Name the new column after the species.
setnames(data, "distance", species_id)
}
}
# Extract the reference genes.
reference_data <- data[gene %chin% reference_gene_ids]
reference_data[, neural := 1.0]
# Take out random samples from the remaining genes. This is another
# compromise with a negative impact on significance. Because there
# is no information on genes with are explicitely *not* TPE-OLD
# genes, we have to assume that a random sample of genes has a low
# probability of including TPE-OLD genes.
without_reference_data <- data[!gene %chin% reference_gene_ids]
reference_samples <- without_reference_data[
sample(
nrow(without_reference_data),
nrow(reference_data)
)
]
# Only include species with at least 25% known values.
reference_samples[, neural := 0.0]
species_distances <- stats::na.omit(species_distances)
# Merge training data. The training data includes all reference
# genes as well as an equal number of random sample genes.
training_data <- rbindlist(list(reference_data, reference_samples))
if (nrow(species_distances) >= 0.25 * gene_count) {
species_ids_included <- c(species_ids_included, species_id)
data <- merge(data, species_distances, all.x = TRUE)
# Construct and train the neural network.
# Replace missing data with mean values. The neural network
# can't handle NAs in a meaningful way. Choosing extreme values
# here would result in heavily biased results. Therefore, the
# mean value is chosen as a compromise. However, this will of
# course lessen the significance of the results.
nn_formula <- stats::as.formula(sprintf(
"neural~%s",
paste(species_ids_included, collapse = "+")
))
mean_distance <- round(species_distances[, mean(distance)])
data[is.na(distance), distance := mean_distance]
layer1 <- length(species_ids) * 0.66
layer2 <- layer1 * 0.66
layer3 <- layer2 * 0.66
# Name the new column after the species.
setnames(data, "distance", species_id)
}
}
# Extract the reference genes.
reference_data <- data[gene %chin% reference_gene_ids]
reference_data[, neural := 1.0]
# Take out random samples from the remaining genes. This is another
# compromise with a negative impact on significance. Because there is
# no information on genes with are explicitely *not* TPE-OLD genes, we
# have to assume that a random sample of genes has a low probability of
# including TPE-OLD genes.
without_reference_data <- data[!gene %chin% reference_gene_ids]
reference_samples <- without_reference_data[
sample(
nrow(without_reference_data),
nrow(reference_data)
nn <- neuralnet::neuralnet(
nn_formula,
training_data,
hidden = c(layer1, layer2, layer3),
linear.output = FALSE
)
]
reference_samples[, neural := 0.0]
if (!is.null(progress)) {
# We do everything in one go, so it's not possible to report
# detailed progress information. As the method is relatively
# quick, this should not be a problem.
progress(0.5)
}
# Merge training data. The training data includes all reference genes as
# well as an equal number of random sample genes.
training_data <- rbindlist(list(reference_data, reference_samples))
# Apply the neural network.
data[, score := neuralnet::compute(nn, data)$net.result]
# Construct and train the neural network.
if (!is.null(progress)) {
# See above.
progress(1.0)
}
nn_formula <- stats::as.formula(sprintf(
"neural~%s",
paste(species_ids_included, collapse = "+")
))
layer1 <- length(species_ids) * 0.66
layer2 <- layer1 * 0.66
layer3 <- layer2 * 0.66
nn <- neuralnet::neuralnet(
nn_formula,
training_data,
hidden = c(layer1, layer2, layer3),
linear.output = FALSE
)
if (!is.null(progress)) {
# We do everything in one go, so it's not possible to report
# detailed progress information. As the method is relatively quick,
# this should not be a problem.
progress(0.5)
data[, .(gene, score)]
}
# Apply the neural network.
data[, score := neuralnet::compute(nn, data)$net.result]
if (!is.null(progress)) {
# See above.
progress(1.0)
}
data[, .(gene, score)]
})
)
}

19
R/proximity.R
View file

@ -2,21 +2,26 @@
#
# A score will be given to each gene such that 0.0 corresponds to the maximal
# mean distance across all genes and 1.0 corresponds to a distance of 0.
proximity <- function(preset, progress = NULL) {
proximity <- function(preset, use_positions = FALSE, progress = NULL) {
species_ids <- preset$species_ids
gene_ids <- preset$gene_ids
cached("proximity", c(species_ids, gene_ids), {
cached("proximity", c(species_ids, gene_ids, use_positions), {
# Prefilter distances by species and gene.
distances <- geposan::distances[
data <- geposan::distances[
species %chin% preset$species_ids & gene %chin% preset$gene_ids
]
# Compute the score as described above.
distances <- distances[, .(mean_distance = mean(distance)), by = "gene"]
max_distance <- distances[, max(mean_distance)]
distances[, score := 1 - mean_distance / max_distance]
data <- if (use_positions) {
data[, .(mean_distance = mean(position)), by = "gene"]
} else {
data[, .(mean_distance = mean(distance)), by = "gene"]
}
max_distance <- data[, max(mean_distance)]
data[, score := 1 - mean_distance / max_distance]
if (!is.null(progress)) {
# We do everything in one go, so it's not possible to report
@ -25,6 +30,6 @@ proximity <- function(preset, progress = NULL) {
progress(1.0)
}
distances[, .(gene, score)]
data[, .(gene, score)]
})
}