Nothing
R/getfreqs.R
In SeqFeatR: A Tool to Associate FASTA Sequences and Features
Defines functions
create_correct_FASTA_input
remove.zeroes
get_freqs_inner
#libs
require(Biostrings)
require(ggplot2)
#function to create the same output as the old readFASTA from new read.AAStringSet
create_correct_FASTA_input <- function(sequences){
liste <- as.character(sequences, use.names=TRUE)
result_list <- c()
for (i in 1:length(liste)){
seq=liste[i][[1]]
name = names(liste[i])
entry <- list (name, seq)
names(entry) <- c("desc", "seq")
result_list[[i]] = entry
}
return (result_list)
}
gF_wo_set_input_file_known_sequences <- structure(function(
###read aa sequences from fasta file - they have to be aligned in order to work!
path_to_file
### a FASTA file with sequence data. For reference please look in example file.
){
### the sequences from the FASTA file.
sequences <- readAAStringSet(path_to_file)
.GlobalEnv[["StringSetsequences"]] <- sequences
.GlobalEnv[["sequences"]] <- sequences
},ex=function(){
ep_wo_set_input_file_known_sequences("SeqFeatR/extdata/Example.fasta")
})
gF_wo_set_consensus_file_known_sequences <- structure(function(
###read aa sequences from fasta file - they have to be aligned in order to work!
path_to_file
### a FASTA file with sequence data. For reference please look in example file.
){
### the sequences from the FASTA file.
sequences <- readAAStringSet(path_to_file)
.GlobalEnv[["epi_consensus"]] <- as.character(sequences)
.GlobalEnv[["sequences"]] <- sequences
},ex=function(){
ep_wo_set_input_file_known_sequences("SeqFeatR/extdata/Example.fasta")
})
#removes "0"s from a string
remove.zeroes <- function(s){
if(substr(s, 1, 1) == "0")
a <- remove.zeroes(substr(s, 2, nchar(s)))
else
a <- s
}
getfreqs <- structure(function(
path_to_file_sequence_alignment=NULL,
save_csv,
save_png,
epitope_position_start,
epitope_position_end,
path_to_file_consensus,
patnum_threshold = 1,
### the minimum number of patients of one HLA type to consider in the calculation.
A11,
### the position of the start of the first HLA A Allel in the description block of the FASTA file.
A12,
### the position of the end of the first HLA A Allel in the description block of the FASTA file.
A21,
### the position of the start of the second HLA A Allel in the description block of the FASTA file.
A22,
### the position of the end of the second HLA A Allel in the description block of the FASTA file.
B11,
### the position of the start of the first HLA B Allel in the description block of the FASTA file.
B12,
### the position of the end of the first HLA B Allel in the description block of the FASTA file.
B21,
### the position of the start of the second HLA B Allel in the description block of the FASTA file.
B22,
### the position of the end of the second HLA B Allel in the description block of the FASTA file.
one_feature=FALSE
### if there is only one identifier
){
get_freqs_inner(path_to_file_sequence_alignment, save_csv, save_png, epitope_position_start, epitope_position_end, path_to_file_consensus, patnum_threshold, A11, A12, A21, A22, B11, B12, B21, B22, one_feature)
},ex=function(){
getfreqs("../inst/extdata/Example_aa.fasta",
"../inst/extdata/Freqs.csv",
"../inst/extdata/Freqs.png",
1,
8,
"../inst/extdata/Example_Consensus_aa.fasta",
patnum.threshold=1,
A11 = 10,
A12 = 11,
A21 = 13,
A22 = 14,
B11 = 17,
B12 = 18,
B21 = 20,
B22 = 21,
one_ident = FALSE)
})
get_freqs_inner <- function(path_to_file_s, save_csv, save_png, epi_pos_start, epi_pos_end, epi_consensus, patnum.threshold, A11, A12, A21, A22, B11, B12, B21, B22, one_ident){
ggplot <- NULL
aes <- NULL
kind <- NULL
geom_bar <- NULL
facet_grid <- NULL
xlab <- NULL
ylab <- NULL
theme <- NULL
element_text <- NULL
ggsave <- NULL
if (!is.null(path_to_file_s) && path_to_file_s == 0){
path_to_file_s <- NULL
}
if (!is.null(path_to_file_s) && !(path_to_file_s == "") ){
gF_wo_set_input_file_known_sequences(path_to_file_s)
}
if (!is.null(epi_consensus) && epi_consensus == 0){
epi_consensus <- NULL
}
if (!is.null(epi_consensus) && !(epi_consensus == "") ){
gF_wo_set_consensus_file_known_sequences(epi_consensus)
}
sequences <- .GlobalEnv[["StringSetsequences"]]
epi_consensus <- .GlobalEnv[["epi_consensus"]]
#find the shortest length of FASTA String, if the sequences are of different length
#min_FASTA_length holds this value
.GlobalEnv[["min_FASTA_length"]] <- min(width(sequences))
min_FASTA_length <- .GlobalEnv[["min_FASTA_length"]]
if (min_FASTA_length < epi_pos_start | min_FASTA_length < epi_pos_end){
print ("Warning! Your alignment is shorter than your epitope end position!")
}
#find out all allels that occur in given sequences
allels.all <- c()
allels.count <- c()
new_sequence_names <- c()
if (one_ident){
for(i in 1:length(sequences)){
#the used values depend on fasta file; they possibly have to be changed - the same holds for the block some lines below
#tropismus.all holds all tropismus from the FASTA files, even duplicates
#tropismus holds no duplicates and none which are only a letter, without number (if the type is 00)
#tropismus.count counts how often the certain alles is there
allels.all <- c(allels.all, strsplit(names(sequences)[i], ";")[[1]][2])
new_sequence_names <- names(sequences)
}
}else{
for(i in 1:length(sequences)){
#the used values depend on fasta file; they possibly have to be changed - the same holds for the block some lines below
#allels.all holds all allels from the FASTA files, even duplicates
#allels holds no duplicates and none which are only a letter, without number (if the type is 00)
#allels.count counts how often the certain alles is there
f <- paste("A", remove.zeroes(substr(names(sequences)[i], A11, A12)), sep="")
s <- paste("A", remove.zeroes(substr(names(sequences)[i], A21, A22)), sep="")
t <- paste("B", remove.zeroes(substr(names(sequences)[i], B11, B12)), sep="")
o <- paste("B", remove.zeroes(substr(names(sequences)[i], B21, B22)), sep="")
allels.all <- c(allels.all, f, s, t, o)
new_sequence_names <- c(new_sequence_names, paste(f, s, t, o))
}
}
allels <- sort(unique(allels.all[allels.all!="A" & allels.all!="B"]))
for(i in 1:length(allels)){
allels.count <- c(allels.count, sum(allels.all==allels[i]))
}
# allels.counter counts the allels which are abouve the patnum.threshold
allels.counter <- 1
sequences_names <- names(sequences)
result_matrix_abs <- matrix(rep(0, length(allels.count)*(nchar(epi_consensus)+2)), ncol=nchar(epi_consensus)+2)
colnames(result_matrix_abs) <- c("Allel", "Freq", strsplit(epi_consensus, "")[[1]])
result_matrix_rel <- matrix(rep(0, length(allels.count)*(nchar(epi_consensus)+2)), ncol=nchar(epi_consensus)+2)
colnames(result_matrix_rel) <- c("Allel", "Freq", strsplit(epi_consensus, "")[[1]])
result_matrix_abs_not <- matrix(rep(0, length(allels.count)*(nchar(epi_consensus)+2)), ncol=nchar(epi_consensus)+2)
colnames(result_matrix_abs) <- c("Allel", "Freq", strsplit(epi_consensus, "")[[1]])
result_matrix_rel_not <- matrix(rep(0, length(allels.count)*(nchar(epi_consensus)+2)), ncol=nchar(epi_consensus)+2)
colnames(result_matrix_rel) <- c("Allel", "Freq", strsplit(epi_consensus, "")[[1]])
for(allel.num in 1:length(allels)){
if(allels.count[allel.num]>patnum.threshold){
cat ("allel: ", allel.num, "\n")
current_allel <- allels[allel.num]
has_allel <- grep(current_allel, new_sequence_names)
has_allel_not <- grep(current_allel, new_sequence_names, invert = T)
result_matrix_abs[allels.counter,2] <- length(has_allel)
result_matrix_abs_not[allels.counter,2] <- length(has_allel_not)
result_matrix_abs[allels.counter,1] <- allels[allel.num]
result_matrix_rel[allels.counter,1] <- allels[allel.num]
result_matrix_abs_not[allels.counter,1] <- paste("!", allels[allel.num])
result_matrix_rel_not[allels.counter,1] <- paste("!", allels[allel.num])
# for each letter in FASTA:
col <- 3
for (j in epi_pos_start:epi_pos_end){
#for each point in sequence do the following:
# for each aa in length of possible aas:
just_this_position <- subseq(sequences, j, j)
mat <- as.matrix(just_this_position)
aa_in_consensus <- substr(epi_consensus, j, j)
subset_has_allel <- mat[has_allel]
not_consensus <- length(which(subset_has_allel != aa_in_consensus))
result_matrix_abs[allels.counter,col] <- not_consensus
result_matrix_rel[allels.counter,col] <- not_consensus/length(subset_has_allel)
subset_has_not_allel <- mat[has_allel_not]
not_consensus_not <- length(which(subset_has_not_allel != aa_in_consensus))
result_matrix_abs_not[allels.counter,col] <- not_consensus_not
result_matrix_rel_not[allels.counter,col] <- not_consensus_not/length(subset_has_not_allel)
col <- col + 1
}
allels.counter <- allels.counter + 1
}
}
if (length(which(result_matrix_abs[,1]=="0")) > 0){
result_matrix_abs <- result_matrix_abs[-(which(result_matrix_abs[,1]=="0")),]
result_matrix_rel <- result_matrix_rel[-(which(result_matrix_rel[,1]=="0")),]
result_matrix_abs_not <- result_matrix_abs_not[-(which(result_matrix_abs_not[,1]=="0")),]
result_matrix_rel_not <- result_matrix_rel_not[-(which(result_matrix_rel_not[,1]=="0")),]
}
result_matrix <- result_matrix_abs[,c(1,2)]
result_matrix_not <- result_matrix_abs_not[,c(1,2)]
coln <- c("Allels", "Freqs")
for (i in 3:ncol(result_matrix_rel)){
result_matrix <- cbind(result_matrix, cbind(result_matrix_abs[,i], result_matrix_rel[,i]))
coln <- c(coln, paste(colnames(result_matrix_abs)[i], "_abs", sep=""), paste(colnames(result_matrix_abs)[i], "_rel", sep=""))
}
colnames(result_matrix) <- coln
coln <- c("Allels", "Freqs")
for (i in 3:ncol(result_matrix_rel_not)){
result_matrix_not <- cbind(result_matrix_not, cbind(result_matrix_abs_not[,i], result_matrix_rel_not[,i]))
coln <- c(coln, paste(colnames(result_matrix_abs_not)[i], "_abs", sep=""), paste(colnames(result_matrix_abs_not)[i], "_rel", sep=""))
}
colnames(result_matrix_not) <- coln
result_matrix <- rbind(result_matrix, result_matrix_not)
grap_result <- c()
for (i in 1:nrow(result_matrix)){
pos <- 3
for (j in 1:nchar(epi_consensus) ){
if (length(grep("!", result_matrix[i,1], fixed=TRUE)) > 0){
grap_result <- rbind(grap_result, c(unlist(strsplit(result_matrix[i,1], split=' ', fixed=TRUE))[2], letter=paste(j, "_", substr(epi_consensus, j, j), sep=""), result_matrix[i,pos], kind="not allel"))
}else{
grap_result <- rbind(grap_result, c(result_matrix[i,1], letter=paste(j, "_", substr(epi_consensus, j, j), sep=""), result_matrix[i,pos], kind="allel"))
}
pos <- pos+2
}
}
grap_result <- as.data.frame(grap_result)
print (grap_result)
colnames(grap_result) <- c("allels", "letter", "value", "kind")
grap_result$value <- as.numeric(as.character(grap_result$value))
grap_result <- within(grap_result, letter <- factor(letter, levels = unique(grap_result$letter),ordered = TRUE))
plot <- ggplot(data=grap_result, aes(x=letter, y=value, fill=kind)) + geom_bar(stat="identity", position="dodge") + facet_grid(allels ~.) + xlab("") + ylab("not epitope amino acid[%]") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
ggsave(plot, file=save_png)
write.csv2(result_matrix, file=save_csv)
}
#getfreqs("TEST.fasta", #<- Deine Fasta
# "Freqs.csv",
# "Freqs.svg",
# 1, #<- Kann so bleiben
# 30, #<- Laenge des Consensus (was ja gleichlang wie dein alignment sein sollte)
# "TEST_Consensus_aa.fasta", # <- Dein Consensus
# patnum_threshold=1,
# A11 = 10, # <- HLA in Header
# A12 = 11, # <- HLA in Header
# A21 = 13, # <- HLA in Header
# A22 = 14, # <- HLA in Header
# B11 = 17, # <- HLA in Header
# B12 = 18, # <- HLA in Header
# B21 = 20, # <- HLA in Header
# B22 = 21, # <- HLA in Header
# one_feature = TRUE)
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Defines functions create_correct_FASTA_input remove.zeroes get_freqs_inner
#libs
require(Biostrings)
require(ggplot2)
#function to create the same output as the old readFASTA from new read.AAStringSet
create_correct_FASTA_input <- function(sequences){
liste <- as.character(sequences, use.names=TRUE)
result_list <- c()
for (i in 1:length(liste)){
seq=liste[i][[1]]
name = names(liste[i])
entry <- list (name, seq)
names(entry) <- c("desc", "seq")
result_list[[i]] = entry
}
return (result_list)
}
gF_wo_set_input_file_known_sequences <- structure(function(
###read aa sequences from fasta file - they have to be aligned in order to work!
path_to_file
### a FASTA file with sequence data. For reference please look in example file.
){
### the sequences from the FASTA file.
sequences <- readAAStringSet(path_to_file)
.GlobalEnv[["StringSetsequences"]] <- sequences
.GlobalEnv[["sequences"]] <- sequences
},ex=function(){
ep_wo_set_input_file_known_sequences("SeqFeatR/extdata/Example.fasta")
})
gF_wo_set_consensus_file_known_sequences <- structure(function(
###read aa sequences from fasta file - they have to be aligned in order to work!
path_to_file
### a FASTA file with sequence data. For reference please look in example file.
){
### the sequences from the FASTA file.
sequences <- readAAStringSet(path_to_file)
.GlobalEnv[["epi_consensus"]] <- as.character(sequences)
.GlobalEnv[["sequences"]] <- sequences
},ex=function(){
ep_wo_set_input_file_known_sequences("SeqFeatR/extdata/Example.fasta")
})
#removes "0"s from a string
remove.zeroes <- function(s){
if(substr(s, 1, 1) == "0")
a <- remove.zeroes(substr(s, 2, nchar(s)))
else
a <- s
}
getfreqs <- structure(function(
path_to_file_sequence_alignment=NULL,
save_csv,
save_png,
epitope_position_start,
epitope_position_end,
path_to_file_consensus,
patnum_threshold = 1,
### the minimum number of patients of one HLA type to consider in the calculation.
A11,
### the position of the start of the first HLA A Allel in the description block of the FASTA file.
A12,
### the position of the end of the first HLA A Allel in the description block of the FASTA file.
A21,
### the position of the start of the second HLA A Allel in the description block of the FASTA file.
A22,
### the position of the end of the second HLA A Allel in the description block of the FASTA file.
B11,
### the position of the start of the first HLA B Allel in the description block of the FASTA file.
B12,
### the position of the end of the first HLA B Allel in the description block of the FASTA file.
B21,
### the position of the start of the second HLA B Allel in the description block of the FASTA file.
B22,
### the position of the end of the second HLA B Allel in the description block of the FASTA file.
one_feature=FALSE
### if there is only one identifier
){
get_freqs_inner(path_to_file_sequence_alignment, save_csv, save_png, epitope_position_start, epitope_position_end, path_to_file_consensus, patnum_threshold, A11, A12, A21, A22, B11, B12, B21, B22, one_feature)
},ex=function(){
getfreqs("../inst/extdata/Example_aa.fasta",
"../inst/extdata/Freqs.csv",
"../inst/extdata/Freqs.png",
1,
8,
"../inst/extdata/Example_Consensus_aa.fasta",
patnum.threshold=1,
A11 = 10,
A12 = 11,
A21 = 13,
A22 = 14,
B11 = 17,
B12 = 18,
B21 = 20,
B22 = 21,
one_ident = FALSE)
})
get_freqs_inner <- function(path_to_file_s, save_csv, save_png, epi_pos_start, epi_pos_end, epi_consensus, patnum.threshold, A11, A12, A21, A22, B11, B12, B21, B22, one_ident){
ggplot <- NULL
aes <- NULL
kind <- NULL
geom_bar <- NULL
facet_grid <- NULL
xlab <- NULL
ylab <- NULL
theme <- NULL
element_text <- NULL
ggsave <- NULL
if (!is.null(path_to_file_s) && path_to_file_s == 0){
path_to_file_s <- NULL
}
if (!is.null(path_to_file_s) && !(path_to_file_s == "") ){
gF_wo_set_input_file_known_sequences(path_to_file_s)
}
if (!is.null(epi_consensus) && epi_consensus == 0){
epi_consensus <- NULL
}
if (!is.null(epi_consensus) && !(epi_consensus == "") ){
gF_wo_set_consensus_file_known_sequences(epi_consensus)
}
sequences <- .GlobalEnv[["StringSetsequences"]]
epi_consensus <- .GlobalEnv[["epi_consensus"]]
#find the shortest length of FASTA String, if the sequences are of different length
#min_FASTA_length holds this value
.GlobalEnv[["min_FASTA_length"]] <- min(width(sequences))
min_FASTA_length <- .GlobalEnv[["min_FASTA_length"]]
if (min_FASTA_length < epi_pos_start | min_FASTA_length < epi_pos_end){
print ("Warning! Your alignment is shorter than your epitope end position!")
}
#find out all allels that occur in given sequences
allels.all <- c()
allels.count <- c()
new_sequence_names <- c()
if (one_ident){
for(i in 1:length(sequences)){
#the used values depend on fasta file; they possibly have to be changed - the same holds for the block some lines below
#tropismus.all holds all tropismus from the FASTA files, even duplicates
#tropismus holds no duplicates and none which are only a letter, without number (if the type is 00)
#tropismus.count counts how often the certain alles is there
allels.all <- c(allels.all, strsplit(names(sequences)[i], ";")[[1]][2])
new_sequence_names <- names(sequences)
}
}else{
for(i in 1:length(sequences)){
#the used values depend on fasta file; they possibly have to be changed - the same holds for the block some lines below
#allels.all holds all allels from the FASTA files, even duplicates
#allels holds no duplicates and none which are only a letter, without number (if the type is 00)
#allels.count counts how often the certain alles is there
f <- paste("A", remove.zeroes(substr(names(sequences)[i], A11, A12)), sep="")
s <- paste("A", remove.zeroes(substr(names(sequences)[i], A21, A22)), sep="")
t <- paste("B", remove.zeroes(substr(names(sequences)[i], B11, B12)), sep="")
o <- paste("B", remove.zeroes(substr(names(sequences)[i], B21, B22)), sep="")
allels.all <- c(allels.all, f, s, t, o)
new_sequence_names <- c(new_sequence_names, paste(f, s, t, o))
}
}
allels <- sort(unique(allels.all[allels.all!="A" & allels.all!="B"]))
for(i in 1:length(allels)){
allels.count <- c(allels.count, sum(allels.all==allels[i]))
}
# allels.counter counts the allels which are abouve the patnum.threshold
allels.counter <- 1
sequences_names <- names(sequences)
result_matrix_abs <- matrix(rep(0, length(allels.count)*(nchar(epi_consensus)+2)), ncol=nchar(epi_consensus)+2)
colnames(result_matrix_abs) <- c("Allel", "Freq", strsplit(epi_consensus, "")[[1]])
result_matrix_rel <- matrix(rep(0, length(allels.count)*(nchar(epi_consensus)+2)), ncol=nchar(epi_consensus)+2)
colnames(result_matrix_rel) <- c("Allel", "Freq", strsplit(epi_consensus, "")[[1]])
result_matrix_abs_not <- matrix(rep(0, length(allels.count)*(nchar(epi_consensus)+2)), ncol=nchar(epi_consensus)+2)
colnames(result_matrix_abs) <- c("Allel", "Freq", strsplit(epi_consensus, "")[[1]])
result_matrix_rel_not <- matrix(rep(0, length(allels.count)*(nchar(epi_consensus)+2)), ncol=nchar(epi_consensus)+2)
colnames(result_matrix_rel) <- c("Allel", "Freq", strsplit(epi_consensus, "")[[1]])
for(allel.num in 1:length(allels)){
if(allels.count[allel.num]>patnum.threshold){
cat ("allel: ", allel.num, "\n")
current_allel <- allels[allel.num]
has_allel <- grep(current_allel, new_sequence_names)
has_allel_not <- grep(current_allel, new_sequence_names, invert = T)
result_matrix_abs[allels.counter,2] <- length(has_allel)
result_matrix_abs_not[allels.counter,2] <- length(has_allel_not)
result_matrix_abs[allels.counter,1] <- allels[allel.num]
result_matrix_rel[allels.counter,1] <- allels[allel.num]
result_matrix_abs_not[allels.counter,1] <- paste("!", allels[allel.num])
result_matrix_rel_not[allels.counter,1] <- paste("!", allels[allel.num])
# for each letter in FASTA:
col <- 3
for (j in epi_pos_start:epi_pos_end){
#for each point in sequence do the following:
# for each aa in length of possible aas:
just_this_position <- subseq(sequences, j, j)
mat <- as.matrix(just_this_position)
aa_in_consensus <- substr(epi_consensus, j, j)
subset_has_allel <- mat[has_allel]
not_consensus <- length(which(subset_has_allel != aa_in_consensus))
result_matrix_abs[allels.counter,col] <- not_consensus
result_matrix_rel[allels.counter,col] <- not_consensus/length(subset_has_allel)
subset_has_not_allel <- mat[has_allel_not]
not_consensus_not <- length(which(subset_has_not_allel != aa_in_consensus))
result_matrix_abs_not[allels.counter,col] <- not_consensus_not
result_matrix_rel_not[allels.counter,col] <- not_consensus_not/length(subset_has_not_allel)
col <- col + 1
}
allels.counter <- allels.counter + 1
}
}
if (length(which(result_matrix_abs[,1]=="0")) > 0){
result_matrix_abs <- result_matrix_abs[-(which(result_matrix_abs[,1]=="0")),]
result_matrix_rel <- result_matrix_rel[-(which(result_matrix_rel[,1]=="0")),]
result_matrix_abs_not <- result_matrix_abs_not[-(which(result_matrix_abs_not[,1]=="0")),]
result_matrix_rel_not <- result_matrix_rel_not[-(which(result_matrix_rel_not[,1]=="0")),]
}
result_matrix <- result_matrix_abs[,c(1,2)]
result_matrix_not <- result_matrix_abs_not[,c(1,2)]
coln <- c("Allels", "Freqs")
for (i in 3:ncol(result_matrix_rel)){
result_matrix <- cbind(result_matrix, cbind(result_matrix_abs[,i], result_matrix_rel[,i]))
coln <- c(coln, paste(colnames(result_matrix_abs)[i], "_abs", sep=""), paste(colnames(result_matrix_abs)[i], "_rel", sep=""))
}
colnames(result_matrix) <- coln
coln <- c("Allels", "Freqs")
for (i in 3:ncol(result_matrix_rel_not)){
result_matrix_not <- cbind(result_matrix_not, cbind(result_matrix_abs_not[,i], result_matrix_rel_not[,i]))
coln <- c(coln, paste(colnames(result_matrix_abs_not)[i], "_abs", sep=""), paste(colnames(result_matrix_abs_not)[i], "_rel", sep=""))
}
colnames(result_matrix_not) <- coln
result_matrix <- rbind(result_matrix, result_matrix_not)
grap_result <- c()
for (i in 1:nrow(result_matrix)){
pos <- 3
for (j in 1:nchar(epi_consensus) ){
if (length(grep("!", result_matrix[i,1], fixed=TRUE)) > 0){
grap_result <- rbind(grap_result, c(unlist(strsplit(result_matrix[i,1], split=' ', fixed=TRUE))[2], letter=paste(j, "_", substr(epi_consensus, j, j), sep=""), result_matrix[i,pos], kind="not allel"))
}else{
grap_result <- rbind(grap_result, c(result_matrix[i,1], letter=paste(j, "_", substr(epi_consensus, j, j), sep=""), result_matrix[i,pos], kind="allel"))
}
pos <- pos+2
}
}
grap_result <- as.data.frame(grap_result)
print (grap_result)
colnames(grap_result) <- c("allels", "letter", "value", "kind")
grap_result$value <- as.numeric(as.character(grap_result$value))
grap_result <- within(grap_result, letter <- factor(letter, levels = unique(grap_result$letter),ordered = TRUE))
plot <- ggplot(data=grap_result, aes(x=letter, y=value, fill=kind)) + geom_bar(stat="identity", position="dodge") + facet_grid(allels ~.) + xlab("") + ylab("not epitope amino acid[%]") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
ggsave(plot, file=save_png)
write.csv2(result_matrix, file=save_csv)
}
#getfreqs("TEST.fasta", #<- Deine Fasta
# "Freqs.csv",
# "Freqs.svg",
# 1, #<- Kann so bleiben
# 30, #<- Laenge des Consensus (was ja gleichlang wie dein alignment sein sollte)
# "TEST_Consensus_aa.fasta", # <- Dein Consensus
# patnum_threshold=1,
# A11 = 10, # <- HLA in Header
# A12 = 11, # <- HLA in Header
# A21 = 13, # <- HLA in Header
# A22 = 14, # <- HLA in Header
# B11 = 17, # <- HLA in Header
# B12 = 18, # <- HLA in Header
# B21 = 20, # <- HLA in Header
# B22 = 21, # <- HLA in Header
# one_feature = TRUE)
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