R/deprecatedFunctions.R
In TheJacksonLaboratory/sRACIPE_dev: Systems biology tool to simulate gene regulatory circuits
Defines functions
load_data
plot_data_stochastic
plot_data_knockout_single
plot_data_knockout_all
plot_network
density_plot
plot_data_deterministic
Documented in
density_plot
load_data
plot_data_deterministic
plot_data_knockout_all
plot_data_knockout_single
plot_data_stochastic
plot_network
# This file contains deprecated functions which will be removed in a future
# version
#' @export
#' @title Load Data (Deprecated).
#' @description Use \code{\link{simulateGRC}}
#'
#' \code{\link{simulateGRC}}, \code{\link{knockdownAnalysis}},
#' \code{\link{overExprAnalysis}}, \code{\link{plotData}},
#' \code{\link{calcHeatmapSimilarity}}
#'
load_data = function(data_simulation = data_simulation,
topology_df = topology) {
working_directory <- getwd()
name_genes <-
read.table(
paste(
working_directory,
"/results/gene_interaction_topology_",
topology$filename,
".txt",
sep = ""
),
header = TRUE,
stringsAsFactors = FALSE
)
name_genes <- t(as.matrix(name_genes))
data_simulation <- log2(data_simulation)
data_simulation <- scale(data_simulation)
name_models <- seq(1:nrow(data_simulation))
row.names(data_simulation) <- name_models
colnames(data_simulation) <- name_genes
return(data_simulation)
}
#' @export
#' @title Plot Stochastic Data (Deprecated)
#' @description Use \code{\link{plotData}} instead.
#'
#' \code{\link{simulateGRC}}, \code{\link{knockdownAnalysis}},
#' \code{\link{overExprAnalysis}}, \code{\link{plotData}},
#' \code{\link{calcHeatmapSimilarity}}
plot_data_stochastic = function(output_file,
plot_filename = filename,
topology_df = topology,
config = configuration,
bin_count = 40) {
message("Plotting the stochastic results")
rf <- colorRampPalette(rev(brewer.pal(11, 'Spectral')))
plot_color <- rf(32)
if (missing(bin_count))
bin_count <- 40
if (missing(plot_filename))
output_filename <- "plots"
working_directory <- getwd()
name_genes <-
read.table(
paste(
working_directory,
"/results/gene_interaction_topology_",
topology$filename,
".txt",
sep = ""
),
header = T,
stringsAsFactors = F
)
data_simulation_all <-
as.data.frame(read.table(output_file, header = F))
col_start <- topology$number_gene * (configuration$NOISE_LEVELS - 1) +
1
col_end <- topology$number_gene * (configuration$NOISE_LEVELS)
data_simulation <-
as.data.frame(data_simulation_all[, col_start:col_end])
data_simulation <- log2(data_simulation)
data_simulation <-
data_simulation[is.finite(rowMeans(data_simulation)),]
means <- colMeans(data_simulation)
sds <- apply(data_simulation, 2, sd)
data_simulation <- sweep(data_simulation, 2, means, FUN = "-")
data_simulation <- sweep(data_simulation, 2, sds, FUN = "/")
name_models <- seq(1:nrow(data_simulation))
name_genes <- t(as.matrix(name_genes))
row.names(data_simulation) <- name_models
colnames(data_simulation) <- name_genes
pca = prcomp(data_simulation, scale. = FALSE, center = FALSE)
#pca_data <- data.frame(x=pca$x[,1],y=pca$x[,2])
pdf(paste("results/", plot_filename, "_stochastic_pca.pdf", sep = ""))
for (i in 1:configuration$NOISE_LEVELS)
{
col_start <- (topology$number_gene) * (configuration$NOISE_LEVELS - i) +
1
col_end <-
(topology$number_gene) * (configuration$NOISE_LEVELS - i + 1)
data_simulation <-
as.data.frame(data_simulation_all[, col_start:col_end])
data_simulation <- log2(data_simulation)
data_simulation <-
data_simulation[is.finite(rowMeans(data_simulation)),]
data_simulation <- sweep(data_simulation, 2, means, FUN = "-")
data_simulation <- sweep(data_simulation, 2, sds, FUN = "/")
name_models <- seq(1:nrow(data_simulation))
row.names(data_simulation) <- name_models
colnames(data_simulation) <- name_genes
pca_data <-
scale(data_simulation, pca$center, pca$scale) %*% pca$rotation
pca_data <- data.frame(x = pca_data[, 1], y = pca_data[, 2])
density_plot(pca_data, bin_count, plot_color)
}
dev.off()
message("Plots in the pdf file in the results folder.")
}
#' @export
#' @title Plot knockout Data (Deprecated)
#' @description Use \code{\link{plotData}} instead.
#'
#' \code{\link{simulateGRC}}, \code{\link{knockdownAnalysis}},
#' \code{\link{overExprAnalysis}}, \code{\link{plotData}},
#' \code{\link{calcHeatmapSimilarity}}
#'
plot_data_knockout_single = function(output_file,
output_file_knockout,
plot_filename = filename,
topology_df = topology,
KNOCKOUT = NA_character_,
config = configuration,
bin_count = 40) {
message("Plotting the single knockout results")
rf <- colorRampPalette(rev(brewer.pal(11, 'Spectral')))
plot_color <- rf(32)
if (missing(bin_count))
bin_count <- 40
if (missing(plot_filename))
output_filename <- "plots"
working_directory <- getwd()
name_genes <-
read.table(
paste(
working_directory,
"/results/gene_interaction_topology_",
topology$filename,
".txt",
sep = ""
),
header = T,
stringsAsFactors = F
)
name_genes <- t(as.matrix(name_genes))
data_simulation <- read.table(output_file, header = F)
name_models <- seq(1:nrow(data_simulation))
row.names(data_simulation) <- name_models
colnames(data_simulation) <- name_genes
knockout_number <- as.integer(which(name_genes == KNOCKOUT))
data_simulation <- log2(data_simulation)
data_simulation <- data_simulation[, -knockout_number]
col.means <- colMeans(data_simulation)
col.sds <- apply(data_simulation, 2, sd)
data_simulation <- sweep(data_simulation, 2, col.means, FUN = "-")
data_simulation <- sweep(data_simulation, 2, col.sds, FUN = "/")
data_simulation_knockout <-
read.table(output_file_knockout, header = F)
name_models <- seq(1:nrow(data_simulation_knockout))
row.names(data_simulation_knockout) <- name_models
colnames(data_simulation_knockout) <- name_genes
data_simulation_knockout <-
data_simulation_knockout[, -knockout_number]
data_simulation_knockout <- log2(data_simulation_knockout)
data_simulation_knockout <-
sweep(data_simulation_knockout, 2, col.means, FUN = "-")
data_simulation_knockout <-
sweep(data_simulation_knockout, 2, col.sds, FUN = "/")
pca = prcomp(data_simulation, scale. = FALSE, center = FALSE)
pca_data <- data.frame(x = pca$x[, 1], y = pca$x[, 2])
pdf(paste("results/", plot_filename, KNOCKOUT, "_knockout.pdf", sep = ""))
density_plot(pca_data, bin_count, plot_color)
pca_data_knockout <-
scale(data_simulation_knockout, pca$center, pca$scale) %*% pca$rotation
pca_data_knockout <-
data.frame(x = pca_data_knockout[, 1], y = pca_data_knockout[, 2])
density_plot(pca_data_knockout, bin_count, plot_color)
#col_count <- as.integer(65536/dim(data_simulation)[2])
#heatmap_data <- t(data_simulation[1:col_count,])
#dist <- as.dist((1-cor(t(heatmap_data), method = "spear"))/2)
#cluster <- hclust(dist,method = 'ward.D2')
#dendrogram <- as.dendrogram(cluster)
full <-
heatmap.2(
t(data_simulation),
col = plot_color,
hclustfun = function(x)
hclust(x, method = 'ward.D2'),
distfun = function(x)
as.dist((1 - cor(t(
x
), method = "spear")) / 2),
trace = "none"
)
heatmap.2(
t(data_simulation_knockout[, rev(full$rowInd)]),
Rowv = NA,
dendrogram = "column",
breaks = full$breaks,
col = plot_color,
hclustfun = function(x)
hclust(x, method = 'ward.D2'),
distfun = function(x)
as.dist((1 - cor(t(
x
), method = "spear")) / 2),
trace = "none"
)
# heatmap.2(t(data_simulation_knockout), col=plot_color, hclustfun = function(x) hclust(x,method = 'ward.D2'), distfun=function(x) as.dist((1-cor(t(x), method = "spear"))/2), trace = "none")
dev.off()
message("Plots in the pdf file in the results folder.")
}
#' @export
#' @title Plot knockout data for all genes (Deprecated)
#' @description Use \code{\link{plotData}} instead.
#'
#' \code{\link{simulateGRC}}, \code{\link{knockdownAnalysis}},
#' \code{\link{overExprAnalysis}}, \code{\link{plotData}},
#' \code{\link{calcHeatmapSimilarity}}
plot_data_knockout_all = function(output_file,
output_file_KOpre,
output_file_KOsuf,
tfs_knockout,
plot_filename = filename,
topology_df = topology,
config = configuration,
bin_count = 40) {
message("Plotting the single knockout results")
rf <- colorRampPalette(rev(brewer.pal(11, 'Spectral')))
plot_color <- rf(32)
if (missing(bin_count))
bin_count <- 40
if (missing(plot_filename))
output_filename <- "plots"
working_directory <- getwd()
name_genes <-
read.table(
paste(
working_directory,
"/results/gene_interaction_topology_",
topology$filename,
".txt",
sep = ""
),
header = T,
stringsAsFactors = F
)
name_genes <- t(as.matrix(name_genes))
data_simulation <- read.table(output_file, header = F)
name_models <- seq(1:nrow(data_simulation))
row.names(data_simulation) <- name_models
colnames(data_simulation) <- name_genes
data_simulation <- log2(data_simulation)
data_simulation1 <- data_simulation
pdf(paste("results/", plot_filename, "_knockout.pdf", sep = ""))
for (tf.current in 1:length(tfs_knockout)) {
KNOCKOUT <- tfs_knockout[tf.current]
knockout_number <- as.integer(which(name_genes == KNOCKOUT))
output_file_knockout <-
paste0(output_file_KOpre, KNOCKOUT, output_file_KOsuf)
data_simulation <- data_simulation1[, -knockout_number]
col.means <- colMeans(data_simulation)
col.sds <- apply(data_simulation, 2, sd)
data_simulation <- sweep(data_simulation, 2, col.means, FUN = "-")
data_simulation <- sweep(data_simulation, 2, col.sds, FUN = "/")
data_simulation_knockout <-
read.table(output_file_knockout, header = F)
name_models <- seq(1:nrow(data_simulation_knockout))
row.names(data_simulation_knockout) <- name_models
colnames(data_simulation_knockout) <- name_genes
data_simulation_knockout <-
data_simulation_knockout[, -knockout_number]
data_simulation_knockout <- log2(data_simulation_knockout)
data_simulation_knockout <-
sweep(data_simulation_knockout, 2, col.means,
FUN = "-")
data_simulation_knockout <-
sweep(data_simulation_knockout, 2, col.sds,
FUN = "/")
pca = prcomp(data_simulation, scale. = FALSE, center = FALSE)
pca_data <- data.frame(x = pca$x[, 1], y = pca$x[, 2])
par(mfrow = c(2, 1))
#par(mfrow=c(1,2), pty="s")
#layout(matrix(c(1,2,3,4), 2, 2, byrow = TRUE))
h1 <- hist(pca_data$x, breaks = bin_count, plot = F)
h2 <- hist(pca_data$y, breaks = bin_count, plot = F)
k <- kde2d(pca_data$x, pca_data$y, n = bin_count)
image(k, col = plot_color)
title(main = paste(KNOCKOUT, "_WT", sep = ""))
#density_plot(pca_data,bin_count,plot_color)
pca_data_knockout <- scale(data_simulation_knockout, pca$center,
pca$scale) %*% pca$rotation
pca_data_knockout <- data.frame(x = pca_data_knockout[, 1],
y = pca_data_knockout[, 2])
h1 <- hist(pca_data_knockout$x, breaks = bin_count, plot = F)
h2 <- hist(pca_data_knockout$y, breaks = bin_count, plot = F)
k <- MASS::kde2d(pca_data_knockout$x, pca_data_knockout$y, n = bin_count)
image(k, col = plot_color)
title(main = paste(KNOCKOUT, "_KO", sep = ""))
#density_plot(pca_data_knockout,bin_count,plot_color)
#col_count <- as.integer(65536/dim(data_simulation)[2])
#heatmap_data <- t(data_simulation[1:col_count,])
#dist <- as.dist((1-cor(t(heatmap_data), method = "spear"))/2)
#cluster <- hclust(dist,method = 'ward.D2')
#dendrogram <- as.dendrogram(cluster)
full <- heatmap.2(
t(data_simulation),
col = plot_color,
hclustfun = function(x)
hclust(x, method = 'ward.D2'),
distfun = function(x)
as.dist((1 - cor(t(
x
),
method = "spear")) /
2),
trace = "none"
)
title(main = paste(KNOCKOUT, "_WT", sep = ""))
heatmap.2(
t(data_simulation_knockout[, rev(full$rowInd)]),
Rowv = NA,
dendrogram = "column",
breaks = full$breaks,
col = plot_color,
hclustfun = function(x)
hclust(x, method = 'ward.D2'),
distfun = function(x)
as.dist((1 - cor(t(
x
), method = "spear")) / 2),
trace = "none"
)
title(main = paste(KNOCKOUT, "_KO", sep = ""))
# heatmap.2(t(data_simulation_knockout), col=plot_color, hclustfun = function(x) hclust(x,method = 'ward.D2'), distfun=function(x) as.dist((1-cor(t(x), method = "spear"))/2), trace = "none")
}
dev.off()
message("Plots in the pdf file in the results folder.")
}
#' @export
#' @title Plot the network (Deprecated)
#' @description Use \code{\link{plotCircuit}}.
#'
#' \code{\link{simulateGRC}}, \code{\link{knockdownAnalysis}},
#' \code{\link{overExprAnalysis}}, \code{\link{plotData}},
#' \code{\link{calcHeatmapSimilarity}}
plot_network = function(topology = topology, plotToFile = TRUE) {
if(plotToFile){
if (!dir.exists(file.path(getwd(), "results")))
dir.create(file.path(getwd(), "results"))
net_file <- paste(getwd(),
"/results/network_",
topology$filename,
".html",
sep = "")
# setwd(net_file)
}
node_list <-
unique(c(topology$topology[, 1], topology$topology[, 2]))
nodes <-
data.frame(
id = node_list,
label = node_list,
font.size = 50,
value = c(rep(1, length(node_list)))
)
edge_col <- data.frame(c(1, 2), c("blue", "darkred"))
arrow_type <- data.frame(c(1, 2), c("arrow", "circle"))
colnames(arrow_type) <- c("type", "color")
colnames(edge_col) <- c("type", "color")
edges <-
data.frame(
from = c(topology$topology[, 1]),
to = c(topology$topology[, 2])
# , arrows = c(c(topology$topology$Target), c(topology$topology$Target))
#, arrows = "to"
,
arrows.to.type = arrow_type$color[c(as.numeric(topology$topology[, 3]))]
,
width = 3
,
color = edge_col$color[c(as.numeric(topology$topology[, 3]))]
)
#visNetwork(nodes, edges, height = "500px", width = "100%") %>%
#visEdges(arrows = "to") %>%
#visOptions(manipulation = TRUE) %>%
#visLayout(randomSeed = 123) %>%
#visPhysics(solver = "forceAtlas2Based")
network <-
visNetwork::visNetwork(nodes, edges, height = "1000px", width = "100%") %>%
#visEdges(arrows = "to") %>%
visOptions(manipulation = TRUE) %>%
visLayout(randomSeed = 123) %>%
#visNodes(scaling = list(label = list(enabled = T))) %>%
visPhysics(solver = "forceAtlas2Based", stabilization = FALSE)
if(plotToFile){
visNetwork::visSave(network, file = net_file, selfcontained = F)
} else {network}
#visNetwork(gephi = "test")
}
#' @export
#' @title Plot the density
#' @description Use \code{\link{plotDensity}}.
density_plot <- function(plot_data, bin_count=40, plot_color=NULL){
densityPlot(plot_data, bin_count, plot_color)
}
#' @export
#' @title Plot the simulated data
#' @description Use \code{\link{plotData}}.
#'
#' \code{\link{simulateGRC}}, \code{\link{knockdownAnalysis}},
#' \code{\link{overExprAnalysis}}, \code{\link{plotData}},
#' \code{\link{calcHeatmapSimilarity}}
plot_data_deterministic = function(data_file = output_file,
plot_filename = plot_filename,
topology = topology,
bin_count = 40,
plotTsne = FALSE) {
working_directory <- getwd()
message("Plotting the results")
data_simulation <- read.table(output_file, header = FALSE)
data_simulation <- load_data(data_simulation, topology)
if (missing(bin_count))
bin_count <- 40
if (missing(plot_filename))
plot_filename <- "plots"
library(reshape2)
library(ggplot2)
library(gplots)
library(MASS)
library(RColorBrewer)
#library(htmlwidgets)
#library(d3heatmap)
rf <- colorRampPalette(rev(brewer.pal(11, 'Spectral')))
plot_color <- rf(32)
pca1 = prcomp(data_simulation, scale. = FALSE)
pca_data <- data.frame(x = pca1$x[, 1], y = pca1$x[, 2])
pdf(paste("results/", plot_filename, "_deterministic.pdf", sep = ""))
density_plot(pca_data, bin_count, plot_color)
#heatmap(t(data_simulation), col=plot_color, hclustfun = function(x) hclust(x,method = 'complete'))
heatmap.2(
t(data_simulation),
col = plot_color,
hclustfun = function(x)
hclust(x, method = 'ward.D2'),
distfun = function(x)
as.dist((1 - cor(t(
x
), method = "spear")) / 2),
trace = "none"
)
if (plotTsne) {
library(Rtsne)
tsne_out <-
Rtsne(as.matrix(data_simulation), check_duplicates = FALSE) # Run TSNE
tsne_data <- data.frame(x = tsne_out$Y[, 1], y = tsne_out$Y[, 2])
density_plot(tsne_data, bin_count, plot_color)
}
dev.off()
#results_directory <- ifelse(!dir.exists(file.path(working_directory, "results")), dir.create(file.path(working_directory, "results")), file.path(working_directory, "results"))
#setwd(results_directory)
#hmap_file <- paste(plot_filename,"_heatmap.html", sep = "")
#hmap <- d3heatmap(t(data_simulation), col=plot_color)
#saveWidget(hmap,hmap_file)
#setwd(working_directory)
# data <- list(data_simulation,pca1,tsne_out,pca_data,tsne_data)
message("Plots in the pdf file in the results folder.")
}
TheJacksonLaboratory/sRACIPE_dev documentation built on May 7, 2019, 8:16 a.m.
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Defines functions load_data plot_data_stochastic plot_data_knockout_single plot_data_knockout_all plot_network density_plot plot_data_deterministic
Documented in density_plot load_data plot_data_deterministic plot_data_knockout_all plot_data_knockout_single plot_data_stochastic plot_network
# This file contains deprecated functions which will be removed in a future
# version
#' @export
#' @title Load Data (Deprecated).
#' @description Use \code{\link{simulateGRC}}
#'
#' \code{\link{simulateGRC}}, \code{\link{knockdownAnalysis}},
#' \code{\link{overExprAnalysis}}, \code{\link{plotData}},
#' \code{\link{calcHeatmapSimilarity}}
#'
load_data = function(data_simulation = data_simulation,
topology_df = topology) {
working_directory <- getwd()
name_genes <-
read.table(
paste(
working_directory,
"/results/gene_interaction_topology_",
topology$filename,
".txt",
sep = ""
),
header = TRUE,
stringsAsFactors = FALSE
)
name_genes <- t(as.matrix(name_genes))
data_simulation <- log2(data_simulation)
data_simulation <- scale(data_simulation)
name_models <- seq(1:nrow(data_simulation))
row.names(data_simulation) <- name_models
colnames(data_simulation) <- name_genes
return(data_simulation)
}
#' @export
#' @title Plot Stochastic Data (Deprecated)
#' @description Use \code{\link{plotData}} instead.
#'
#' \code{\link{simulateGRC}}, \code{\link{knockdownAnalysis}},
#' \code{\link{overExprAnalysis}}, \code{\link{plotData}},
#' \code{\link{calcHeatmapSimilarity}}
plot_data_stochastic = function(output_file,
plot_filename = filename,
topology_df = topology,
config = configuration,
bin_count = 40) {
message("Plotting the stochastic results")
rf <- colorRampPalette(rev(brewer.pal(11, 'Spectral')))
plot_color <- rf(32)
if (missing(bin_count))
bin_count <- 40
if (missing(plot_filename))
output_filename <- "plots"
working_directory <- getwd()
name_genes <-
read.table(
paste(
working_directory,
"/results/gene_interaction_topology_",
topology$filename,
".txt",
sep = ""
),
header = T,
stringsAsFactors = F
)
data_simulation_all <-
as.data.frame(read.table(output_file, header = F))
col_start <- topology$number_gene * (configuration$NOISE_LEVELS - 1) +
1
col_end <- topology$number_gene * (configuration$NOISE_LEVELS)
data_simulation <-
as.data.frame(data_simulation_all[, col_start:col_end])
data_simulation <- log2(data_simulation)
data_simulation <-
data_simulation[is.finite(rowMeans(data_simulation)),]
means <- colMeans(data_simulation)
sds <- apply(data_simulation, 2, sd)
data_simulation <- sweep(data_simulation, 2, means, FUN = "-")
data_simulation <- sweep(data_simulation, 2, sds, FUN = "/")
name_models <- seq(1:nrow(data_simulation))
name_genes <- t(as.matrix(name_genes))
row.names(data_simulation) <- name_models
colnames(data_simulation) <- name_genes
pca = prcomp(data_simulation, scale. = FALSE, center = FALSE)
#pca_data <- data.frame(x=pca$x[,1],y=pca$x[,2])
pdf(paste("results/", plot_filename, "_stochastic_pca.pdf", sep = ""))
for (i in 1:configuration$NOISE_LEVELS)
{
col_start <- (topology$number_gene) * (configuration$NOISE_LEVELS - i) +
1
col_end <-
(topology$number_gene) * (configuration$NOISE_LEVELS - i + 1)
data_simulation <-
as.data.frame(data_simulation_all[, col_start:col_end])
data_simulation <- log2(data_simulation)
data_simulation <-
data_simulation[is.finite(rowMeans(data_simulation)),]
data_simulation <- sweep(data_simulation, 2, means, FUN = "-")
data_simulation <- sweep(data_simulation, 2, sds, FUN = "/")
name_models <- seq(1:nrow(data_simulation))
row.names(data_simulation) <- name_models
colnames(data_simulation) <- name_genes
pca_data <-
scale(data_simulation, pca$center, pca$scale) %*% pca$rotation
pca_data <- data.frame(x = pca_data[, 1], y = pca_data[, 2])
density_plot(pca_data, bin_count, plot_color)
}
dev.off()
message("Plots in the pdf file in the results folder.")
}
#' @export
#' @title Plot knockout Data (Deprecated)
#' @description Use \code{\link{plotData}} instead.
#'
#' \code{\link{simulateGRC}}, \code{\link{knockdownAnalysis}},
#' \code{\link{overExprAnalysis}}, \code{\link{plotData}},
#' \code{\link{calcHeatmapSimilarity}}
#'
plot_data_knockout_single = function(output_file,
output_file_knockout,
plot_filename = filename,
topology_df = topology,
KNOCKOUT = NA_character_,
config = configuration,
bin_count = 40) {
message("Plotting the single knockout results")
rf <- colorRampPalette(rev(brewer.pal(11, 'Spectral')))
plot_color <- rf(32)
if (missing(bin_count))
bin_count <- 40
if (missing(plot_filename))
output_filename <- "plots"
working_directory <- getwd()
name_genes <-
read.table(
paste(
working_directory,
"/results/gene_interaction_topology_",
topology$filename,
".txt",
sep = ""
),
header = T,
stringsAsFactors = F
)
name_genes <- t(as.matrix(name_genes))
data_simulation <- read.table(output_file, header = F)
name_models <- seq(1:nrow(data_simulation))
row.names(data_simulation) <- name_models
colnames(data_simulation) <- name_genes
knockout_number <- as.integer(which(name_genes == KNOCKOUT))
data_simulation <- log2(data_simulation)
data_simulation <- data_simulation[, -knockout_number]
col.means <- colMeans(data_simulation)
col.sds <- apply(data_simulation, 2, sd)
data_simulation <- sweep(data_simulation, 2, col.means, FUN = "-")
data_simulation <- sweep(data_simulation, 2, col.sds, FUN = "/")
data_simulation_knockout <-
read.table(output_file_knockout, header = F)
name_models <- seq(1:nrow(data_simulation_knockout))
row.names(data_simulation_knockout) <- name_models
colnames(data_simulation_knockout) <- name_genes
data_simulation_knockout <-
data_simulation_knockout[, -knockout_number]
data_simulation_knockout <- log2(data_simulation_knockout)
data_simulation_knockout <-
sweep(data_simulation_knockout, 2, col.means, FUN = "-")
data_simulation_knockout <-
sweep(data_simulation_knockout, 2, col.sds, FUN = "/")
pca = prcomp(data_simulation, scale. = FALSE, center = FALSE)
pca_data <- data.frame(x = pca$x[, 1], y = pca$x[, 2])
pdf(paste("results/", plot_filename, KNOCKOUT, "_knockout.pdf", sep = ""))
density_plot(pca_data, bin_count, plot_color)
pca_data_knockout <-
scale(data_simulation_knockout, pca$center, pca$scale) %*% pca$rotation
pca_data_knockout <-
data.frame(x = pca_data_knockout[, 1], y = pca_data_knockout[, 2])
density_plot(pca_data_knockout, bin_count, plot_color)
#col_count <- as.integer(65536/dim(data_simulation)[2])
#heatmap_data <- t(data_simulation[1:col_count,])
#dist <- as.dist((1-cor(t(heatmap_data), method = "spear"))/2)
#cluster <- hclust(dist,method = 'ward.D2')
#dendrogram <- as.dendrogram(cluster)
full <-
heatmap.2(
t(data_simulation),
col = plot_color,
hclustfun = function(x)
hclust(x, method = 'ward.D2'),
distfun = function(x)
as.dist((1 - cor(t(
x
), method = "spear")) / 2),
trace = "none"
)
heatmap.2(
t(data_simulation_knockout[, rev(full$rowInd)]),
Rowv = NA,
dendrogram = "column",
breaks = full$breaks,
col = plot_color,
hclustfun = function(x)
hclust(x, method = 'ward.D2'),
distfun = function(x)
as.dist((1 - cor(t(
x
), method = "spear")) / 2),
trace = "none"
)
# heatmap.2(t(data_simulation_knockout), col=plot_color, hclustfun = function(x) hclust(x,method = 'ward.D2'), distfun=function(x) as.dist((1-cor(t(x), method = "spear"))/2), trace = "none")
dev.off()
message("Plots in the pdf file in the results folder.")
}
#' @export
#' @title Plot knockout data for all genes (Deprecated)
#' @description Use \code{\link{plotData}} instead.
#'
#' \code{\link{simulateGRC}}, \code{\link{knockdownAnalysis}},
#' \code{\link{overExprAnalysis}}, \code{\link{plotData}},
#' \code{\link{calcHeatmapSimilarity}}
plot_data_knockout_all = function(output_file,
output_file_KOpre,
output_file_KOsuf,
tfs_knockout,
plot_filename = filename,
topology_df = topology,
config = configuration,
bin_count = 40) {
message("Plotting the single knockout results")
rf <- colorRampPalette(rev(brewer.pal(11, 'Spectral')))
plot_color <- rf(32)
if (missing(bin_count))
bin_count <- 40
if (missing(plot_filename))
output_filename <- "plots"
working_directory <- getwd()
name_genes <-
read.table(
paste(
working_directory,
"/results/gene_interaction_topology_",
topology$filename,
".txt",
sep = ""
),
header = T,
stringsAsFactors = F
)
name_genes <- t(as.matrix(name_genes))
data_simulation <- read.table(output_file, header = F)
name_models <- seq(1:nrow(data_simulation))
row.names(data_simulation) <- name_models
colnames(data_simulation) <- name_genes
data_simulation <- log2(data_simulation)
data_simulation1 <- data_simulation
pdf(paste("results/", plot_filename, "_knockout.pdf", sep = ""))
for (tf.current in 1:length(tfs_knockout)) {
KNOCKOUT <- tfs_knockout[tf.current]
knockout_number <- as.integer(which(name_genes == KNOCKOUT))
output_file_knockout <-
paste0(output_file_KOpre, KNOCKOUT, output_file_KOsuf)
data_simulation <- data_simulation1[, -knockout_number]
col.means <- colMeans(data_simulation)
col.sds <- apply(data_simulation, 2, sd)
data_simulation <- sweep(data_simulation, 2, col.means, FUN = "-")
data_simulation <- sweep(data_simulation, 2, col.sds, FUN = "/")
data_simulation_knockout <-
read.table(output_file_knockout, header = F)
name_models <- seq(1:nrow(data_simulation_knockout))
row.names(data_simulation_knockout) <- name_models
colnames(data_simulation_knockout) <- name_genes
data_simulation_knockout <-
data_simulation_knockout[, -knockout_number]
data_simulation_knockout <- log2(data_simulation_knockout)
data_simulation_knockout <-
sweep(data_simulation_knockout, 2, col.means,
FUN = "-")
data_simulation_knockout <-
sweep(data_simulation_knockout, 2, col.sds,
FUN = "/")
pca = prcomp(data_simulation, scale. = FALSE, center = FALSE)
pca_data <- data.frame(x = pca$x[, 1], y = pca$x[, 2])
par(mfrow = c(2, 1))
#par(mfrow=c(1,2), pty="s")
#layout(matrix(c(1,2,3,4), 2, 2, byrow = TRUE))
h1 <- hist(pca_data$x, breaks = bin_count, plot = F)
h2 <- hist(pca_data$y, breaks = bin_count, plot = F)
k <- kde2d(pca_data$x, pca_data$y, n = bin_count)
image(k, col = plot_color)
title(main = paste(KNOCKOUT, "_WT", sep = ""))
#density_plot(pca_data,bin_count,plot_color)
pca_data_knockout <- scale(data_simulation_knockout, pca$center,
pca$scale) %*% pca$rotation
pca_data_knockout <- data.frame(x = pca_data_knockout[, 1],
y = pca_data_knockout[, 2])
h1 <- hist(pca_data_knockout$x, breaks = bin_count, plot = F)
h2 <- hist(pca_data_knockout$y, breaks = bin_count, plot = F)
k <- MASS::kde2d(pca_data_knockout$x, pca_data_knockout$y, n = bin_count)
image(k, col = plot_color)
title(main = paste(KNOCKOUT, "_KO", sep = ""))
#density_plot(pca_data_knockout,bin_count,plot_color)
#col_count <- as.integer(65536/dim(data_simulation)[2])
#heatmap_data <- t(data_simulation[1:col_count,])
#dist <- as.dist((1-cor(t(heatmap_data), method = "spear"))/2)
#cluster <- hclust(dist,method = 'ward.D2')
#dendrogram <- as.dendrogram(cluster)
full <- heatmap.2(
t(data_simulation),
col = plot_color,
hclustfun = function(x)
hclust(x, method = 'ward.D2'),
distfun = function(x)
as.dist((1 - cor(t(
x
),
method = "spear")) /
2),
trace = "none"
)
title(main = paste(KNOCKOUT, "_WT", sep = ""))
heatmap.2(
t(data_simulation_knockout[, rev(full$rowInd)]),
Rowv = NA,
dendrogram = "column",
breaks = full$breaks,
col = plot_color,
hclustfun = function(x)
hclust(x, method = 'ward.D2'),
distfun = function(x)
as.dist((1 - cor(t(
x
), method = "spear")) / 2),
trace = "none"
)
title(main = paste(KNOCKOUT, "_KO", sep = ""))
# heatmap.2(t(data_simulation_knockout), col=plot_color, hclustfun = function(x) hclust(x,method = 'ward.D2'), distfun=function(x) as.dist((1-cor(t(x), method = "spear"))/2), trace = "none")
}
dev.off()
message("Plots in the pdf file in the results folder.")
}
#' @export
#' @title Plot the network (Deprecated)
#' @description Use \code{\link{plotCircuit}}.
#'
#' \code{\link{simulateGRC}}, \code{\link{knockdownAnalysis}},
#' \code{\link{overExprAnalysis}}, \code{\link{plotData}},
#' \code{\link{calcHeatmapSimilarity}}
plot_network = function(topology = topology, plotToFile = TRUE) {
if(plotToFile){
if (!dir.exists(file.path(getwd(), "results")))
dir.create(file.path(getwd(), "results"))
net_file <- paste(getwd(),
"/results/network_",
topology$filename,
".html",
sep = "")
# setwd(net_file)
}
node_list <-
unique(c(topology$topology[, 1], topology$topology[, 2]))
nodes <-
data.frame(
id = node_list,
label = node_list,
font.size = 50,
value = c(rep(1, length(node_list)))
)
edge_col <- data.frame(c(1, 2), c("blue", "darkred"))
arrow_type <- data.frame(c(1, 2), c("arrow", "circle"))
colnames(arrow_type) <- c("type", "color")
colnames(edge_col) <- c("type", "color")
edges <-
data.frame(
from = c(topology$topology[, 1]),
to = c(topology$topology[, 2])
# , arrows = c(c(topology$topology$Target), c(topology$topology$Target))
#, arrows = "to"
,
arrows.to.type = arrow_type$color[c(as.numeric(topology$topology[, 3]))]
,
width = 3
,
color = edge_col$color[c(as.numeric(topology$topology[, 3]))]
)
#visNetwork(nodes, edges, height = "500px", width = "100%") %>%
#visEdges(arrows = "to") %>%
#visOptions(manipulation = TRUE) %>%
#visLayout(randomSeed = 123) %>%
#visPhysics(solver = "forceAtlas2Based")
network <-
visNetwork::visNetwork(nodes, edges, height = "1000px", width = "100%") %>%
#visEdges(arrows = "to") %>%
visOptions(manipulation = TRUE) %>%
visLayout(randomSeed = 123) %>%
#visNodes(scaling = list(label = list(enabled = T))) %>%
visPhysics(solver = "forceAtlas2Based", stabilization = FALSE)
if(plotToFile){
visNetwork::visSave(network, file = net_file, selfcontained = F)
} else {network}
#visNetwork(gephi = "test")
}
#' @export
#' @title Plot the density
#' @description Use \code{\link{plotDensity}}.
density_plot <- function(plot_data, bin_count=40, plot_color=NULL){
densityPlot(plot_data, bin_count, plot_color)
}
#' @export
#' @title Plot the simulated data
#' @description Use \code{\link{plotData}}.
#'
#' \code{\link{simulateGRC}}, \code{\link{knockdownAnalysis}},
#' \code{\link{overExprAnalysis}}, \code{\link{plotData}},
#' \code{\link{calcHeatmapSimilarity}}
plot_data_deterministic = function(data_file = output_file,
plot_filename = plot_filename,
topology = topology,
bin_count = 40,
plotTsne = FALSE) {
working_directory <- getwd()
message("Plotting the results")
data_simulation <- read.table(output_file, header = FALSE)
data_simulation <- load_data(data_simulation, topology)
if (missing(bin_count))
bin_count <- 40
if (missing(plot_filename))
plot_filename <- "plots"
library(reshape2)
library(ggplot2)
library(gplots)
library(MASS)
library(RColorBrewer)
#library(htmlwidgets)
#library(d3heatmap)
rf <- colorRampPalette(rev(brewer.pal(11, 'Spectral')))
plot_color <- rf(32)
pca1 = prcomp(data_simulation, scale. = FALSE)
pca_data <- data.frame(x = pca1$x[, 1], y = pca1$x[, 2])
pdf(paste("results/", plot_filename, "_deterministic.pdf", sep = ""))
density_plot(pca_data, bin_count, plot_color)
#heatmap(t(data_simulation), col=plot_color, hclustfun = function(x) hclust(x,method = 'complete'))
heatmap.2(
t(data_simulation),
col = plot_color,
hclustfun = function(x)
hclust(x, method = 'ward.D2'),
distfun = function(x)
as.dist((1 - cor(t(
x
), method = "spear")) / 2),
trace = "none"
)
if (plotTsne) {
library(Rtsne)
tsne_out <-
Rtsne(as.matrix(data_simulation), check_duplicates = FALSE) # Run TSNE
tsne_data <- data.frame(x = tsne_out$Y[, 1], y = tsne_out$Y[, 2])
density_plot(tsne_data, bin_count, plot_color)
}
dev.off()
#results_directory <- ifelse(!dir.exists(file.path(working_directory, "results")), dir.create(file.path(working_directory, "results")), file.path(working_directory, "results"))
#setwd(results_directory)
#hmap_file <- paste(plot_filename,"_heatmap.html", sep = "")
#hmap <- d3heatmap(t(data_simulation), col=plot_color)
#saveWidget(hmap,hmap_file)
#setwd(working_directory)
# data <- list(data_simulation,pca1,tsne_out,pca_data,tsne_data)
message("Plots in the pdf file in the results folder.")
}
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