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plot.functions.R
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plot.functions.R
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create_joined <- function(dnds_path_eden = "",
dnds_path_alt = "",
gap_path = "",
only_legend = FALSE,
window_size = 10,
gap_threshold = 0.6,
gapcolor = TRUE,
points = TRUE) {
# process alt
require(zoo)
gap_data <- try(read.table(gap_path, header = F))
dnds_data <- try(read.table(dnds_path_alt, header = T, sep=','))
dN <- dnds_data$dN
dS <- dnds_data$dS
dNdS <- rep(0, length(dN))
dNdS[which(dS > 0)] <- dN[which(dS > 0)] / dS[which(dS > 0)]
# process the dnds data with sliding window
pvalue_window <- sliding_window(dN,
dS,
gap_data = gap_data,
w_size = window_size,
g_threshold = gap_threshold)
# generate a matrix with start and end positions for plotting of significant windows
epitopes_alt <-
cluster_matrix(pvalues = pvalue_window,
w_size = window_size,
significance_level = 0.05)
epitopes_alt$method <- "SLAC"
# create data frame with all informations
df_alt <- data.frame(
position = dnds_data$Codon,
selection = dNdS,
selection_smooth = smooth.spline(dnds_data$Codon, dNdS, spar =
0.35)$y,
gap = gap_data$V1,
gap_smooth = smooth.spline(dnds_data$Codon, gap_data$V1, spar =
0.35)$y,
stringsAsFactors = FALSE
)
df_alt$method <- "SLAC"
# process eden
gap_data <- try(read.table(gap_path, header = F))
dnds_data <- try(read.table(dnds_path_eden, header = T))
dN <- dnds_data$Nd_tip + dnds_data$Nd
dS <- dnds_data$Sd_tip + dnds_data$Sd
dNdS <- rep(0, length(dN))
dNdS[which(dS > 0)] <- dN[which(dS > 0)] / dS[which(dS > 0)]
# process the dnds data with sliding window
pvalue_window <- sliding_window(dN,
dS,
gap_data = gap_data,
w_size = window_size,
g_threshold = gap_threshold)
# generate a matrix with start and end positions for plotting of significant windows
epitopes_eden <-
cluster_matrix(pvalues = pvalue_window,
w_size = window_size,
significance_level = 0.05)
epitopes_eden$method <- "EDEN"
# create data frame with all informations
df_eden <- data.frame(
position = dnds_data$pos,
selection = dNdS,
selection_smooth = smooth.spline(dnds_data$pos, dNdS, spar =
0.35)$y,
gap = gap_data$V1,
gap_smooth = smooth.spline(dnds_data$pos, gap_data$V1, spar =
0.35)$y,
stringsAsFactors = FALSE
)
df_eden$method <- "EDEN"
epitopes <- rbind(epitopes_alt, epitopes_eden)
df_b <- rbind(df_eden, df_alt)
fig_a <- ggplot(df_b, aes(position, selection, color=method))
# fig_a <- fig_a + geom_point(size = 1.7, alpha = 3 / 4)
fig_a <- fig_a + geom_line(aes(y = selection_smooth))
fig_a <- fig_a + theme_classic() + xlab("position in alignment") + ylab("dN/dS ratio")
fig_a <- fig_a + ggtitle(paste(dnds_path_eden, sep = ""))
# highlight significant aereas
fig_a <-
fig_a + geom_rect(data = epitopes_eden,
aes(NULL, NULL, xmin = start, xmax = end),
ymin = -Inf,
ymax = Inf,
fill = "red"
)
fig_a <-
fig_a + geom_rect(data = epitopes,
aes(NULL, NULL, xmin = start, xmax = end),
ymin = -Inf,
ymax = Inf,
fill = "blue"
)
fig_a
}
create_msa_plot_slac <- function(dnds_path = "",
gap_path = "",
only_legend = FALSE,
window_size = 10,
gap_threshold = 0.6,
gapcolor = TRUE,
points = TRUE) {
require(zoo)
gap_data <- try(read.table(gap_path, header = F))
dnds_data <- try(read.table(dnds_path, header = T, sep=','))
dN <- dnds_data$dN
dS <- dnds_data$dS
dNdS <- rep(0, length(dN))
dNdS[which(dS > 0)] <- dN[which(dS > 0)] / dS[which(dS > 0)]
# process the dnds data with sliding window
pvalue_window <- sliding_window(dN,
dS,
gap_data = gap_data,
w_size = window_size,
g_threshold = gap_threshold)
# generate a matrix with start and end positions for plotting of significant windows
epitopes <-
cluster_matrix(pvalues = pvalue_window,
w_size = window_size,
significance_level = 0.05)
# create data frame with all informations
df <- data.frame(
position = dnds_data$Codon,
selection = dNdS,
selection_smooth = smooth.spline(dnds_data$Codon, dNdS, spar =
0.35)$y,
gap = gap_data$V1,
gap_smooth = smooth.spline(dnds_data$Codon, gap_data$V1, spar =
0.35)$y,
stringsAsFactors = FALSE
)
#### genereate plot ####
# create sequence plot figure with dnds and gap informations
fig_a <- ggplot(df, aes(position, selection))
fig_a <-
fig_a + theme_classic() + xlab("position in alignment") + ylab("dN/dS ratio")
fig_a <- fig_a + ggtitle(paste(dnds_path, sep = ""))
# highlight significant aereas
fig_a <-
fig_a + geom_rect(
data = epitopes,
aes(NULL, NULL, xmin = start, xmax = end),
ymin = -Inf,
ymax = Inf,
fill = "red"
)
fig_a <-
fig_a + geom_ribbon(aes(x = position, ymax = selection_smooth, ymin = 1), fill =
"grey80")
if (points) {
if (gapcolor) {
fig_a <- fig_a + geom_point(aes(colour = gap), size = 1.7, alpha = 3 / 4)
} else {
fig_a <- fig_a + geom_point(size = 1.7, alpha = 3 / 4)
}
}
fig_a <-
fig_a + scale_colour_gradient(low = "#4db898", high = "#ea5420")
fig_a <- fig_a + geom_line(aes(y = selection_smooth))
#fig_a <- fig_a + labs(title=paste(sample_name,"; g_threshold=", gap_threshold,"; window_size=", window_size,sep=""))
# fig_a <- fig_a + geom_hline(aes(yintercept=1))
return(fig_a)
}
create_msa_plot <- function(dnds_path = "",
gap_path = "",
only_legend = FALSE,
window_size = 10,
gap_threshold = 0.6,
gapcolor = TRUE,
points = TRUE) {
require(zoo)
gap_data <- try(read.table(gap_path, header = F))
dnds_data <- try(read.table(dnds_path, header = T))
dN <- dnds_data$Nd_tip + dnds_data$Nd
dS <- dnds_data$Sd_tip + dnds_data$Sd
dNdS <- rep(0, length(dN))
dNdS[which(dS > 0)] <- dN[which(dS > 0)] / dS[which(dS > 0)]
# process the dnds data with sliding window
pvalue_window <- sliding_window(dN,
dS,
gap_data = gap_data,
w_size = window_size,
g_threshold = gap_threshold)
# generate a matrix with start and end positions for plotting of significant windows
epitopes <-
cluster_matrix(pvalues = pvalue_window,
w_size = window_size,
significance_level = 0.05)
# create data frame with all informations
df <- data.frame(
position = dnds_data$pos,
selection = dNdS,
selection_smooth = smooth.spline(dnds_data$pos, dNdS, spar =
0.35)$y,
gap = gap_data$V1,
gap_smooth = smooth.spline(dnds_data$pos, gap_data$V1, spar =
0.35)$y,
stringsAsFactors = FALSE
)
#### genereate plot ####
# create sequence plot figure with dnds and gap informations
fig_a <- ggplot(df, aes(position, selection))
fig_a <-
fig_a + theme_classic() + xlab("position in alignment") + ylab("dN/dS ratio")
fig_a <- fig_a + ggtitle(paste(dnds_path, sep = ""))
# highlight significant aereas
fig_a <-
fig_a + geom_rect(
data = epitopes,
aes(NULL, NULL, xmin = start, xmax = end),
ymin = -Inf,
ymax = Inf,
fill = "red"
)
fig_a <-
fig_a + geom_ribbon(aes(x = position, ymax = selection_smooth, ymin = 1), fill =
"grey80")
if (points) {
if (gapcolor) {
fig_a <- fig_a + geom_point(aes(colour = gap), size = 1.7, alpha = 3 / 4)
} else {
fig_a <- fig_a + geom_point(size = 1.7, alpha = 3 / 4)
}
}
fig_a <-
fig_a + scale_colour_gradient(low = "#4db898", high = "#ea5420")
fig_a <- fig_a + geom_line(aes(y = selection_smooth))
#fig_a <- fig_a + labs(title=paste(sample_name,"; g_threshold=", gap_threshold,"; window_size=", window_size,sep=""))
# fig_a <- fig_a + geom_hline(aes(yintercept=1))
return(fig_a)
}
# This function creates a p-value matrix indicating significant positions based
sliding_window <-
function(dN,
dS,
gap_data,
w_size = 10,
g_threshold = 0.2) {
require(zoo)
dNdS_window <- rep(0, length(dN))
if (w_size > 0) {
dN_window <- rollsum(dN, w_size, fill = list(NA, NULL, NA))
dS_window <- rollsum(dS, w_size, fill = list(NA, NULL, NA))
dNdS_window[which(dS_window > 0)] <-
dN_window[which(dS_window > 0)] /
dS_window[which(dS_window > 0)]
dNdS_gap <- rollmean(gap_data$V1, w_size, fill = list(NA, NULL, NA))
dNdS_gap[which(is.na(dNdS_gap))] <- 1
}
# iterate over window and find significant windows with FDR correction
dN_all <- sum(dN, na.rm = T)
dS_all <- sum(dS, na.rm = T)
window <- rep(1, length(dN))
for (i in 1:length(dNdS_window)) {
if (dNdS_gap[i] < g_threshold) {
if (!is.na(dN_window[i]) && !is.na(dS_window[i])) {
test_matrix <- matrix(c(dN_window[i], dN_all,
dS_window[i], dS_all), nrow = 2)
pval <-
fisher.test(test_matrix, alternative = c("greater"))[["p.value"]]
window[i] <- p.adjust(pval, method = "fdr", n = length(window))
} else {
window[i] <- 1
}
} else {
# g_threshold
window[i] <- 1
}
}
return(window)
}
# This function creates start and end coordinates of clusters froma list of pvalues
cluster_matrix <- function(pvalues,
significance_level = 0.05,
w_size = 10) {
iteration <- 1
i <- 1
clusters <- NULL
while (!is.na (which(pvalues[i:length(pvalues)] <= significance_level)[1])) {
# check if there are some significant elements
if (i == 1) {
i <-
which(pvalues[i:length(pvalues)] <= significance_level)[1] # first sig elem
clusters$start[iteration] <- i
i <-
which(pvalues[i:length(pvalues)] > significance_level)[1] + clusters$start[iteration] - 1 # last sig. elem.
clusters$end[iteration] <- i
iteration <- iteration + 1
} else {
i <-
which(pvalues[i:length(pvalues)] <= significance_level)[1] + clusters$end[iteration -
1]
clusters$start[iteration] <- i
i <-
which(pvalues[i:length(pvalues)] > significance_level)[1] + clusters$start[iteration] - 1
clusters$end[iteration] <- i
iteration <- iteration + 1
}
}
if (is.null(clusters)) {
# add some zero values to prevent plotting issues
clusters$start[1] <- 0
clusters$end[1] <- 0
clusters <- as.data.frame(clusters)
} else {
clusters <- as.data.frame(clusters)
clusters$start <- clusters$start - w_size / 2
clusters$end <- clusters$end + w_size / 2
}
return(clusters)
}