johrpan/geposanui
1
0
Fork 0
mirror of https://github.com/johrpan/geposanui.git synced 2025-10-26 19:27:24 +01:00
Code Issues Projects Releases Packages Wiki Activity Actions

Separate out geposan package

Browse source
This commit is contained in:
Elias Projahn 2021-10-19 14:15:28 +02:00
parent 68354bf808
commit dc0265a13f
16 changed files with 154 additions and 731 deletions
Download patch file Download diff file Expand all files Collapse all files

115
data.R Normal file
View file

@ -0,0 +1,115 @@
library(data.table)
#' Species IDs of known replicatively aging species.
species_ids_replicative <- c(
"bihybrid",
"btaurus",
"bthybrid",
"cfamiliaris",
"chircus",
"cjacchus",
"clfamiliaris",
"csabaeus",
"ecaballus",
"fcatus",
"ggorilla",
"hsapiens",
"lafricana",
"mfascicularis",
"mmulatta",
"mmurinus",
"mnemestrina",
"nleucogenys",
"oaries",
"pabelii",
"panubis",
"ppaniscus",
"ptroglodytes",
"sscrofa",
"tgelada"
)
#' Species from [geposan] and their aging status.
species <- geposan::species[, .(
id,
name,
replicative = id %chin% species_ids_replicative
)]
#' Gene names of genes for verified TPE-OLD genes.
genes_verified_tpe_old <- c(
"C1S",
"DSP",
"ISG15",
"SORBS2",
"TERT"
)
#' Gene names of genes with a suggested TPE-OLD.
genes_suggested_tpe_old <- c(
"AKAP3",
"ANO2",
"CCND2",
"CD163L1",
"CD9",
"FOXM1",
"GALNT8",
"NDUFA9",
"TEAD4",
"TIGAR",
"TSPAN9"
)
#' Genes from [geposan] and their TPE-OLD status.
genes <- geposan::genes[, .(
id,
name,
chromosome,
n_species,
suggested = name %chin% genes_suggested_tpe_old,
verified = name %chin% genes_verified_tpe_old
)]
#' All available methods from [geposan] and additional information on them.
methods <- list(
list(
id = "clusteriness",
name = "Clustering",
description = "Clustering of genes"
),
list(
id = "correlation",
name = "Correlation",
description = "Correlation with known genes"
),
list(
id = "proximity",
name = "Proximity",
description = "Proximity to telomeres"
),
list(
id = "neural",
name = "Neural",
description = "Assessment by neural network"
)
)
#' Gene IDs of known or suggested TPE-OLD genes.
genes_tpe_old <- genes[suggested | verified == TRUE, id]
#' Preset for [geposan] including all species and TPE-OLD genes for reference.
preset_all_species <- geposan::preset(
methods = c("clusteriness", "correlation", "proximity", "neural"),
species = species$id,
genes = genes$id,
reference_genes = genes_tpe_old
)
#' Preset for [geposan] including only replicatively aging species as well as
#' TPE-OLD genes for reference.
preset_replicative_species <- geposan::preset(
methods = c("clusteriness", "correlation", "proximity", "neural"),
species = species_ids_replicative,
genes = genes$id,
reference_genes = genes_tpe_old
)

9
main.R Normal file
View file

@ -0,0 +1,9 @@
library(shiny)
# Initialize data first.
source("data.R")
source("server.R")
source("ui.R")
runApp(shinyApp(ui, server))

34
optimize.R
View file

@ -1,34 +0,0 @@
#' Find the best weights to rank the data.
#'
#' This function ranks the provided data table based on a weighted score
#' computed from the specified `columns`. It tries to find the optimal weights
#' that result in a ranking, where the mean rank of the given reference genes
#' is as high as possible.
#'
#' @param data Input data including the columns.
#' @param colums Columns containing the separate scores between 0.0 and 1.0.
#' @param reference_gene_ids IDs of the reference genes within the input data.
#'
#' @returns Vector of optimal column weights adding up to 1.0.
optimize_weights <- function(data, columns, reference_gene_ids) {
#' Compute the mean rank of the reference genes when applying the weights.
mean_rank <- function(weights) {
data <- copy(data)
data[, score := 0.0]
for (i in seq_along(columns)) {
column <- columns[i]
weighted <- weights[i] * data[, ..column]
data[, score := score + weighted]
}
setorder(data, -score)
data[, rank := .I]
data[gene %chin% reference_gene_ids, mean(rank)]
}
weights <- optim(rep(1.0, length(columns)), mean_rank)$par
total_weight <- sum(weights)
weights / total_weight
}

56
process/clusteriness.R
View file

@ -1,56 +0,0 @@
library(data.table)
#' Perform a cluster analysis.
#'
#' This function will cluster the data using `hclust` and `cutree` (with the
#' specified height). Every cluster with at least two members qualifies for
#' further analysis. Clusters are then ranked based on their size in relation
#' to the number of values. The return value is a final score between zero and
#' one. Lower ranking clusters contribute less to this score.
clusteriness <- function(data, height = 1000000) {
n <- length(data)
# Return a score of 0.0 if there is just one or no value at all.
if (n < 2) {
return(0.0)
}
# Cluster the data and compute the cluster sizes.
tree <- hclust(dist(data))
clusters <- cutree(tree, h = height)
cluster_sizes <- sort(tabulate(clusters), decreasing = TRUE)
# Compute the "clusteriness" score.
score <- 0.0
for (i in seq_along(cluster_sizes)) {
cluster_size <- cluster_sizes[i]
if (cluster_size >= 2) {
cluster_score <- cluster_size / n
score <- score + cluster_score / i
}
}
score
}
#' Process genes clustering their distance to telomeres.
process_clusteriness <- function(distances, gene_ids, preset) {
results <- data.table(gene = gene_ids)
# Prefilter the input data by species.
distances <- distances[species %chin% preset$species_ids]
# Add an index for quickly accessing data per gene.
setkey(distances, gene)
#' Perform the cluster analysis for one gene.
compute <- function(gene_id) {
clusteriness(distances[gene_id, distance])
}
results[, score := compute(gene), by = 1:nrow(results)]
}

63
process/correlation.R
View file

@ -1,63 +0,0 @@
library(data.table)
#' Compute the mean correlation coefficient comparing gene distances with a set
#' of reference genes.
process_correlation <- function(distances, gene_ids, preset) {
results <- data.table(gene = gene_ids)
reference_gene_ids <- preset$reference_gene_ids
reference_count <- length(reference_gene_ids)
# Prefilter distances by species.
distances <- distances[species %chin% preset$species_ids]
# Add an index for quickly accessing data per gene.
setkey(distances, gene)
# Prepare the reference genes' data.
reference_distances <- distances[gene %chin% reference_gene_ids]
#' Perform the correlation for one gene.
compute <- function(gene_id) {
gene_distances <- distances[gene_id]
gene_species_count <- nrow(gene_distances)
# Return a score of 0.0 if there is just one or no value at all.
if (gene_species_count <= 1) {
return(0.0)
}
#' Buffer for the sum of correlation coefficients.
correlation_sum <- 0
# Correlate with all reference genes but not with the gene itself.
for (reference_gene_id in
reference_gene_ids[reference_gene_ids != gene_id]) {
data <- merge(
gene_distances,
reference_distances[reference_gene_id],
by = "species"
)
# Skip this reference gene, if there are not enough value pairs.
# This will lessen the final score, because it effectively
# represents a correlation coefficient of 0.0.
if (nrow(data) <= 1) {
next
}
# Order data by the reference gene's distance to get a monotonic
# relation.
setorder(data, distance.y)
correlation_sum <- correlation_sum + abs(cor(
data[, distance.x], data[, distance.y],
method = "spearman"
))
}
# Compute the score as the mean correlation coefficient.
score <- correlation_sum / reference_count
}
results[, score := compute(gene), by = 1:nrow(results)]
}

254
process/input.R
View file

@ -1,254 +0,0 @@
library(biomaRt)
library(data.table)
library(progress)
library(rlog)
library(stringr)
#' Species IDs of known replicatively aging species.
species_ids_replicative <- c(
"bihybrid",
"btaurus",
"bthybrid",
"cfamiliaris",
"chircus",
"cjacchus",
"clfamiliaris",
"csabaeus",
"ecaballus",
"fcatus",
"ggorilla",
"hsapiens",
"lafricana",
"mfascicularis",
"mmulatta",
"mmurinus",
"mnemestrina",
"nleucogenys",
"oaries",
"pabelii",
"panubis",
"ppaniscus",
"ptroglodytes",
"sscrofa",
"tgelada"
)
#' Gene names of genes for verified TPE-OLD genes.
genes_verified_tpe_old <- c(
"C1S",
"DSP",
"ISG15",
"SORBS2",
"TERT"
)
#' Gene names of genes with a suggested TPE-OLD.
genes_suggested_tpe_old <- c(
"AKAP3",
"ANO2",
"CCND2",
"CD163L1",
"CD9",
"FOXM1",
"GALNT8",
"NDUFA9",
"TEAD4",
"TIGAR",
"TSPAN9"
)
#' Shared accessor for the Ensembl API.
ensembl <- NULL
#' Get the ensembl accessor and initialize it if necessary.
get_ensembl <- function() {
if (is.null(ensembl)) {
ensembl <<- useEnsembl("ensembl", version = 104)
}
ensembl
}
#' Get all chromosome names for a Ensembl dataset.
#'
#' Valid chromosome names include decimal numbers as well as 'X' and 'Y'.
get_chromosome_names <- function(dataset) {
chromosome_names <- listFilterOptions(dataset, "chromosome_name")
chromosome_names[str_which(chromosome_names, "^[0-9]+|[XY]$")]
}
#' Retrieve information on species.
#'
#' The result will be a `data.table` with the following columns:
#'
#' - `id` Species ID as presented by Ensembl.
#' - `name` Human readable species name.
#' - `replicative` Whether the species is likely to be aging replicatively.
retrieve_species <- function() {
# Ensembl datasets correspond to distinct species.
ensembl_datasets <- data.table(listDatasets(get_ensembl()))
# Filter out species ID and name from the result.
species <- ensembl_datasets[, .(
id = str_match(dataset, "(.*)_gene_ensembl")[, 2],
name = str_match(description, "(.*) genes \\(.*\\)")[, 2]
)]
species[, replicative := id %chin% species_ids_replicative]
}
#' Retrieve information on human genes.
#'
#' This will only include genes on assembled chromosomes. Chromosomes are
#' filtered based on their name being either a decimal number, 'X' or 'Y'.
#'
#' The result will be a `data.table` with the following columns:
#'
#' - `id` Ensembl gene ID.
#' - `ǹame` HGNC name of the gene.
#' - `chromosome` Human chromosome on which the gene is located.
retrieve_genes <- function() {
dataset <- useDataset("hsapiens_gene_ensembl", mart = get_ensembl())
genes <- data.table(getBM(
attributes = c("ensembl_gene_id", "hgnc_symbol", "chromosome_name"),
filters = "chromosome_name",
values = get_chromosome_names(dataset),
mart = useDataset("hsapiens_gene_ensembl", mart = get_ensembl())
))
genes[, .(
id = ensembl_gene_id,
name = hgnc_symbol,
chromosome = chromosome_name,
verified = hgnc_symbol %chin% genes_verified_tpe_old,
suggested = hgnc_symbol %chin% genes_suggested_tpe_old
)]
}
#' Retrieve gene distance data.
#'
#' The data will include all available values for the given species and genes
#' that are located on assembled chromosomes.
#'
#' The result will be a `data.table` with the following columns:
#'
#' - `species` Species ID.
#' - `gene` Ensembl gene ID.
#' - `distance` Distance to nearest telomere in base pairs.
retrieve_distances <- function(species_ids, gene_ids) {
ensembl <- get_ensembl()
# Exclude the human from the species, in case it is present there.
species_ids <- species_ids[species_ids != "hsapiens"]
species_count <- length(species_ids)
gene_count <- length(gene_ids)
log_info(sprintf(
"Retrieving distance data for %i genes from %i species",
gene_count,
species_count
))
progress <- progress_bar$new(
total = gene_count,
format = "Retrieving distance data [:bar] :percent (ETA :eta)"
)
# Special case the human species and retrieve all available distance
# information.
dataset <- useDataset("hsapiens_gene_ensembl", mart = ensembl)
human_distances <- data.table(getBM(
attributes = c(
"ensembl_gene_id",
"chromosome_name",
"start_position",
"end_position"
),
filters = "chromosome_name",
values = get_chromosome_names(dataset),
mart = dataset
))
# Compute the nearest distance to telomeres.
human_distances[,
chromosome_length := max(end_position),
by = chromosome_name
]
distances <- human_distances[, .(
species = "hsapiens",
gene = ensembl_gene_id,
distance = pmin(
start_position,
chromosome_length - end_position
)
)]
for (i in 1:species_count) {
species_id <- species_ids[i]
progress$tick()
dataset <- useDataset(
sprintf("%s_gene_ensembl", species_id),
mart = ensembl
)
# Besides the attributes that are always present, we need to check for
# human orthologs. Some species don't have that information and will be
# skipped.
if (!"hsapiens_homolog_ensembl_gene" %chin%
listAttributes(dataset, what = "name")) {
next
}
chromosome_names <- get_chromosome_names(dataset)
# Skip the species, if there are no assembled chromosomes.
if (length(chromosome_names) <= 0) {
next
}
# Retrieve information on all genes of the current species, that have
# human orthologs. This is called "homolog" in the Ensembl schema.
ensembl_distances <- data.table(getBM(
attributes = c(
"hsapiens_homolog_ensembl_gene",
"chromosome_name",
"start_position",
"end_position"
),
filters = c("with_hsapiens_homolog", "chromosome_name"),
values = list(TRUE, chromosome_names),
mart = dataset
))
# Precompute the genes' distance to the nearest telomere.
ensembl_distances[,
chromosome_length := max(end_position),
by = chromosome_name
]
species_distances <- ensembl_distances[, .(
species = species_id,
gene = hsapiens_homolog_ensembl_gene,
distance = pmin(
start_position,
chromosome_length - end_position
)
)]
distances <- rbindlist(list(distances, species_distances))
}
# Arbitrarily exclude duplicated genes.
# TODO: Consider a refined approach or work out how to include all
# duplicates.
unique(distances, by = c("species", "gene"))
}

62
process/methods.R
View file

@ -1,62 +0,0 @@
source("process/clusteriness.R")
source("process/correlation.R")
source("process/neural.R")
source("process/proximity.R")
#' Construct a new method.
#'
#' A method describes a way to perform a computation on gene distance data that
#' results in a single score per gene. The function should accept the following
#' parameters in this order:
#'
#' - `distances` Distance data to use.
#' - `gene_ids` Genes to process.
#' - `preset` Preset to apply.
#'
#' The function should return a `data.table` with the following columns:
#'
#' - `gene` Gene ID of the processed gene.
#' - `score` Score for the gene between 0.0 and 1.0.
#'
#' @param id Internal identifier for the method.
#' @param name Human readable name for the method.
#' @param description Short human readable description.
#' @param fn Function to perform the computation.
#'
#' @return A named list containing the arguments.
method <- function(id, name, description, fn) {
list(
id = id,
name = name,
description = description,
fn = fn
)
}
#' All methods to be included in the analysis.
methods <- list(
method(
"clusteriness",
"Clustering",
"Clustering of genes",
process_clusteriness
),
method(
"correlation",
"Correlation",
"Correlation with known genes",
process_correlation
),
method(
"proximity",
"Proximity",
"Proximity to telomeres",
process_proximity
),
method(
"neural",
"Neural",
"Assessment by neural network",
process_neural
)
)

101
process/neural.R
View file

@ -1,101 +0,0 @@
library(data.table)
library(neuralnet)
#' Find genes by training a neural network on reference position data.
#' @param seed A seed to get reproducible results.
process_neural <- function(distances, gene_ids, preset, seed = 726839) {
species_ids <- preset$species_ids
reference_gene_ids <- preset$reference_gene_ids
set.seed(seed)
gene_count <- length(gene_ids)
# Prefilter distances by species.
distances <- 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)
#' 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
for (species_id in species_ids) {
# Make a column specific to this species.
species_distances <- distances[species == species_id, .(gene, distance)]
setnames(species_distances, "distance", species_id)
# Only include species with at least 25% known values.
species_distances <- na.omit(species_distances)
if (nrow(species_distances) >= 0.25 * gene_count) {
species_ids_included <- append(species_ids_included, species_id)
data <- merge(data, species_distances, all = 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.
for (species_id in species_ids_included) {
mean_value <- data[, mean(get(species_id), na.rm = TRUE)]
data[
is.na(get(species_id)),
eval(quote(species_id)) := round(mean_value)
]
}
# 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)
)
]
reference_samples[, neural := 0.0]
# 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))
# Construct and train the neural network.
nn_formula <- as.formula(sprintf(
"neural~%s",
paste(species_ids_included, collapse = "+")
))
layer1 <- length(species_ids_included) * 0.66
layer2 <- layer1 * 0.66
layer3 <- layer2 * 0.66
nn <- neuralnet(
nn_formula,
training_data,
hidden = c(layer1, layer2, layer3),
linear.output = FALSE
)
# Return the resulting scores given by applying the neural network.
data[, score := compute(nn, data)$net.result]
data[, .(gene, score)]
}

29
process/presets.R
View file

@ -1,29 +0,0 @@
library(data.table)
#' Create a new preset.
#'
#' A preset is a combination of input values to all processing methods. The
#' preset's hash will be used to cache the results of applying those.
#'
#' @param species_ids IDs of species to include.
#' @param reference_gene_ids Reference genes to use.
#'
#' @return A named list containing the arguments.
preset <- function(species_ids, reference_gene_ids) {
list(
species_ids = species_ids,
reference_gene_ids = reference_gene_ids
)
}
#' A default preset including only replicatively aging species.
preset_replicative_species <- preset(
species[replicative == TRUE, id],
genes[suggested | verified == TRUE, id]
)
#' A default preset including all species.
preset_all_species <- preset(
species[, id],
genes[suggested | verified == TRUE, id]
)

58
process/process.R
View file

@ -1,58 +0,0 @@
library(data.table)
source("process/util.R")
# Load input data
source("process/input.R")
species <- run_cached("inputs/species", retrieve_species)
genes <- run_cached("inputs/genes", retrieve_genes)
distances <- run_cached(
"inputs/distances",
retrieve_distances,
species[, id],
genes[, id]
)
genes <- merge(
genes,
distances[, .(n_species = .N), by = "gene"],
by.x = "id",
by.y = "gene"
)
source("process/methods.R")
source("process/presets.R")
#' Apply all methods with the specified preset without caching.
process_priv <- function(preset) {
results <- data.table(gene = genes[, id])
for (method in methods) {
method_results <- method$fn(distances, genes[, id], preset)
setnames(method_results, "score", method$id)
results <- merge(
results,
method_results
)
}
results
}
#' Apply all methods with the specified preset.
#'
#' The result will be cached by the preset's hash and restored from cache, if
#' possible. The return value is a `data.table` with one row for each gene
#' identified by it's ID (`gene` column). The additional columns contain the
#' resulting per method and are named after the method IDs.
process <- function(preset) {
run_cached(
sprintf("results/%s", rlang::hash(preset)),
process_priv,
preset
)
}

20
process/proximity.R
View file

@ -1,20 +0,0 @@
library(data.table)
#' Score the mean distance of genes to the telomeres across species.
#'
#' 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.
process_proximity <- function(distances, gene_ids, preset) {
species_count <- length(preset$species_ids)
# Prefilter distances by species.
distances <- distances[species %chin% preset$species_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]
distances[, .(gene, score)]
}

63
shiny/server.R → server.R
View file

@ -1,13 +1,14 @@
library(data.table) library(data.table)
library(DT) library(DT)
library(geposan)
library(gprofiler2) library(gprofiler2)
library(plotly) library(plotly)
library(rclipboard) library(rclipboard)
library(shiny) library(shiny)
source("optimize.R")
source("rank_plot.R") source("rank_plot.R")
source("scatter_plot.R") source("scatter_plot.R")
source("utils.R")
#' Java script function to replace gene IDs with Ensembl gene links. #' Java script function to replace gene IDs with Ensembl gene links.
js_link <- JS("function(row, data) { js_link <- JS("function(row, data) {
@ -45,16 +46,19 @@ server <- function(input, output, session) {
} }
} }
reference_gene_ids <- genes[suggested | verified == TRUE, id] weights <- geposan::optimize_weights(
weights <- optimize_weights(results, method_ids, reference_gene_ids) results,
method_ids,
genes_tpe_old
)
mapply(function(method_id, weight) { for (method_id in method_ids) {
updateSliderInput( updateSliderInput(
session, session,
sprintf("%s_weight", method_id), sprintf("%s_weight", method_id),
value = weight * 100 value = weights[[method_id]] * 100
) )
}, method_ids, weights) }
}) })
# Observe each method's enable button. # Observe each method's enable button.
@ -67,14 +71,17 @@ server <- function(input, output, session) {
#' Rank the results based on the specified weights. Filter out genes with #' Rank the results based on the specified weights. Filter out genes with
#' too few species but don't apply the cut-off score. #' too few species but don't apply the cut-off score.
results <- reactive({ results <- reactive({
# Select the species preset. # Select the preset.
preset <- if (input$species == "all") {
results <- if (input$species == "all") { preset_all_species
process(preset_all_species)
} else { } else {
process(preset_replicative_species) preset_replicative_species
} }
# Perform the analysis cached based on the preset's hash.
results <- run_cached(rlang::hash(preset), geposan::analyze, preset)
# Add all gene information to the results.
results <- merge( results <- merge(
results, results,
genes, genes,
@ -82,41 +89,21 @@ server <- function(input, output, session) {
by.y = "id" by.y = "id"
) )
# Compute scoring factors and the weighted score. # Exclude genes with too few species.
results <- results[n_species >= input$n_species]
total_weight <- 0.0 # Rank the results based on the weights.
results[, score := 0.0]
weights <- NULL
for (method in methods) { for (method in methods) {
if (input[[method$id]]) { if (input[[method$id]]) {
weight <- input[[sprintf("%s_weight", method$id)]] weight <- input[[sprintf("%s_weight", method$id)]]
total_weight <- total_weight + weight weights[[method$id]] <- weight
column <- method$id
weighted <- weight * results[, ..column]
results[, score := score + weighted]
} }
} }
results[, score := score / total_weight] geposan::ranking(results, weights)
# Exclude genes with too few species.
results <- results[n_species >= input$n_species]
# Penalize missing species.
if (input$penalize) {
species_count <- if (input$species == "all") {
nrow(species)
} else {
length(species_ids_replicative)
}
results <- results[, score := score * n_species / species_count]
}
# Order the results based on their score.
setorder(results, -score, na.last = TRUE)
results[, rank := .I]
}) })
#' Apply the cut-off score to the ranked results. #' Apply the cut-off score to the ranked results.

7
shiny/main.R
View file

@ -1,7 +0,0 @@
library(shiny)
source("process/process.R")
source("shiny/server.R")
source("shiny/ui.R")
runApp(shinyApp(ui, server))

6
shiny/ui.R → ui.R
View file

@ -56,11 +56,7 @@ ui <- fluidPage(
value = 100 value = 100
) )
) )
}), })
checkboxInput(
"penalize",
"Penalize missing values"
),
), ),
mainPanel( mainPanel(
tabsetPanel( tabsetPanel(

0
process/util.R → utils.R
View file