Nothing
R/orPlot.R
In SeqFeatR: A Tool to Associate FASTA Sequences and Features
Defines functions
two_wo_set_input_file_known_sequences
create_sequence_graphic_inner
require(plotrix)
two_wo_set_input_file_known_sequences <- function(input_file, seperator){
data <- read.csv2(input_file, stringsAsFactors = FALSE, sep=seperator)
.GlobalEnv[["input_data"]] <- data
}
orPlot <- structure(function(#create two side plot
### Generates an sequence bar plot.
##details<< This tool took the results from the search for epitopes and combines them in an graphical output where each position in the sequence has its own bar. The heigt of the bar is the log10 of the p-value, the direction is from the odds ratio (below one is down, above one is up). If the user adds a column number for an amino acid or DNA base, this sequence is added above each bar. Further sequences (such as a kind of reference or consensus sequence) besides the one created with find_possible_epiopes have to be added manually to the input *.csv file. If your bar plot should be additionally colored by another type of information - like eg entropy of the sequence - you can add the position of the column with this information in "has_color". If you do not want any color, than just insert 0. The column should contain only numbers between zero and one.
##note<< The reference sequence has to be aligned with the sequences used for possible epitope analysis to be shown correctly.
path_to_file_assocpoint_results = NULL,
# the input file with data
save_name_pdf,
# the name of the save file. Will be processed further with numbers. See details.
separator = ";",
# value with which the csv is seperated.
number_of_cases = 2,
# the number of different search types like different HLA genes or tropism
odds_column_position = c(6),
# columnnumber of the odds ratio to determine if up or down. Can be a repeated value as every 4th column
p_value_column_position = c(2),
# columnnumber of the p-value to determine the height. Can be a repeated value as every 4th column
name_column_position = c(2),
# the name for the right lable
freq = 7,
# frequency of repeat in columns with more than two cases
sequence_column_position = NULL,
# columnnumber of the amino acid sequence to show above the seq. Can be a repeated value as every 4th column
max_y_axis = 50,
# the estimated guess of the best p-value. Usuall the number behind the e as positiv number
interval = 10,
# the intervall on the y axis
has_color = 0,
# if there shoul be presented another kind of information as color. See details.
bias = 5,
# bias of colors
p_or_od = "P"
# if p values or OR should be plotted
){
result <- create_sequence_graphic_inner(path_to_file_assocpoint_results, save_name_pdf, separator, number_of_cases, odds_column_position, p_value_column_position, name_column_position, freq, sequence_column_position, max_y_axis, interval, has_color, bias, p_or_od)
return (result)
},ex=function(){
ep <- system.file("extdata", "epitope_results.csv", package="SeqFeatR")
en <- system.file("extdata", "final_graphic", package="SeqFeatR")
orPlot(ep,
en,
";",
14,
11,
2,
2,
12,
NULL,
2,
0.5,
0,
5,
"P"
)
})
create_sequence_graphic_inner <- function(input_file, save_name,seperator, number_of_cases, odds.position, p.value.position, name, freq, aminoacid.position, high_log_p, intervall, has_color, bias, por){
if (is.null(input_file)==FALSE){
two_wo_set_input_file_known_sequences(input_file, seperator)
}
data <- .GlobalEnv[["input_data"]]
labr <- c(seq(high_log_p, 0, by=-intervall), seq(intervall, high_log_p, by=intervall))
number_of_pages <- ceiling((nrow(data)/50)/4)
frequency <- 0
#repeat cases
for (k in 1:number_of_cases){
head <- colnames(data)[name+frequency]
print (head)
print (odds.position+frequency)
odds.ratio <- as.numeric(data[,(odds.position+frequency)])
print (data)
corrected_p <- as.numeric(data[,(p.value.position+frequency)])
AA <- c()
colly <- c()
if (has_color){
color <- -log10(as.numeric(data[,has_color]))
ColorRamp <- colorRampPalette(c("green", "blue", "darkblue"), bias = as.numeric(bias))(300)
ColorLevels <- seq(0, max(color), length=length(ColorRamp))
for (i in 1:length(color)){
colly <- c(colly, ColorRamp[which(ColorLevels >= color[i])[1]])
}
}
if (is.null(aminoacid.position)){
aminoacid.position <- NA
}
if (!is.na(aminoacid.position)){
#### FERTIGMACHNE
AA <- as.character(data[,(aminoacid.position+frequency)])
}
height <- c()
namese <- c()
if (por == "P"){
for (i in 1:length(odds.ratio)){
if (odds.ratio[i] > 1){
if (is.na(corrected_p[i])){
height <- c(height, NA)
}else{
height <- c(height, -log10(corrected_p[i]))
}
}
else{
if (is.na(corrected_p[i])){
height <- c(height, NA)
}else{
height <- c(height, log10(corrected_p[i]))
}
}
}
ylab_lable <- "log10 (p-value)"
}
if (por == "OR"){
for (i in 1:length(odds.ratio)){
if (is.infinite(odds.ratio[i])){
height <- c(height, log10(max(odds.ratio[is.finite(odds.ratio)]))+1)
}else{
height <- c(height, log10(odds.ratio[i]))
}
}
ylab_lable <- "log10 (OR)"
}
#repeat pdf
m <- 1
n <- 50
for (i in 1:number_of_pages){
pdf(paste(save_name, k, "_", i, ".pdf", sep=""), width = 11.69, height = 18.27)
if (has_color){
layout(matrix(c(1,2,3,4,5,6,7,8), 4, 2, byrow = TRUE), widths=c(10,1))
}else{
par( mfrow = c( 4, 1 ) )
}
for (j in 1:4){
#cat (m, n, "\n")
#if last page less than 4 and last one got, end here!!!
if (m > length(height)){
break
}else if (n > length(height) && m > (length(height)-2)){
n <- length(height)
if (has_color){
barplot(height[m:n], axes = FALSE, xlab="position", ylab=ylab_lable, width=1, space=0, ylim = c(-high_log_p,high_log_p), col=colly[m:n])
}else {
barplot(height[m:n], axes = FALSE, xlab="position", ylab=ylab_lable, width=1, space=0, ylim = c(-high_log_p,high_log_p))
}
#cat (m, n, "\n")
axis(1, at=seq(0.5), labels=n) #Position
axis(2, at=seq(-high_log_p, high_log_p, by=intervall), labels=labr) #p-value
mtext(paste("<-- !",head," | ",head," -->", sep=""), side=4, line=-3, cex=0.8, padj = 1)
#axis(3, at=seq(0.5), labels=namese[n], line=1, tck=0) #V and C
if (!is.na(aminoacid.position)){
axis(3, at=seq(0.5), labels=AA[n], tck=0) #Sequence!!
}
if (has_color){
image(1, ColorLevels, matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1), col=ColorRamp,xlab="",ylab="",xaxt="n", las = 1)
}
break
}else if(n > length(height)){
n <- length(height)
new_end <- n-m+0.5
if (has_color){
barplot(height[m:n], axes = FALSE, xlab="position", ylab=ylab_lable, width=1, space=0, ylim = c(-high_log_p,high_log_p), col=colly[m:n])
}else {
barplot(height[m:n], axes = FALSE, xlab="position", ylab=ylab_lable, width=1, space=0, ylim = c(-high_log_p,high_log_p))
}
#cat (m, n, "\n")
#print (seq(m, n))
#print (seq(0.5, new_end, by=1))
axis(1, at=seq(0.5, new_end, by=1), labels=seq(m, n)) #Position
axis(2, at=seq(-high_log_p, high_log_p, by=intervall), labels=labr) #p-value
mtext(paste("<-- !",head," | ",head," -->", sep=""), side=4, line=-3, cex=0.8, padj = 1)
#axis(3, at=seq(0.5, new_end, by=1), labels=namese[m:n], line=1, tck=0) #V and C
if (!is.na(aminoacid.position)){
axis(3, at=seq(0.5, new_end, by=1), labels=AA[m:n], tck=0) #Sequence!!
}
if (has_color){
image(1, ColorLevels, matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1), col=ColorRamp,xlab="",ylab="",xaxt="n", las = 1)
}
}else{
#repeat on one pdf
### oben R5, unten X4
if (has_color){
barplot(height[m:n], axes = FALSE, xlab="position", ylab=ylab_lable, width=1, space=0, ylim = c(-high_log_p,high_log_p), col=colly[m:n])
}else {
barplot(height[m:n], axes = FALSE, xlab="position", ylab=ylab_lable, width=1, space=0, ylim = c(-high_log_p,high_log_p))
}
#cat (m, n, "\n")
axis(1, at=seq(0.5, 49.5, by=1), labels=seq(m, n)) #Position
axis(2, at=seq(-high_log_p, high_log_p, by=intervall), labels=labr) #p-value
mtext(paste("<-- !",head," | ",head," -->", sep=""), side=4, line=-3, cex=0.8, padj = 1)
#axis(3, at=seq(0.5, 49.5, by=1), labels=namese[m:n], line=1, tck=0) #V and C
if (!is.na(aminoacid.position)){
axis(3, at=seq(0.5, 49.5, by=1), labels=AA[m:n], tck=0) #Sequence!!
}
if (has_color){
image(1, ColorLevels, matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1), col=ColorRamp,xlab="",ylab="",xaxt="n", las = 1)
}
}
m <- m + 50
n <- n + 50
}
dev.off()
}
frequency <- frequency + freq
}
print ("fin")
return (TRUE)
}
#orPlot("Results_for_graphic_env_subtype_C_with_entropy.csv",
#"../inst/extdata/Results_for_graphic_env_subtype_C_with_entropy.pdf",
#";",
#1,
#7,
#4,
#2,
#7,
#6,
#4,
#0.5,
#4,
#2,
#"OR")
Try the SeqFeatR package in your browser
Any scripts or data that you put into this service are public.
SeqFeatR documentation built on May 2, 2019, 3:10 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions two_wo_set_input_file_known_sequences create_sequence_graphic_inner
require(plotrix)
two_wo_set_input_file_known_sequences <- function(input_file, seperator){
data <- read.csv2(input_file, stringsAsFactors = FALSE, sep=seperator)
.GlobalEnv[["input_data"]] <- data
}
orPlot <- structure(function(#create two side plot
### Generates an sequence bar plot.
##details<< This tool took the results from the search for epitopes and combines them in an graphical output where each position in the sequence has its own bar. The heigt of the bar is the log10 of the p-value, the direction is from the odds ratio (below one is down, above one is up). If the user adds a column number for an amino acid or DNA base, this sequence is added above each bar. Further sequences (such as a kind of reference or consensus sequence) besides the one created with find_possible_epiopes have to be added manually to the input *.csv file. If your bar plot should be additionally colored by another type of information - like eg entropy of the sequence - you can add the position of the column with this information in "has_color". If you do not want any color, than just insert 0. The column should contain only numbers between zero and one.
##note<< The reference sequence has to be aligned with the sequences used for possible epitope analysis to be shown correctly.
path_to_file_assocpoint_results = NULL,
# the input file with data
save_name_pdf,
# the name of the save file. Will be processed further with numbers. See details.
separator = ";",
# value with which the csv is seperated.
number_of_cases = 2,
# the number of different search types like different HLA genes or tropism
odds_column_position = c(6),
# columnnumber of the odds ratio to determine if up or down. Can be a repeated value as every 4th column
p_value_column_position = c(2),
# columnnumber of the p-value to determine the height. Can be a repeated value as every 4th column
name_column_position = c(2),
# the name for the right lable
freq = 7,
# frequency of repeat in columns with more than two cases
sequence_column_position = NULL,
# columnnumber of the amino acid sequence to show above the seq. Can be a repeated value as every 4th column
max_y_axis = 50,
# the estimated guess of the best p-value. Usuall the number behind the e as positiv number
interval = 10,
# the intervall on the y axis
has_color = 0,
# if there shoul be presented another kind of information as color. See details.
bias = 5,
# bias of colors
p_or_od = "P"
# if p values or OR should be plotted
){
result <- create_sequence_graphic_inner(path_to_file_assocpoint_results, save_name_pdf, separator, number_of_cases, odds_column_position, p_value_column_position, name_column_position, freq, sequence_column_position, max_y_axis, interval, has_color, bias, p_or_od)
return (result)
},ex=function(){
ep <- system.file("extdata", "epitope_results.csv", package="SeqFeatR")
en <- system.file("extdata", "final_graphic", package="SeqFeatR")
orPlot(ep,
en,
";",
14,
11,
2,
2,
12,
NULL,
2,
0.5,
0,
5,
"P"
)
})
create_sequence_graphic_inner <- function(input_file, save_name,seperator, number_of_cases, odds.position, p.value.position, name, freq, aminoacid.position, high_log_p, intervall, has_color, bias, por){
if (is.null(input_file)==FALSE){
two_wo_set_input_file_known_sequences(input_file, seperator)
}
data <- .GlobalEnv[["input_data"]]
labr <- c(seq(high_log_p, 0, by=-intervall), seq(intervall, high_log_p, by=intervall))
number_of_pages <- ceiling((nrow(data)/50)/4)
frequency <- 0
#repeat cases
for (k in 1:number_of_cases){
head <- colnames(data)[name+frequency]
print (head)
print (odds.position+frequency)
odds.ratio <- as.numeric(data[,(odds.position+frequency)])
print (data)
corrected_p <- as.numeric(data[,(p.value.position+frequency)])
AA <- c()
colly <- c()
if (has_color){
color <- -log10(as.numeric(data[,has_color]))
ColorRamp <- colorRampPalette(c("green", "blue", "darkblue"), bias = as.numeric(bias))(300)
ColorLevels <- seq(0, max(color), length=length(ColorRamp))
for (i in 1:length(color)){
colly <- c(colly, ColorRamp[which(ColorLevels >= color[i])[1]])
}
}
if (is.null(aminoacid.position)){
aminoacid.position <- NA
}
if (!is.na(aminoacid.position)){
#### FERTIGMACHNE
AA <- as.character(data[,(aminoacid.position+frequency)])
}
height <- c()
namese <- c()
if (por == "P"){
for (i in 1:length(odds.ratio)){
if (odds.ratio[i] > 1){
if (is.na(corrected_p[i])){
height <- c(height, NA)
}else{
height <- c(height, -log10(corrected_p[i]))
}
}
else{
if (is.na(corrected_p[i])){
height <- c(height, NA)
}else{
height <- c(height, log10(corrected_p[i]))
}
}
}
ylab_lable <- "log10 (p-value)"
}
if (por == "OR"){
for (i in 1:length(odds.ratio)){
if (is.infinite(odds.ratio[i])){
height <- c(height, log10(max(odds.ratio[is.finite(odds.ratio)]))+1)
}else{
height <- c(height, log10(odds.ratio[i]))
}
}
ylab_lable <- "log10 (OR)"
}
#repeat pdf
m <- 1
n <- 50
for (i in 1:number_of_pages){
pdf(paste(save_name, k, "_", i, ".pdf", sep=""), width = 11.69, height = 18.27)
if (has_color){
layout(matrix(c(1,2,3,4,5,6,7,8), 4, 2, byrow = TRUE), widths=c(10,1))
}else{
par( mfrow = c( 4, 1 ) )
}
for (j in 1:4){
#cat (m, n, "\n")
#if last page less than 4 and last one got, end here!!!
if (m > length(height)){
break
}else if (n > length(height) && m > (length(height)-2)){
n <- length(height)
if (has_color){
barplot(height[m:n], axes = FALSE, xlab="position", ylab=ylab_lable, width=1, space=0, ylim = c(-high_log_p,high_log_p), col=colly[m:n])
}else {
barplot(height[m:n], axes = FALSE, xlab="position", ylab=ylab_lable, width=1, space=0, ylim = c(-high_log_p,high_log_p))
}
#cat (m, n, "\n")
axis(1, at=seq(0.5), labels=n) #Position
axis(2, at=seq(-high_log_p, high_log_p, by=intervall), labels=labr) #p-value
mtext(paste("<-- !",head," | ",head," -->", sep=""), side=4, line=-3, cex=0.8, padj = 1)
#axis(3, at=seq(0.5), labels=namese[n], line=1, tck=0) #V and C
if (!is.na(aminoacid.position)){
axis(3, at=seq(0.5), labels=AA[n], tck=0) #Sequence!!
}
if (has_color){
image(1, ColorLevels, matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1), col=ColorRamp,xlab="",ylab="",xaxt="n", las = 1)
}
break
}else if(n > length(height)){
n <- length(height)
new_end <- n-m+0.5
if (has_color){
barplot(height[m:n], axes = FALSE, xlab="position", ylab=ylab_lable, width=1, space=0, ylim = c(-high_log_p,high_log_p), col=colly[m:n])
}else {
barplot(height[m:n], axes = FALSE, xlab="position", ylab=ylab_lable, width=1, space=0, ylim = c(-high_log_p,high_log_p))
}
#cat (m, n, "\n")
#print (seq(m, n))
#print (seq(0.5, new_end, by=1))
axis(1, at=seq(0.5, new_end, by=1), labels=seq(m, n)) #Position
axis(2, at=seq(-high_log_p, high_log_p, by=intervall), labels=labr) #p-value
mtext(paste("<-- !",head," | ",head," -->", sep=""), side=4, line=-3, cex=0.8, padj = 1)
#axis(3, at=seq(0.5, new_end, by=1), labels=namese[m:n], line=1, tck=0) #V and C
if (!is.na(aminoacid.position)){
axis(3, at=seq(0.5, new_end, by=1), labels=AA[m:n], tck=0) #Sequence!!
}
if (has_color){
image(1, ColorLevels, matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1), col=ColorRamp,xlab="",ylab="",xaxt="n", las = 1)
}
}else{
#repeat on one pdf
### oben R5, unten X4
if (has_color){
barplot(height[m:n], axes = FALSE, xlab="position", ylab=ylab_lable, width=1, space=0, ylim = c(-high_log_p,high_log_p), col=colly[m:n])
}else {
barplot(height[m:n], axes = FALSE, xlab="position", ylab=ylab_lable, width=1, space=0, ylim = c(-high_log_p,high_log_p))
}
#cat (m, n, "\n")
axis(1, at=seq(0.5, 49.5, by=1), labels=seq(m, n)) #Position
axis(2, at=seq(-high_log_p, high_log_p, by=intervall), labels=labr) #p-value
mtext(paste("<-- !",head," | ",head," -->", sep=""), side=4, line=-3, cex=0.8, padj = 1)
#axis(3, at=seq(0.5, 49.5, by=1), labels=namese[m:n], line=1, tck=0) #V and C
if (!is.na(aminoacid.position)){
axis(3, at=seq(0.5, 49.5, by=1), labels=AA[m:n], tck=0) #Sequence!!
}
if (has_color){
image(1, ColorLevels, matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1), col=ColorRamp,xlab="",ylab="",xaxt="n", las = 1)
}
}
m <- m + 50
n <- n + 50
}
dev.off()
}
frequency <- frequency + freq
}
print ("fin")
return (TRUE)
}
#orPlot("Results_for_graphic_env_subtype_C_with_entropy.csv",
#"../inst/extdata/Results_for_graphic_env_subtype_C_with_entropy.pdf",
#";",
#1,
#7,
#4,
#2,
#7,
#6,
#4,
#0.5,
#4,
#2,
#"OR")
Try the SeqFeatR package in your browser
Any scripts or data that you put into this service are public.
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