TravisPlot: TravisPlot
In lzcyzm/Travis_Dev: Transcriptomic View of Genomic Features
Usage
1
Arguments
peak
TravisCoordsFromTxDb
txdb
genome
noBins
saveToPDFprefix
returnCount
includeNeighborDNA
Examples
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##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (peak, TravisCoordsFromTxDb = NA, txdb = NA, genome = NA,
noBins = 10, saveToPDFprefix = NA, returnCount = FALSE, includeNeighborDNA = FALSE)
{
suppressWarnings(if (is.na(TravisCoordsFromTxDb) & is.na(txdb) &
is.na(genome)) {
stop("Most provide one of the three: TravisCoords, txdb or genome")
})
if (suppressWarnings(is.na(TravisCoordsFromTxDb))) {
if (suppressWarnings(is.na(txdb))) {
print("Downloading Transcriptome Information from UCSC ...")
txdb <- suppressMessages(makeTxDbFromUCSC(genome = genome))
print("Making Travis Coordinates ...")
TravisCoords <- suppressMessages(makeTravisCoordsFromTxDb(txdb))
}
else {
print("Making Travis Coordinates from provided TranscriptDb Object ...")
TravisCoords <- makeTravisCoordsFromTxDb(txdb, noBins = noBins)
}
}
else {
print("Using provided Travis Coordinates")
TravisCoords <- TravisCoordsFromTxDb
}
noGroup <- length(peak)
group_names <- names(peak)
m <- peak
if (is.null(group_names)) {
group_names <- paste("item", 1:noGroup)
}
for (i in 1:noGroup) {
temp = .countTravisDensity(peak[[i]], TravisCoords)
temp = cbind(temp, Feature = group_names[i])
m[[i]] = temp
}
ct = .combineListOfDataFrame(m)
ct[[4]] <- as.character(ct[[4]])
count_result <- ct
temp <- ct$category
ct <- ct[ct$count > 0, ]
ct[ct$category == "lncRNA", 2] <- ct[ct$category == "lncRNA",
2] + 6
ct <- data.frame(ct, weight = ct$count)
ct$weight <- as.numeric(ct$weight)
ct1 <- ct[ct$category == "mRNA", ]
ct2 <- ct[ct$category == "lncRNA", ]
featureSet <- as.character(unique(ct$Feature))
for (i in 1:length(featureSet)) {
id <- (ct1$Feature == featureSet[i])
ct1$weight[id] <- ct1$weight[id]/sum(ct1$weight[id])
id <- (ct2$Feature == featureSet[i])
ct2$weight[id] <- ct2$weight[id]/sum(ct2$weight[id])
}
if (includeNeighborDNA) {
p1 <- ggplot(ct1, aes(x = pos, group = Feature, weight = 5 *
weight)) + scale_x_continuous(minor_breaks = seq(0,
5, 1)) + ggtitle("Distribution on mRNA") + theme(axis.ticks = element_blank(),
axis.text.x = element_blank()) + xlab("") + ylab("Frequency") +
annotate("rect", xmin = 1, xmax = 4, ymin = -0.9,
ymax = -0.5, alpha = 0.2, fill = "red") + annotate("rect",
xmin = 0, xmax = 1, ymin = -0.9, ymax = -0.1, alpha = 0.2,
fill = "green") + annotate("rect", xmin = 1, xmax = 2,
ymin = -0.5, ymax = -0.1, alpha = 0.2, fill = "yellow") +
annotate("rect", xmin = 2, xmax = 3, ymin = -0.5,
ymax = -0.1, alpha = 0.2, fill = "orange") +
annotate("rect", xmin = 3, xmax = 4, ymin = -0.5,
ymax = -0.1, alpha = 0.2, fill = "yellow") +
annotate("rect", xmin = 4, xmax = 5, ymin = -0.9,
ymax = -0.1, alpha = 0.2, fill = "green") + geom_density(adjust = 0.5,
aes(fill = factor(Feature)), alpha = 0.2) + annotate("text",
x = 2.5, y = -0.7, label = "mRNA") + annotate("text",
x = 1.5, y = -0.3, label = "5'UTR", size = 3) + annotate("text",
x = 2.5, y = -0.3, label = "CDS", size = 3) + annotate("text",
x = 0.5, y = -0.5, label = "Promoter") + annotate("text",
x = 4.5, y = -0.5, label = "Tail") + annotate("text",
x = 3.5, y = -0.3, label = "3'UTR", size = 3)
p2 <- ggplot(ct2, aes(x = pos, group = Feature, weight = 3 *
weight)) + ggtitle("Distribution on lncRNA") + scale_x_continuous(minor_breaks = seq(6,
9, 1)) + theme(axis.ticks = element_blank(), axis.text.x = element_blank()) +
xlab("") + ylab("Frequency") + annotate("rect", xmin = 6,
xmax = 7, ymin = -0.9, ymax = -0.1, alpha = 0.2,
fill = "green") + annotate("rect", xmin = 7, xmax = 8,
ymin = -0.9, ymax = -0.1, alpha = 0.2, fill = "red") +
annotate("rect", xmin = 8, xmax = 9, ymin = -0.9,
ymax = -0.1, alpha = 0.2, fill = "green") + geom_density(adjust = 0.5,
aes(fill = factor(Feature)), alpha = 0.2) + annotate("text",
x = 7.5, y = -0.5, label = "lncRNA") + annotate("text",
x = 6.5, y = -0.5, label = "Promoter") + annotate("text",
x = 8.5, y = -0.5, label = "Tail")
}
else {
p1 <- ggplot(ct1, aes(x = pos, group = Feature, weight = 5 *
weight)) + ggtitle("Distribution on mRNA") + theme(axis.ticks = element_blank(),
axis.text.x = element_blank()) + xlab("") + ylab("Frequency") +
annotate("rect", xmin = 1, xmax = 4, ymin = -0.9,
ymax = -0.5, alpha = 0.2, fill = "red") + annotate("rect",
xmin = 1, xmax = 2, ymin = -0.5, ymax = -0.1, alpha = 0.2,
fill = "yellow") + annotate("rect", xmin = 2, xmax = 3,
ymin = -0.5, ymax = -0.1, alpha = 0.2, fill = "orange") +
annotate("rect", xmin = 3, xmax = 4, ymin = -0.5,
ymax = -0.1, alpha = 0.2, fill = "yellow") +
geom_density(adjust = 0.5, aes(fill = factor(Feature)),
alpha = 0.2) + annotate("text", x = 2.5, y = -0.7,
label = "mRNA") + annotate("text", x = 1.5, y = -0.3,
label = "5'UTR", size = 3) + annotate("text", x = 2.5,
y = -0.3, label = "CDS", size = 3) + annotate("text",
x = 3.5, y = -0.3, label = "3'UTR", size = 3) + xlim(1,
4)
p2 <- ggplot(ct2, aes(x = pos, group = Feature, weight = 3 *
weight)) + ggtitle("Distribution on lncRNA") + theme(axis.ticks = element_blank(),
axis.text.x = element_blank()) + xlab("") + ylab("Frequency") +
annotate("rect", xmin = 7, xmax = 8, ymin = -0.9,
ymax = -0.1, alpha = 0.2, fill = "red") + geom_density(adjust = 0.5,
aes(fill = factor(Feature)), alpha = 0.2) + annotate("text",
x = 7.5, y = -0.5, label = "lncRNA") + xlim(7, 8)
}
suppressWarnings(if (is.na(saveToPDFprefix)) {
print("no figure saved ...")
}
else {
f1 <- paste(saveToPDFprefix, "_Travis.pdf", sep = "")
pdf(file = f1, width = 9, height = 9)
.multiplot(p1, p2, cols = 1)
dev.off()
print(paste("Figures saved into", f1, "...", sep = " "))
})
multiplot(p1, p2, cols = 1)
if (returnCount) {
q <- list(original = ct, mRNA_normalized = ct1, ncRNA_normalized = ct2)
return(q)
}
}
lzcyzm/Travis_Dev documentation built on May 21, 2019, 9:16 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Usage
1 |
Arguments
peak |
|
TravisCoordsFromTxDb |
|
txdb |
|
genome |
|
noBins |
|
saveToPDFprefix |
|
returnCount |
|
includeNeighborDNA |
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (peak, TravisCoordsFromTxDb = NA, txdb = NA, genome = NA,
noBins = 10, saveToPDFprefix = NA, returnCount = FALSE, includeNeighborDNA = FALSE)
{
suppressWarnings(if (is.na(TravisCoordsFromTxDb) & is.na(txdb) &
is.na(genome)) {
stop("Most provide one of the three: TravisCoords, txdb or genome")
})
if (suppressWarnings(is.na(TravisCoordsFromTxDb))) {
if (suppressWarnings(is.na(txdb))) {
print("Downloading Transcriptome Information from UCSC ...")
txdb <- suppressMessages(makeTxDbFromUCSC(genome = genome))
print("Making Travis Coordinates ...")
TravisCoords <- suppressMessages(makeTravisCoordsFromTxDb(txdb))
}
else {
print("Making Travis Coordinates from provided TranscriptDb Object ...")
TravisCoords <- makeTravisCoordsFromTxDb(txdb, noBins = noBins)
}
}
else {
print("Using provided Travis Coordinates")
TravisCoords <- TravisCoordsFromTxDb
}
noGroup <- length(peak)
group_names <- names(peak)
m <- peak
if (is.null(group_names)) {
group_names <- paste("item", 1:noGroup)
}
for (i in 1:noGroup) {
temp = .countTravisDensity(peak[[i]], TravisCoords)
temp = cbind(temp, Feature = group_names[i])
m[[i]] = temp
}
ct = .combineListOfDataFrame(m)
ct[[4]] <- as.character(ct[[4]])
count_result <- ct
temp <- ct$category
ct <- ct[ct$count > 0, ]
ct[ct$category == "lncRNA", 2] <- ct[ct$category == "lncRNA",
2] + 6
ct <- data.frame(ct, weight = ct$count)
ct$weight <- as.numeric(ct$weight)
ct1 <- ct[ct$category == "mRNA", ]
ct2 <- ct[ct$category == "lncRNA", ]
featureSet <- as.character(unique(ct$Feature))
for (i in 1:length(featureSet)) {
id <- (ct1$Feature == featureSet[i])
ct1$weight[id] <- ct1$weight[id]/sum(ct1$weight[id])
id <- (ct2$Feature == featureSet[i])
ct2$weight[id] <- ct2$weight[id]/sum(ct2$weight[id])
}
if (includeNeighborDNA) {
p1 <- ggplot(ct1, aes(x = pos, group = Feature, weight = 5 *
weight)) + scale_x_continuous(minor_breaks = seq(0,
5, 1)) + ggtitle("Distribution on mRNA") + theme(axis.ticks = element_blank(),
axis.text.x = element_blank()) + xlab("") + ylab("Frequency") +
annotate("rect", xmin = 1, xmax = 4, ymin = -0.9,
ymax = -0.5, alpha = 0.2, fill = "red") + annotate("rect",
xmin = 0, xmax = 1, ymin = -0.9, ymax = -0.1, alpha = 0.2,
fill = "green") + annotate("rect", xmin = 1, xmax = 2,
ymin = -0.5, ymax = -0.1, alpha = 0.2, fill = "yellow") +
annotate("rect", xmin = 2, xmax = 3, ymin = -0.5,
ymax = -0.1, alpha = 0.2, fill = "orange") +
annotate("rect", xmin = 3, xmax = 4, ymin = -0.5,
ymax = -0.1, alpha = 0.2, fill = "yellow") +
annotate("rect", xmin = 4, xmax = 5, ymin = -0.9,
ymax = -0.1, alpha = 0.2, fill = "green") + geom_density(adjust = 0.5,
aes(fill = factor(Feature)), alpha = 0.2) + annotate("text",
x = 2.5, y = -0.7, label = "mRNA") + annotate("text",
x = 1.5, y = -0.3, label = "5'UTR", size = 3) + annotate("text",
x = 2.5, y = -0.3, label = "CDS", size = 3) + annotate("text",
x = 0.5, y = -0.5, label = "Promoter") + annotate("text",
x = 4.5, y = -0.5, label = "Tail") + annotate("text",
x = 3.5, y = -0.3, label = "3'UTR", size = 3)
p2 <- ggplot(ct2, aes(x = pos, group = Feature, weight = 3 *
weight)) + ggtitle("Distribution on lncRNA") + scale_x_continuous(minor_breaks = seq(6,
9, 1)) + theme(axis.ticks = element_blank(), axis.text.x = element_blank()) +
xlab("") + ylab("Frequency") + annotate("rect", xmin = 6,
xmax = 7, ymin = -0.9, ymax = -0.1, alpha = 0.2,
fill = "green") + annotate("rect", xmin = 7, xmax = 8,
ymin = -0.9, ymax = -0.1, alpha = 0.2, fill = "red") +
annotate("rect", xmin = 8, xmax = 9, ymin = -0.9,
ymax = -0.1, alpha = 0.2, fill = "green") + geom_density(adjust = 0.5,
aes(fill = factor(Feature)), alpha = 0.2) + annotate("text",
x = 7.5, y = -0.5, label = "lncRNA") + annotate("text",
x = 6.5, y = -0.5, label = "Promoter") + annotate("text",
x = 8.5, y = -0.5, label = "Tail")
}
else {
p1 <- ggplot(ct1, aes(x = pos, group = Feature, weight = 5 *
weight)) + ggtitle("Distribution on mRNA") + theme(axis.ticks = element_blank(),
axis.text.x = element_blank()) + xlab("") + ylab("Frequency") +
annotate("rect", xmin = 1, xmax = 4, ymin = -0.9,
ymax = -0.5, alpha = 0.2, fill = "red") + annotate("rect",
xmin = 1, xmax = 2, ymin = -0.5, ymax = -0.1, alpha = 0.2,
fill = "yellow") + annotate("rect", xmin = 2, xmax = 3,
ymin = -0.5, ymax = -0.1, alpha = 0.2, fill = "orange") +
annotate("rect", xmin = 3, xmax = 4, ymin = -0.5,
ymax = -0.1, alpha = 0.2, fill = "yellow") +
geom_density(adjust = 0.5, aes(fill = factor(Feature)),
alpha = 0.2) + annotate("text", x = 2.5, y = -0.7,
label = "mRNA") + annotate("text", x = 1.5, y = -0.3,
label = "5'UTR", size = 3) + annotate("text", x = 2.5,
y = -0.3, label = "CDS", size = 3) + annotate("text",
x = 3.5, y = -0.3, label = "3'UTR", size = 3) + xlim(1,
4)
p2 <- ggplot(ct2, aes(x = pos, group = Feature, weight = 3 *
weight)) + ggtitle("Distribution on lncRNA") + theme(axis.ticks = element_blank(),
axis.text.x = element_blank()) + xlab("") + ylab("Frequency") +
annotate("rect", xmin = 7, xmax = 8, ymin = -0.9,
ymax = -0.1, alpha = 0.2, fill = "red") + geom_density(adjust = 0.5,
aes(fill = factor(Feature)), alpha = 0.2) + annotate("text",
x = 7.5, y = -0.5, label = "lncRNA") + xlim(7, 8)
}
suppressWarnings(if (is.na(saveToPDFprefix)) {
print("no figure saved ...")
}
else {
f1 <- paste(saveToPDFprefix, "_Travis.pdf", sep = "")
pdf(file = f1, width = 9, height = 9)
.multiplot(p1, p2, cols = 1)
dev.off()
print(paste("Figures saved into", f1, "...", sep = " "))
})
multiplot(p1, p2, cols = 1)
if (returnCount) {
q <- list(original = ct, mRNA_normalized = ct1, ncRNA_normalized = ct2)
return(q)
}
}
|
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