Grob getter

Description

'Grob' class is a container for 'grob' based object defined with grid system. Generic function Grob gets grob object supported by grid system, and make an instance of subclass of class 'Grob'.

'GrobList' is a container of list of 'Grob' object.

Usage

list(list("Grob"), list("gg"))(x)
list(list("Grob"), list("gtable"))(x)
list(list("Grob"), list("trellis"))(x)
list(list("Grob"), list("lattice"))(x)
list(list("Grob"), list("GGbio"))(x)

Arguments

Value

A Grob object.

Author

Tengfei Yin

Plot class

Description

genealize a graphic object to a Plot object.

Usage

list(list("Plot"), list("gg"))(x)
  list(list("Plot"), list("trellis"))(x, mutable = FALSE)
  list(list("Plot"), list("GGbio"))(x)
  list(list("Plot"), list("Ideogram"))(x)

Arguments

Value

A Plot object.

Author

Tengfei Yin

Tracked class

Description

Create a tracked object, designed for tracks function.

Usage

Tracked(mutable = TRUE, fixed = FALSE, labeled = TRUE,
        hasAxis = FALSE, bgColor = "white", height = unit(1, "null"))

Arguments

Value

a Tracked object.

Author

Tengfei Yin

Arrange grobs by parse their legend.

Description

Arrange grobs and parse their legend, then put it together on the right.

Usage

arrangeGrobByParsingLegend(..., nrow = NULL, ncol = NULL,
                           widths = c(4, 1), legend.idx = NULL)

Arguments

Value

a

Author

Tengfei Yin

Examples

library(ggplot2)
p1 <- qplot(x = mpg, y= cyl, data = mtcars, color = carb)
p2 <- qplot(x = mpg, y= cyl, data = mtcars, color = wt)
p3 <- qplot(x = mpg, y= cyl, data = mtcars, color = qsec)
p4 <- qplot(x = mpg, y= cyl, data = mtcars, color = gear)
arrangeGrobByParsingLegend(p1, p2, p3, p4)
arrangeGrobByParsingLegend(p1, p2, p3, p4, ncol = 1)
arrangeGrobByParsingLegend(p1, p2, p3, p4, legend.idx = 2)

Generic autoplot function

Description

autoplot is a generic function to visualize various data object, it tries to give better default graphics and customized choices for each data type, quick and convenient to explore your genomic data compare to low level ggplot method, it is much simpler and easy to produce fairly complicate graphics, though you may lose some flexibility for each layer.

Usage

list(list("autoplot"), list("GRanges"))(object, ..., chr, xlab, ylab, main, truncate.gaps = FALSE,
                 truncate.fun = NULL, ratio = 0.0025, space.skip = 0.1,
                 legend = TRUE, geom = NULL, stat = NULL,
                 chr.weight = NULL,
                 coord = c("default", "genome", "truncate_gaps"),
                 layout = c("linear", "karyogram", "circle"))
list(list("autoplot"), list("GRangesList"))(object, ..., xlab, ylab, main, indName = "grl_name",
                 geom = NULL, stat = NULL, coverage.col = "gray50",
                 coverage.fill = coverage.col, group.selfish = FALSE)
list(list("autoplot"), list("IRanges"))(object, ..., xlab, ylab, main)
list(list("autoplot"), list("Seqinfo"))(object, ideogram = FALSE, ... )
list(list("autoplot"), list("GAlignments"))(object, ..., xlab, ylab, main, which,
      geom = NULL, stat = NULL)
list(list("autoplot"), list("BamFile"))(object, ..., which, xlab, ylab, main,
                 bsgenome, geom = "line", stat = "coverage", method = c("raw",
                 "estimate"), coord = c("linear", "genome"),
                 resize.extra = 10, space.skip = 0.1, show.coverage =
                 TRUE)
list(list("autoplot"), list("character"))(object, ..., xlab, ylab, main, which)
list(list("autoplot"), list("TxDbOREnsDb"))(object, which, ..., xlab, ylab, main, truncate.gaps =
                 FALSE, truncate.fun = NULL, ratio = 0.0025,
                 mode = c("full", "reduce"),geom =
                 c("alignment"), stat = c("identity", "reduce"),
                 names.expr = "tx_name", label = TRUE)
list(list("autoplot"), list("BSgenome"))(object, which, ...,
                    xlab, ylab, main, geom = NULL)
list(list("autoplot"), list("Rle"))(object, ..., xlab, ylab, main, binwidth, nbin = 30,
          geom = NULL, stat = c("bin", "identity", "slice"),
          type = c("viewSums", "viewMins", "viewMaxs", "viewMeans"))
list(list("autoplot"), list("RleList"))(object, ..., xlab, ylab, main, nbin = 30, binwidth,
         facetByRow = TRUE, stat = c("bin", "identity", "slice"),
         geom = NULL, type = c("viewSums", "viewMins", "viewMaxs", "viewMeans"))
list(list("autoplot"), list("matrix"))(object, ..., xlab, ylab, main,
              geom = c("tile", "raster"), axis.text.angle = NULL,
              hjust = 0.5, na.value = NULL,
              rownames.label = TRUE, colnames.label = TRUE,
              axis.text.x = TRUE, axis.text.y = TRUE)
list(list("autoplot"), list("ExpressionSet"))(object, ..., type = c("heatmap", "none",
                 "scatterplot.matrix", "pcp", "MA", "boxplot",
                 "mean-sd"), test.method =
                 "t", rotate = FALSE, pheno.plot = FALSE, main_to_pheno
                 = 4.5, padding = 0.2)
list(list("autoplot"), list("RangedSummarizedExperiment"))(object, ..., type = c("heatmap", "link", "pcp", "boxplot", "scatterplot.matrix"), pheno.plot = FALSE,
                 main_to_pheno = 4.5, padding = 0.2, assay.id = 1)
list(list("autoplot"), list("VCF"))(object, ...,
              xlab, ylab, main,
              assay.id,
              type = c("default", "geno", "info", "fixed"),
              full.string = FALSE,
              ref.show = TRUE,
              genome.axis = TRUE,
              transpose = TRUE)
list(list("autoplot"), list("OrganismDb"))(object, which, ...,
                   xlab, ylab, main,
                   truncate.gaps = FALSE,
                   truncate.fun = NULL,
                   ratio = 0.0025,
                   geom = c("alignment"),
                   stat = c("identity", "reduce"),
                   columns = c("TXNAME", "SYMBOL", "TXID", "GENEID"),
                   names.expr = "SYMBOL",
                   label = TRUE,
                   label.color = "gray40")
list(list("autoplot"), list("VRanges"))(object, ...,which = NULL,
                             arrow = TRUE, indel.col = "gray30",
                             geom = NULL,
                             xlab, ylab, main)
list(list("autoplot"), list("TabixFile"))(object, which, ...)

Arguments

Value

A ggplot object, so you can use common features from ggplot2 package to manipulate the plot.

Author

Tengfei Yin

Examples

set.seed(1)
N <- 1000
library(GenomicRanges)
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))

idx <- sample(1:length(gr), size = 50)


###################################################
### code chunk number 3: default
###################################################
autoplot(gr[idx])


###################################################
### code chunk number 4: bar-default-pre
###################################################
set.seed(123)
gr.b <- GRanges(seqnames = "chr1", IRanges(start = seq(1, 100, by = 10),
width = sample(4:9, size = 10, replace = TRUE)),
score = rnorm(10, 10, 3), value = runif(10, 1, 100))
gr.b2 <- GRanges(seqnames = "chr2", IRanges(start = seq(1, 100, by = 10),
width = sample(4:9, size = 10, replace = TRUE)),
score = rnorm(10, 10, 3), value = runif(10, 1, 100))
gr.b <- c(gr.b, gr.b2)
head(gr.b)


###################################################
### code chunk number 5: bar-default
###################################################
p1 <- autoplot(gr.b, geom = "bar")
## use value to fill the bar
p2 <- autoplot(gr.b, geom = "bar", aes(fill = value))
tracks(default = p1, fill = p2)


###################################################
### code chunk number 6: autoplot.Rnw:236-237
###################################################
autoplot(gr[idx], geom = "arch", aes(color = value), facets = sample ~ seqnames)


###################################################
### code chunk number 7: gr-group
###################################################
gra <- GRanges("chr1", IRanges(c(1,7,20), end = c(4,9,30)), group = c("a", "a", "b"))
## if you desn't specify group, then group based on stepping levels, and gaps are computed without
## considering extra group method
p1 <- autoplot(gra, aes(fill = group), geom = "alignment")
## when use group method, gaps only computed for grouped intervals.
## default is group.selfish = TRUE, each group keep one row.
## in this way, group labels could be shown as y axis.
p2 <- autoplot(gra, aes(fill = group, group = group), geom = "alignment")
## group.selfish = FALSE, save space
p3 <- autoplot(gra, aes(fill = group, group = group), geom = "alignment", group.selfish = FALSE)
tracks('non-group' = p1,'group.selfish = TRUE' = p2 , 'group.selfish = FALSE' = p3)


###################################################
### code chunk number 8: gr-facet-strand
###################################################
autoplot(gr, stat = "coverage", geom = "area",
facets = strand ~ seqnames, aes(fill = strand))


###################################################
### code chunk number 9: gr-autoplot-circle
###################################################
autoplot(gr[idx], layout = 'circle')


###################################################
### code chunk number 10: gr-circle
###################################################
seqlengths(gr) <- c(400, 500, 700)
values(gr)$to.gr <- gr[sample(1:length(gr), size = length(gr))]
idx <- sample(1:length(gr), size = 50)
gr <- gr[idx]
ggplot() + layout_circle(gr, geom = "ideo", fill = "gray70", radius = 7, trackWidth = 3) +
layout_circle(gr, geom = "bar", radius = 10, trackWidth = 4,
aes(fill = score, y = score)) +
layout_circle(gr, geom = "point", color = "red", radius = 14,
trackWidth = 3, grid = TRUE, aes(y = score)) +
layout_circle(gr, geom = "link", linked.to = "to.gr", radius = 6, trackWidth = 1)


###################################################
### code chunk number 11: seqinfo-src
###################################################
data(hg19Ideogram, package = "biovizBase")
sq <- seqinfo(hg19Ideogram)
sq


###################################################
### code chunk number 12: seqinfo
###################################################
autoplot(sq[paste0("chr", c(1:22, "X"))])


###################################################
### code chunk number 13: ir-load
###################################################
set.seed(1)
N <- 100
ir <-  IRanges(start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE))
## add meta data
df <- DataFrame(value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))
values(ir) <- df
ir


###################################################
### code chunk number 14: ir-exp
###################################################
p1 <- autoplot(ir)
p2 <- autoplot(ir, aes(fill = pair)) + theme(legend.position = "none")
p3 <- autoplot(ir, stat = "coverage", geom = "line", facets = sample ~. )
p4 <- autoplot(ir, stat = "reduce")
tracks(p1, p2, p3, p4)


###################################################
### code chunk number 15: grl-simul
###################################################
set.seed(1)
N <- 100
## ======================================================================
##  simmulated GRanges
## ======================================================================
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(30:40, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))


grl <- split(gr, values(gr)$pair)


###################################################
### code chunk number 16: grl-exp
###################################################
## default gap.geom is 'chevron'
p1 <- autoplot(grl, group.selfish = TRUE)
p2 <- autoplot(grl, group.selfish = TRUE, main.geom = "arrowrect", gap.geom = "segment")
tracks(p1, p2)


###################################################
### code chunk number 17: grl-name
###################################################
autoplot(grl, aes(fill = ..grl_name..))
## equal to
## autoplot(grl, aes(fill = grl_name))


###################################################
### code chunk number 18: rle-simul
###################################################
library(IRanges)
set.seed(1)
lambda <- c(rep(0.001, 4500), seq(0.001, 10, length = 500),
seq(10, 0.001, length = 500))

## @knitr create
xVector <- rpois(1e4, lambda)
xRle <- Rle(xVector)
xRle


###################################################
### code chunk number 19: rle-bin
###################################################
p1 <- autoplot(xRle)
p2 <- autoplot(xRle, nbin = 80)
p3 <- autoplot(xRle, geom = "heatmap", nbin = 200)
tracks('nbin = 30' = p1, "nbin = 80" = p2, "nbin = 200(heatmap)" = p3)


###################################################
### code chunk number 20: rle-id
###################################################
p1 <- autoplot(xRle, stat = "identity")
p2 <- autoplot(xRle, stat = "identity", geom = "point", color = "red")
tracks('line' = p1, "point" = p2)


###################################################
### code chunk number 21: rle-slice
###################################################
p1 <- autoplot(xRle, type = "viewMaxs", stat = "slice", lower = 5)
p2 <- autoplot(xRle, type = "viewMaxs", stat = "slice", lower = 5, geom = "heatmap")
tracks('bar' = p1, "heatmap" = p2)


###################################################
### code chunk number 22: rlel-simul
###################################################
xRleList <- RleList(xRle, 2L * xRle)
xRleList


###################################################
### code chunk number 23: rlel-bin
###################################################
p1 <- autoplot(xRleList)
p2 <- autoplot(xRleList, nbin = 80)
p3 <- autoplot(xRleList, geom = "heatmap", nbin = 200)
tracks('nbin = 30' = p1, "nbin = 80" = p2, "nbin = 200(heatmap)" = p3)


###################################################
### code chunk number 24: rlel-id
###################################################
p1 <- autoplot(xRleList, stat = "identity")
p2 <- autoplot(xRleList, stat = "identity", geom = "point", color = "red")
tracks('line' = p1, "point" = p2)


###################################################
### code chunk number 25: rlel-slice
###################################################
p1 <- autoplot(xRleList, type = "viewMaxs", stat = "slice", lower = 5)
p2 <- autoplot(xRleList, type = "viewMaxs", stat = "slice", lower = 5, geom = "heatmap")
tracks('bar' = p1, "heatmap" = p2)


###################################################
### code chunk number 26: txdb
###################################################
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
data(genesymbol, package = "biovizBase")
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene


###################################################
### code chunk number 27: txdb-visual
###################################################
p1 <- autoplot(txdb, which = genesymbol["ALDOA"], names.expr = "tx_name:::gene_id")
p2 <- autoplot(txdb, which = genesymbol["ALDOA"], stat = "reduce", color = "brown",
fill = "brown")
tracks(full = p1, reduce = p2, heights = c(5, 1)) + ylab("")


###################################################
### EnsDb
###################################################
## Fetching gene models from an EnsDb object.
library(EnsDb.Hsapiens.v75)
ensdb <- EnsDb.Hsapiens.v75
## We use a GenenameFilter to specifically retrieve all transcripts for that gene.
p1 <- autoplot(ensdb, which = GenenameFilter("ALDOA"), names.expr = "gene_name")
## Instead of providing the GenenameFilter, we can also use filter expressions
p2 <- autoplot(ensdb, which = ~ genename == "ALDOA", stat = "reduce",
color = "brown", fill = "brown")
tracks(full = p1, reduce = p2, heights = c(5, 1)) + ylab("")

## Alternatively, we can specify a GRangesFilter and display all genes
## that are (partially) overlapping with that genomic region:
gr <- GRanges(seqnames=16, IRanges(30768000, 30770000), strand="+")
autoplot(ensdb, GRangesFilter(gr, "any"), names.expr="gene_name")
## Just submitting the GRanges object also works.
autoplot(ensdb, gr, names.expr="gene_name")

## Or genes encoded on both strands.
gr <- GRanges(seqnames = 16, IRanges(30768000, 30770000), strand = "*")
autoplot(ensdb, GRangesFilter(gr), names.expr="gene_name")

## Also, we can spefify directly the gene ids and plot all transcripts of these
## genes (not only those overlapping with the region)
autoplot(ensdb, GeneIdFilter(c("ENSG00000196118", "ENSG00000156873")))

###################################################
### code chunk number 28: ga-load
###################################################
library(GenomicAlignments)
data("genesymbol", package = "biovizBase")
bamfile <- system.file("extdata", "SRR027894subRBM17.bam",
package="biovizBase")
which <- keepStandardChromosomes(genesymbol["RBM17"])
## need to set use.names = TRUE
ga <- readGAlignments(bamfile,
param = ScanBamParam(which = which),
use.names = TRUE)


###################################################
### code chunk number 29: ga-exp
###################################################
p1 <- autoplot(ga)
p2 <- autoplot(ga, geom = "rect")
p3 <- autoplot(ga, geom = "line", stat = "coverage")
tracks(default = p1, rect = p2, coverage = p3)


###################################################
### code chunk number 30: bf-load (eval = FALSE)
###################################################
## library(Rsamtools)
## bamfile <- "./wgEncodeCaltechRnaSeqK562R1x75dAlignsRep1V2.bam"
## bf <- BamFile(bamfile)


###################################################
### code chunk number 31: bf-est-cov (eval = FALSE)
###################################################
## autoplot(bamfile)
## autoplot(bamfile, which = c("chr1", "chr2"))
## autoplot(bf)
## autoplot(bf, which = c("chr1", "chr2"))
##
## data(genesymbol, package = "biovizBase")
## autoplot(bamfile,  method = "raw", which = genesymbol["ALDOA"])
##
## library(BSgenome.Hsapiens.UCSC.hg19)
## autoplot(bf, stat = "mismatch", which = genesymbol["ALDOA"], bsgenome = Hsapiens)


###################################################
### code chunk number 32: char-bam (eval = FALSE)
###################################################
## bamfile <- "./wgEncodeCaltechRnaSeqK562R1x75dAlignsRep1V2.bam"
## autoplot(bamfile)


###################################################
### code chunk number 33: char-gr
###################################################
library(rtracklayer)
test_path <- system.file("tests", package = "rtracklayer")
test_bed <- file.path(test_path, "test.bed")
autoplot(test_bed, aes(fill = name))


###################################################
###  matrix
###################################################
volcano <- volcano[20:70, 20:60] - 150
autoplot(volcano)
autoplot(volcano, xlab = "xlab", main = "main", ylab = "ylab")
## special scale theme for 0-centered values
autoplot(volcano, geom = "raster")+scale_fill_fold_change()

## when a matrix has colnames and rownames label them by default
colnames(volcano) <- sort(sample(1:300, size = ncol(volcano), replace = FALSE))
autoplot(volcano)+scale_fill_fold_change()

rownames(volcano) <- letters[sample(1:24, size = nrow(volcano), replace = TRUE)]
autoplot(volcano)

## even with row/col names, you could also disable it and just use numeric index
autoplot(volcano, colnames.label = FALSE)
autoplot(volcano, rownames.label = FALSE, colnames.label = FALSE)

## don't want the axis has label??
autoplot(volcano, axis.text.x = FALSE)
autoplot(volcano, axis.text.y = FALSE)


# or totally remove axis
colnames(volcano) <- lapply(letters[sample(1:24, size = ncol(volcano),
replace = TRUE)],
function(x){
paste(rep(x, 7), collapse = "")
})

## Oops, overlapped
autoplot(volcano)
## tweak with it.
autoplot(volcano, axis.text.angle =  -45, hjust = 0)

## when character is the value
x <- sample(c(letters[1:3], NA), size = 100, replace = TRUE)
mx <- matrix(x, nrow = 5)
autoplot(mx)
## tile gives you a white margin
rownames(mx) <- LETTERS[1:5]
autoplot(mx, color = "white")
colnames(mx) <- LETTERS[1:20]
autoplot(mx, color = "white")
autoplot(mx, color = "white", size = 2)
## weird in aes(), though works
## default tile is flexible
autoplot(mx, aes(width = 0.6, height = 0.6))
autoplot(mx, aes(width = 0.6, height = 0.6), na.value = "white")
autoplot(mx,  aes(width = 0.6, height = 0.6)) + theme_clear()

###################################################
### Views
###################################################
lambda <- c(rep(0.001, 4500), seq(0.001, 10, length = 500),
seq(10, 0.001, length = 500))
xVector <- dnorm(1:5e3, mean = 1e3, sd = 200)
xRle <- Rle(xVector)
v1 <- Views(xRle, start = sample(.4e3:.6e3, size = 50, replace = FALSE), width =1000)
autoplot(v1)
names(v1) <- letters[sample(1:24, size = length(v1), replace = TRUE)]
autoplot(v1)
autoplot(v1, geom = "tile", aes(width = 0.5, height = 0.5))
autoplot(v1, geom = "line")
autoplot(v1, geom = "line", aes(color = row)) + theme(legend.position = "none")
autoplot(v1, geom = "line", facets = NULL)
autoplot(v1, geom = "line", facets = NULL, alpha  = 0.1)


###################################################
### ExpressionSet
###################################################
library(Biobase)
data(sample.ExpressionSet)
sample.ExpressionSet
set.seed(1)
## select 50 features
idx <- sample(seq_len(dim(sample.ExpressionSet)[1]), size = 50)
eset <- sample.ExpressionSet[idx,]
eset
autoplot(as.matrix(pData(eset)))

## default heatmap
p1 <- autoplot(eset)
p2 <- p1 + scale_fill_fold_change()
p2
autoplot(eset)
autoplot(eset, geom = "tile", color = "white", size = 2)
autoplot(eset, geom = "tile", aes(width = 0.6, height = 0.6))

autoplot(eset, pheno.plot = TRUE)
idx <- order(pData(eset)[,1])
eset2 <- eset[,idx]
autoplot(eset2, pheno.plot = TRUE)

## parallel coordainte plot
autoplot(eset, type = "pcp")

## boxplot
autoplot(eset, type = "boxplot")


## scatterplot.matrix
## slow, be carefull
##autoplot(eset[, 1:7], type = "scatterplot.matrix")

## mean-sd
autoplot(eset, type = "mean-sd")




###################################################
### RangedSummarizedExperiment
###################################################
library(SummarizedExperiment)
nrows <- 200; ncols <- 6
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
counts2 <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
IRanges(floor(runif(200, 1e5, 1e6)), width=100),
strand=sample(c("+", "-"), 200, TRUE))
colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
row.names=LETTERS[1:6])
sset <- SummarizedExperiment(assays=SimpleList(counts=counts,
counts2 = counts2),
rowRanges=rowRanges, colData=colData)
autoplot(sset) + scale_fill_fold_change()
autoplot(sset, pheno.plot = TRUE)


###################################################
### pcp
###################################################
autoplot(sset, type = "pcp")


###################################################
### boxplot
###################################################
autoplot(sset, type = "boxplot")


###################################################
### scatterplot matrix
###################################################
##autoplot(sset, type = "scatterplot.matrix")


###################################################
### vcf
###################################################

library(VariantAnnotation)
vcffile <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")
vcf <- readVcf(vcffile, "hg19")
## default use type 'geno'
## default use genome position
autoplot(vcf)
## or disable it
autoplot(vcf, genome.axis = FALSE)
## not transpose
autoplot(vcf, genome.axis = FALSE, transpose = FALSE, rownames.label = FALSE)
autoplot(vcf)
## use
autoplot(vcf, assay.id = "DS")
## equivalent to
autoplot(vcf, assay.id = 2)
## doesn't work when assay.id cannot find
autoplot(vcf, assay.id = "NO")
## use AF or first
autoplot(vcf, type = "info")
## geom bar
autoplot(vcf, type = "info", aes(y  = THETA))
autoplot(vcf, type = "info", aes(y  = THETA, fill = VT, color = VT))
autoplot(vcf, type = "fixed")
autoplot(vcf, type = "fixed", size = 10) + xlim(c(50310860, 50310890)) + ylim(0.75, 1.25)

p1 <- autoplot(vcf, type = "fixed") + xlim(50310860, 50310890)
p2 <- autoplot(vcf, type = "fixed", full.string = TRUE) + xlim(50310860, 50310890)
tracks("full.string = FALSE" = p1, "full.string = TRUE" = p2)+
scale_y_continuous(breaks = NULL, limits = c(0, 3))
p3 <- autoplot(vcf, type = "fixed", ref.show = FALSE) + xlim(50310860, 50310890) +
scale_y_continuous(breaks = NULL, limits = c(0, 2))
p3


###################################################
### code chunk number 56: bs-v
###################################################
library(BSgenome.Hsapiens.UCSC.hg19)
data(genesymbol, package = "biovizBase")
p1 <- autoplot(Hsapiens, which = resize(genesymbol["ALDOA"], width = 50))
p2 <- autoplot(Hsapiens, which = resize(genesymbol["ALDOA"], width = 50), geom = "rect")
tracks(text = p1, rect = p2)


###################################################
### code chunk number 57: sessionInfo
###################################################
sessionInfo()

Alignment geoms for GRanges object

Description

Show interval data as alignment.

Usage

% for GRangeslist(list("geom_alignment"), list("GRanges"))(data, ..., xlab, ylab, main, facets = NULL, stat =
                 c("stepping", "identity"), range.geom = c("rect",
                 "arrowrect"), gap.geom = c("chevron", "arrow",
                 "segment"), rect.height = NULL, group.selfish = TRUE,
                  label = TRUE)
list(list("geom_alignment"), list("TxDbOREnsDb"))(data, ..., which, columns = c("tx_id", "tx_name",
                 "gene_id"), names.expr = "tx_name", facets = NULL,
                 truncate.gaps = FALSE, truncate.fun = NULL, ratio =
                 0.0025)
list(list("geom_alignment"), list("GRangesList"))(data, ..., which = NULL,
                          cds.rect.h = 0.25,
                          exon.rect.h = cds.rect.h,
                          utr.rect.h = cds.rect.h/2,
                          xlab, ylab, main,
                          facets = NULL, geom = "alignment",
                          stat = c("identity", "reduce"),
                          range.geom = "rect",
                          gap.geom = "arrow",
                          utr.geom = "rect",
                          names.expr = NULL,
                          label = TRUE,
                          label.color = "gray40",
                          arrow.rate = 0.015,
                          length = unit(0.1, "cm"))
list(list("geom_alignment"), list("OrganismDb"))(data, ..., which,
                   columns = c("TXNAME", "SYMBOL", "TXID", "GENEID"),
                   names.expr = "SYMBOL",
                   facets = NULL,
                   truncate.gaps = FALSE,
                   truncate.fun = NULL, ratio = 0.0025
                   )

Arguments

Value

A 'Layer'.

Author

Tengfei Yin

Examples

set.seed(1)
N <- 100
require(GenomicRanges)
## ======================================================================
##  simmulated GRanges
## ======================================================================
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))


## ======================================================================
##  default
## ======================================================================
ggplot(gr) + geom_alignment()
## or
ggplot() + geom_alignment(gr)

## ======================================================================
##  facetting and aesthetics
## ======================================================================
ggplot(gr) + geom_alignment(facets = sample ~ seqnames, aes(color = strand, fill = strand))

## ======================================================================
##  stat:stepping
## ======================================================================
ggplot(gr) + geom_alignment(stat = "stepping", aes(group = pair))

## ======================================================================
##  group.selfish controls when
## ======================================================================
ggplot(gr) + geom_alignment(stat = "stepping", aes(group = pair), group.selfish = FALSE)

## =======================================
##  main/gap geom
## =======================================
ggplot(gr) + geom_alignment(range.geom = "arrowrect", gap.geom = "chevron")

## =======================================
##  For TxDb
## =======================================
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
data(genesymbol, package = "biovizBase")
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
## made a track comparing full/reduce stat.
ggbio() + geom_alignment(data = txdb, which = genesymbol["RBM17"])
p1 <- ggplot(txdb) + geom_alignment(which = genesymbol["RBM17"])
p1
p2 <- ggplot(txdb) + geom_alignment(which = genesymbol["RBM17"], stat = "reduce")
tracks(full = p1, reduce = p2, heights = c(3, 1))
tracks(full = p1, reduce = p2, heights = c(3, 1)) + theme_tracks_sunset()
tracks(full = p1, reduce = p2, heights = c(3, 1)) +
theme_tracks_sunset(axis.line.color = NA)
## change y labels
ggplot(txdb) + geom_alignment(which = genesymbol["RBM17"], names.expr = "tx_id:::gene_id")

Arch geoms for GRanges object

Description

Show interval data as arches.

Usage

% for data.framelist(list("geom_arch"), list("data.frame"))(data, ..., n = 25, max.height = 10)
% for GRangeslist(list("geom_arch"), list("GRanges"))(data, ..., xlab, ylab, main, facets = NULL,
                 rect.height = 0, n = 25, max.height = 10)

Arguments

• x start of the arches

• xend end of the arches

• height height of arches
  |xlab | Label for x | |ylab | Label for y | |main | Title for plot. | |n | Integer values at which interpolation takes place to create 'n' equally spaced points spanning the interval ['min(x)', 'max(x)']. | |facets | Faceting formula to use. | |rect.height | When data is GRanges , this padding the arches from original y value to allow users putting arches 'around' the interval rectangles. | |max.height | Max height of all arches. |

Details

To draw a interval data as arches, we need to provide a special geom for this purpose. Arches is popular in gene viewer or genomoe browser, when they try to show isoforms or gene model. geom_arch , just like any other geom_* function in ggplot2, you can pass aes() to it to map variable to height of arches.

Value

A 'Layer'.

Author

Tengfei Yin

Examples

set.seed(1)
N <- 100
library(GenomicRanges)

## =======================================
##  simmulated GRanges
## =======================================
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))

## =======================================
##  default
## =======================================
ggplot(gr) + geom_arch()
# or
ggplot() + geom_arch(gr)

## =======================================
##  facetting and aesthetics
## =======================================
ggplot(gr) + geom_arch(aes(color = value, height = value, size = value),
alpha = 0.2, facets = sample ~ seqnames)

Arrow geoms for GRanges object

Description

Show interval data as arrows.

Usage

list(list("geom_arrow"), list("GRanges"))(data, ..., xlab, ylab, main,
          angle = 30, length = unit(0.12, "cm"), type = "open",
          stat = c("stepping", "identity"), facets = NULL,
          arrow.rate = 0.03, group.selfish = TRUE)

Arguments

Value

A 'Layer'.

Author

Tengfei Yin

Examples

set.seed(1)
N <- 100
require(GenomicRanges)
## ======================================================================
##  simmulated GRanges
## ======================================================================
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))


## ======================================================================
##  default
## ======================================================================
ggplot(gr) + geom_arrow()
# or
ggplot() + geom_arrow(gr)

## ======================================================================
##  facetting and aesthetics
## ======================================================================
ggplot(gr) + geom_arrow(facets = sample ~ seqnames, aes(color = strand, fill = strand))

## ======================================================================
##  stat:identity
## ======================================================================
ggplot(gr) + geom_arrow(stat = "identity", aes(y = value))


## ======================================================================
##  stat:stepping
## ======================================================================
ggplot(gr) + geom_arrow(stat = "stepping", aes(y = value, group = pair))

## ======================================================================
##  group.selfish
## ======================================================================
ggplot(gr) + geom_arrow(stat = "stepping", aes(y = value, group = pair), group.selfish = FALSE)



## ======================================================================
## other options to control arrow angle, density, ...
## ======================================================================
library(grid)
ggplot(gr) + geom_arrow(stat = "stepping", aes(y = value, group = pair),
arrow.rate = 0.01, legnth = unit(0.3, "cm"), agnle = 45,
group.selfish = FALSE)

Arrowrect geoms for GRanges object

Description

Show interval data as rectangle with a arrow head.

Usage

list(list("geom_arrowrect"), list("GRanges"))(data, ..., xlab, ylab, main,
              facets = NULL, stat = c("stepping", "identity"),
              rect.height = NULL, arrow.head = 0.06,
              arrow.head.rate = arrow.head,  arrow.head.fix = NULL,
              group.selfish = TRUE)

Arguments

Value

A 'Layer'.

Author

Tengfei Yin

Examples

set.seed(1)
N <- 100
require(GenomicRanges)
## ======================================================================
##  simmulated GRanges
## ======================================================================
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))


## ======================================================================
##  default
## ======================================================================
ggplot(gr) + geom_arrowrect()
## or
ggplot() + geom_arrowrect(gr)

## ======================================================================
##  facetting and aesthetics
## ======================================================================
ggplot(gr) + geom_arrowrect(facets = sample ~ seqnames, aes(color = strand, fill = strand))


## ======================================================================
##  stat:identity
## ======================================================================
ggplot(gr) + geom_arrowrect(stat = "identity", aes(y = value))


## ======================================================================
##  stat:stepping
## ======================================================================
ggplot(gr) + geom_arrowrect(stat = "stepping", aes(y = value, group = pair))


## ======================================================================
##  group.selfish controls when
## ======================================================================
ggplot(gr) + geom_arrowrect(gr, stat = "stepping", aes(y = value, group = pair), group.selfish = FALSE)

Segment geoms for GRanges object

Description

Show interval data as vertical bar, width equals to interval width and use 'score' or specified 'y' as y scale.

Usage

list(list("geom_bar"), list("ANY"))(data, ...)
list(list("geom_bar"), list("GRanges"))(data,..., xlab, ylab, main)

Arguments

Details

Useful for showing bed like files, when imported as GRanges, have a extra 'score' column, use it as default y, you could also specify y by using aes(y = ).

Value

A 'Layer'.

Examples

## load
library(GenomicRanges)

## simul
set.seed(123)
gr.b <- GRanges(seqnames = "chr1", IRanges(start = seq(1, 100, by = 10),
width = sample(4:9, size = 10, replace = TRUE)),
score = rnorm(10, 10, 3), value = runif(10, 1, 100))
gr.b2 <- GRanges(seqnames = "chr2", IRanges(start = seq(1, 100, by = 10),
width = sample(4:9, size = 10, replace = TRUE)),
score = rnorm(10, 10, 3), value = runif(10, 1, 100))
gr.b <- c(gr.b, gr.b2)
## default use score as y

## bar
ggplot(gr.b) + geom_bar(aes(fill = value))
## or
ggplot() + geom_bar(gr.b, aes(fill = value))
ggplot(gr.b) + geom_bar(aes(y = value))
## equal to
autoplot(gr.b, geom = "bar")

Chevron geoms for GRanges object

Description

Break normal intervals stroed in GRanges object and show them as chevron, useful for showing model or splice summary.

Usage

list(list("geom_chevron"), list("GRanges"))(data, ..., xlab, ylab, main,
             offset = 0.1,
             facets = NULL,
             stat = c("stepping", "identity"),
             chevron.height.rescale = c(0.1, 0.8),
             group.selfish = TRUE)

Arguments

Details

To draw a normal GRanges as Chevron, we need to provide a special geom for this purpose. Chevron is popular in gene viewer or genomoe browser, when they try to show isoforms or gene model. geom_chevron , just like any other geom_* function in ggplot2, you can pass aes() to it to use height of chevron or width of chevron to show statistics summary.

Value

A 'Layer'.

Author

Tengfei Yin

Examples

set.seed(1)
N <- 100
require(GenomicRanges)

## ======================================================================
##  simmulated GRanges
## ======================================================================
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))



## ======================================================================
##  default
##
##  ======================================================================
ggplot(gr) + geom_chevron()
## or
ggplot() + geom_chevron(gr)


## ======================================================================
##  facetting and aesthetics
## ======================================================================
ggplot(gr) + geom_chevron(facets = sample ~ seqnames, aes(color = strand))


## ======================================================================
##  stat:identity
## ======================================================================
ggplot(gr) + geom_chevron(stat = "identity", aes(y = value))


## ======================================================================
##  stat:stepping
## ======================================================================
ggplot(gr) + geom_chevron(stat = "stepping", aes(group = pair))


## ======================================================================
##  group.selfish controls when
## ======================================================================
ggplot(gr) + geom_chevron(stat = "stepping", aes(group = pair), group.selfish = FALSE,
xlab = "xlab", ylab = "ylab", main = "main")

p <- qplot(x = mpg, y = cyl, data = mtcars)

## ======================================================================
##  offset
## ======================================================================
gr2 <- GRanges("chr1", IRanges(c(1, 10, 20), width = 5))
gr2.p <- gaps(gr2)
## resize to connect them
gr2.p <- resize(gr2.p, fix = "center", width = width(gr2.p)+2)

ggplot(gr2) + geom_rect() + geom_chevron(gr2.p)


## notice the rectangle height is 0.8
## offset = 0 just like a line
ggplot(gr2) + geom_rect() + geom_chevron(gr2.p, offset = 0)


## equal height
ggplot(gr2) + geom_rect() + geom_chevron(gr2.p, offset = 0.4)


## ======================================================================
##  chevron.height
## ======================================================================
values(gr2.p)$score <- c(100, 200)
ggplot(gr2) + geom_rect() + geom_chevron(gr2.p, offset = "score")
## chevron.height
ggplot(gr2) + geom_rect() + geom_chevron(gr2.p, offset = "score",
chevron.height.rescale = c(0.4, 10))

Rect geoms for GRanges object

Description

Show interval data as rectangle.

Usage

list(list("geom_rect"), list("ANY"))(data, ...)
list(list("geom_rect"), list("GRanges"))(data,..., xlab, ylab, main,
          facets = NULL, stat = c("stepping", "identity"),
          rect.height = NULL,
          group.selfish = TRUE)

Arguments

Value

A 'Layer'.

Author

Tengfei Yin

Examples

set.seed(1)
N <- 100
require(GenomicRanges)

## ======================================================================
##  simmulated GRanges
## ======================================================================
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))



## ======================================================================
##  data.frame call ggplot2::geom_rect
## ======================================================================
ggplot() + geom_rect(data = mtcars, aes(xmin = mpg, ymin = wt, xmax = mpg + 10, ymax = wt + 0.2,
fill = cyl))



## ======================================================================
##  default
## ======================================================================
ggplot(gr) + geom_rect()
# or
ggplot() + geom_rect(gr)


## ======================================================================
##  facetting and aesthetics
## ======================================================================
ggplot(gr) + geom_rect(facets = sample ~ seqnames, aes(color = strand, fill = strand))


## ======================================================================
##  stat:identity
## ======================================================================
ggplot(gr) + geom_rect(stat = "identity", aes(y = value))


## ======================================================================
##  stat:stepping
## ======================================================================
ggplot(gr) + geom_rect(stat = "stepping", aes(y = value, group = pair))


## ======================================================================
##  group.selfish controls when
## ======================================================================
ggplot(gr) + geom_rect(stat = "stepping", aes(y = value, group = pair), group.selfish = FALSE)

Segment geoms for GRanges object

Description

Show interval data as segments.

Usage

list(list("geom_segment"), list("ANY"))(data, ...)
% for GRangeslist(list("geom_segment"), list("GRanges"))(data,..., xlab, ylab, main,
          facets = NULL, stat = c("stepping", "identity"),
          group.selfish = TRUE)

Arguments

Value

A 'Layer'.

Author

Tengfei Yin

Examples

set.seed(1)
N <- 100
require(GenomicRanges)

## ======================================================================
##  simmulated GRanges
## ======================================================================
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))


## ======================================================================
##  data.frame call ggplot2::geom_segment
## ======================================================================
ggplot() + geom_segment(data = mtcars, aes(x = mpg, y = wt, xend = mpg + 10, yend = wt + 0.2,
fill = cyl))



## ======================================================================
##  default
##
##  ======================================================================
ggplot(gr) + geom_segment()
## or
ggplot() + geom_segment(gr)


## ======================================================================
##  facetting and aesthetics
## ======================================================================
ggplot(gr) + geom_segment(facets = sample ~ seqnames, aes(color = strand, fill = strand))


## ======================================================================
##  stat:identity
## ======================================================================
ggplot(gr) + geom_segment(stat = "identity", aes(y = value))


## ======================================================================
##  stat:stepping
## ======================================================================
ggplot(gr) + geom_segment(stat = "stepping", aes(y = value, group = pair))


## ======================================================================
##  group.selfish controls when
## ======================================================================
ggplot(gr) + geom_segment(stat = "stepping", aes(y = value, group = pair), group.selfish = FALSE)

class ggbio

Description

a sub class of ggplot and gg class defined in ggplot2 package, used for ggbio specific methods.

Usage

GGbio(ggplot = NULL, data = NULL, fetchable = FALSE, blank =
                 FALSE, ...)

Arguments

Details

This class is defined to facilitate the ggbio-specific visualization method, especially when using ggplot to construct ggbio supported object, that will return a ggbio class. And internals tricks will help a lazy evaluation for following + method.

Value

a ggbio object.

Seealso

ggplot

Author

Tengfei Yin

Examples

p1 <- qplot()
g1 <- ggbio(p1)
class(g1)

ggplot methods

Description

These methods extend ggplot to support several types of Bioconductor objects, as well as some base types like matrix. They return a ggbio object, which stores the original data object. Please check the corresponding method for mold to see how an object is coerced into a data.frame.

Usage

list(list("ggplot"), list("Vector"))(data, mapping = aes(), list(),
                        environment = parent.frame())
list(list("ggplot"), list("Seqinfo"))(data, mapping = aes(), list(),
                        environment = parent.frame())
list(list("ggplot"), list("ExpressionSet"))(data, mapping = aes(), list(),
                        environment = parent.frame())
list(list("ggplot"), list("RsamtoolsFile"))(data, mapping = aes(), list(),
                        environment = parent.frame())
list(list("ggplot"), list("TxDbOREnsDb"))(data, mapping = aes(), list(),
                        environment = parent.frame())
list(list("ggplot"), list("BSgenome"))(data, mapping = aes(), list(),
                        environment = parent.frame())
list(list("ggplot"), list("matrix"))(data, mapping = aes(), list(),
                        environment = parent.frame())
list(list("ggplot"), list("character"))(data, mapping = aes(), list(),
                        environment = parent.frame())
list(list("ggplot"), list("SummarizedExperiment"))(data, mapping = aes(),
                        assay.id = 1L, list(), environment = parent.frame())
list(list("ggplot"), list("GAlignments"))(data, mapping = aes(), list(),
                        environment = parent.frame())
list(list("ggplot"), list("VCF"))(data, mapping = aes(), list(),
                        environment = parent.frame())

Arguments

Details

The biggest difference for objects returned by ggplot in ggbio from ggplot2, is we always keep the original data copy, this is useful because in ggbio, our starting point is not always data.frame, many special statistical transformation is computed upon original data objects instead of coerced data.frame. This is a hack to follow ggplot2's API while allow our own defined components to trace back to original data copy and do the transformation. For objects supported by mold we transform them to data.frame stored along the original data set, for objects which not supported by mold method, we only store the original copy for ggbio specific graphics.

ggplot() is typically used to construct a plot incrementally, using the + operator to add layers to the existing ggplot object. This is advantageous in that the code is explicit about which layers are added and the order in which they are added. For complex graphics with multiple layers, initialization with ggplot is recommended. You can always call qplot in package ggplot2 or autoplot in ggbio for convenient usage.

There are three common ways to invoke ggplot :

• list() list(list("ggplot(df, aes(x, y, ))"))

• list() list(list("ggplot(df)"))

• list() list(list("ggplot()"))

The first method is recommended if all layers use the same data and the same set of aesthetics, although this method can also be used to add a layer using data from another data frame. The second method specifies the default data frame to use for the plot, but no aesthetics are defined up front. This is useful when one data frame is used predominantly as layers are added, but the aesthetics may vary from one layer to another. The third method initializes a skeleton ggplot object which is fleshed out as layers are added. This method is useful when multiple data frames are used to produce different layers, as is often the case in complex graphics.

The examples below illustrate how these methods of invoking ggplot can be used in constructing a graphic.

Value

a return ggbio object, which is a subclass of ggplot defined in ggplot2 package, but that's more, a '.data' list entry is stored with the returned object.

Seealso

mold , ggbio

Author

Tengfei Yin

Examples

set.seed(1)
N <- 100
library(GenomicRanges)
## GRanges
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))

## automatically facetting and assign y
## this must mean geom_rect support GRanges object
ggplot(gr) + geom_rect()
ggplot(gr) + geom_alignment()
ggplot() + geom_alignment(gr)


## use pure ggplot2's geom_rect, no auto facet
ggplot(gr) + ggplot2::geom_rect(aes(xmin = start, ymin = score,
xmax = end, ymax = score + 1))

## GRangesList
grl <- split(gr, values(gr)$pair)
ggplot(grl) + geom_alignment()
ggplot(grl) + geom_rect()
ggplot(grl) + ggplot2::geom_rect(aes(xmin = start, ymin = score,
xmax = end, ymax = score + 1))


## IRanges
ir <- ranges(gr)
ggplot(ir) + geom_rect()
ggplot(ir) + layout_circle(geom = "rect")

## Seqinfo
seqlengths(gr) <- c(400, 500, 420)
ggplot(seqinfo(gr)) + geom_point(aes(x = midpoint, y = seqlengths))

## matrix
mx <- matrix(1:12, nrow = 3)
ggplot(mx, aes(x = x, y = y)) + geom_raster(aes(fill = value))
## row is the factor
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = value))
colnames(mx)
colnames(mx) <- letters[1:ncol(mx)]
mx
## has extra 'colnames'
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = colnames))
rownames(mx)
rownames(mx) <- LETTERS[1:nrow(mx)]
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = rownames))
## please check autoplot, matrix for more control

##  Views
% subject <- XInteger(10, 3:-6)
% ## equal width
% v1 <- Views(subject, start=4:1, width = 3)
% ggplot(v1) + geom_tile(aes(x = x, y = row, fill = value))
% ggplot(v1) + geom_tile(aes(x = x, y = factor(1), fill = value)) +
% facet_grid(group ~. )
% ## unequal width
% v2 <- Views(subject, start=4:1, width = 3:6)
% ggplot(v2) + geom_tile(aes(x = x, y = row, fill = value))

## ExpressionSet
library(Biobase)
data(sample.ExpressionSet)
sample.ExpressionSet
set.seed(1)
## select 50 features
idx <- sample(seq_len(dim(sample.ExpressionSet)[1]), size = 50)
eset <- sample.ExpressionSet[idx,]

ggplot(eset) + geom_tile(aes(x = x, y = y, fill = value))
## please check autoplot,matrix method which gives you more control
ggplot(eset) + geom_tile(aes(x = x, y = y, fill = sex))
ggplot(eset) + geom_tile(aes(x = x, y = y, fill = type))

## Rle
library(IRanges)
lambda <- c(rep(0.001, 4500), seq(0.001, 10, length = 500),
seq(10, 0.001, length = 500))
xVector <- rpois(1e4, lambda)
xRle <- Rle(xVector)
ggplot(xRle) + geom_tile(aes(x = x, y = y, fill = value))

## RleList
xRleList <- RleList(xRle, 2L * xRle)
xRleList
ggplot(xRleList) + geom_tile(aes(x = x, y = y, fill = value)) +
facet_grid(group~.)
names(xRleList) <- c("a" ,"b")
ggplot(xRleList) + geom_tile(aes(x = x, y = y, fill = value)) +
facet_grid(group~.)


## RangedSummarizedExperiment
library(SummarizedExperiment)
nrows <- 200; ncols <- 6
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
counts2 <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
IRanges(floor(runif(200, 1e5, 1e6)), width=100),
strand=sample(c("+", "-"), 200, TRUE))
colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
row.names=LETTERS[1:6])
sset <- SummarizedExperiment(assays=SimpleList(counts=counts,
counts2 = counts2),
rowRanges=rowRanges, colData=colData)
ggplot(sset) + geom_raster(aes(x = x, y = y , fill = value))

Save a ggplot object or tracks with sensible defaults

Description

ggsave is a convenient function for saving a plot. It defaults to saving the last plot that you displayed, and for a default size uses the size of the current graphics device. It also guesses the type of graphics device from the extension. This means the only argument you need to supply is the filename.

Usage

ggsave(filename, plot = last_plot(),
    device = default_device(filename), path = NULL,
    scale = 1, width = par("din")[1],
    height = par("din")[2], units = c("in", "cm", "mm"),
    dpi = 300, limitsize = TRUE, ...)

Arguments

Details

ggsave currently recognises the extensions eps/ps, tex (pictex), pdf, jpeg, tiff, png, bmp, svg and wmf (windows only).

Create a circle layout

Description

Create a circle layout.

Usage

list(list("layout_circle"), list("GRanges"))(data, ..., geom = c("point", "line", "link", "ribbon",
                 "rect", "bar", "segment", "hist", "scale", "heatmap", "ideogram",
                 "text"), linked.to, radius = 10, trackWidth = 5,
                 space.skip = 0.015, direction = c("clockwise",
                 "anticlockwise"), link.fun = function(x, y, n = 30)
                 bezier(x, y, evaluation = n), rect.inter.n = 60, rank,
                 ylim = NULL,
                 scale.n = 60, scale.unit = NULL, scale.type = c("M",
                 "B", "sci"), grid.n = 5, grid.background = "gray70",
                 grid.line = "white", grid = FALSE, chr.weight = NULL)
list(list("layout_circle"), list("missing"))(data, ...)
circle(...)

Arguments

Value

A 'Layer'.

Author

Tengfei Yin

Examples

N <- 100
library(GenomicRanges)
## ======================================================================
##  simmulated GRanges
## ======================================================================
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))


seqlengths(gr) <- c(400, 500, 700)
values(gr)$to.gr <- gr[sample(1:length(gr), size = length(gr))]

## doesn't pass gr to the ggplot
ggplot() + layout_circle(gr, geom = "ideo", fill = "gray70", radius = 7, trackWidth = 3) +
layout_circle(gr, geom = "bar", radius = 10, trackWidth = 4, aes(fill = score, y = score)) +
layout_circle(gr, geom = "point", color = "red", radius = 14,
trackWidth = 3, grid = TRUE, aes(y = score)) +
layout_circle(gr, geom = "link", linked.to = "to.gr", radius = 6,
trackWidth = 1)

## more formal API
ggplot(gr) + layout_circle(geom = "ideo", fill = "gray70", radius = 7, trackWidth = 3) +
layout_circle(geom = "bar", radius = 10, trackWidth = 4, aes(fill = score, y = score)) +
layout_circle(geom = "point", color = "red", radius = 14,
trackWidth = 3, grid = TRUE, aes(y = score)) +
layout_circle(geom = "link", linked.to = "to.gr", radius = 6, trackWidth = 1)

Create a karyogram layout

Description

Create a karyogram layout.

Usage

list(list("layout_karyogram"), list("GRanges"))(data, ..., xlab, ylab, main,
          facets = seqnames ~ ., cytobands = FALSE, geom = "rect",
          stat = NULL, ylim = NULL, rect.height = 10)

Arguments

Value

A 'Layer'.

Author

Tengfei Yin

Examples

### R code from vignette source 'karyogram.Rnw'

###################################################
### code chunk number 1: loading
###################################################
library(ggbio)
data(hg19IdeogramCyto, package = "biovizBase")
head(hg19IdeogramCyto)
## default pre-set color stored in
getOption("biovizBase")$cytobandColor


###################################################
### code chunk number 2: default
###################################################
autoplot(hg19IdeogramCyto, layout = "karyogram", cytobands = TRUE)



###################################################
### code chunk number 3: change-order
###################################################
library(GenomicRanges)
hg19 <- keepSeqlevels(hg19IdeogramCyto, paste0("chr", c(1:22, "X", "Y")))
head(hg19)
autoplot(hg19, layout = "karyogram", cytobands = TRUE)


###################################################
### code chunk number 4: cyto-normal
###################################################
library(GenomicRanges)
## it's a 'ideogram'
biovizBase::isIdeogram(hg19)
## set to FALSE
autoplot(hg19, layout = "karyogram", cytobands = FALSE, aes(fill = gieStain)) +
scale_fill_giemsa()


###################################################
### code chunk number 5: load-RNAediting
###################################################
data(darned_hg19_subset500, package = "biovizBase")
dn <- darned_hg19_subset500
head(dn)
## add seqlengths
## we have seqlegnths information in another data set
data(hg19Ideogram, package = "biovizBase")
seqlengths(dn) <- seqlengths(hg19Ideogram)[names(seqlengths(dn))]
## now we have seqlengths
head(dn)
## then we change order
dn <- keepSeqlevels(dn, paste0("chr", c(1:22, "X")))
autoplot(dn, layout = "karyogram")
## this equivalent to
## autoplot(seqinfo(dn))


###################################################
### code chunk number 6: load-RNAediting-color
###################################################
## since default is geom rectangle, even though it's looks like segment
## we still use both fill/color to map colors
autoplot(dn, layout = "karyogram", aes(color = exReg, fill = exReg))


###################################################
### code chunk number 7: load-RNAediting-color-NA
###################################################
## since default is geom rectangle, even though it's looks like segment
## we still use both fill/color to map colors
autoplot(dn, layout = "karyogram", aes(color = exReg, fill = exReg)) +
scale_color_discrete(na.value = "brown")


###################################################
### code chunk number 8: load-RNAediting-color-fake
###################################################
dn2 <- dn
seqlengths(dn2) <- rep(max(seqlengths(dn2)), length(seqlengths(dn2)) )
autoplot(dn2, layout = "karyogram", aes(color = exReg, fill = exReg))


###################################################
### code chunk number 9: plotKaryogram (eval = FALSE)
###################################################
## plotKaryogram(dn)
## plotKaryogram(dn, aes(color = exReg, fill = exReg))


###################################################
### code chunk number 10: low-default
###################################################
## plot ideogram
p <- ggplot(hg19) + layout_karyogram(cytobands = TRUE)
p
## eqevelant autoplot(hg19, layout = "karyogram", cytobands = TRUE)


###################################################
### code chunk number 11: low-default-addon
###################################################
p <- p + layout_karyogram(dn, geom = "rect", ylim = c(11, 21), color = "red")
## commented line below won't work
## the cytoband fill color has been used already.
## p <- p + layout_karyogram(dn, aes(fill = exReg, color = exReg), geom = "rect")
p


###################################################
### code chunk number 12: edit-space
###################################################
## plot chromosome space
p <- autoplot(seqinfo(dn))
## make sure you pass rect as geom
## otherwise you just get background
p <- p + layout_karyogram(dn, aes(fill = exReg, color = exReg), geom = "rect")
values(dn)$pvalue <- rnorm(length(dn))
p + layout_karyogram(dn, aes(x = start, y = pvalue), ylim = c(10, 30), geom = "line", color = "red")
p


###################################################
### code chunk number 13: sessionInfo
###################################################
sessionInfo()

Simple navigation for ggbio object.

Description

A set of simple navigation API apply to ggbio object, let you move along the genome and zoom in/out.

Usage

zoom(fac = 1/2)
zoom_in(fac = 1/2)
zoom_out(fac = 2)
nextView(unit = c("view", "gene", "exon", "utr"))
prevView(unit = c("view", "gene", "exon", "utr"))

Arguments

Details

zoom_in and zoom_out are just simple wrapper around zoom function.

For more convenient, gene features based jumpting we will support it in the future.

Value

A special class of navigation.

Author

Tengfei Yin

Plot estimated fragment length for paired-end RNA-seq data

Description

Plot estimated fragment length for paired-end RNA-seq data against single reduced data model.

Usage

list(list("plotFragLength"), list("character,GRanges"))(data, model,
               gap.ratio = 0.0025,
               geom = c("segment", "point", "line"),
               type = c("normal", "cut"),
               heights = c(400, 100),
               annotation = TRUE)

Arguments

Details

We use a easy way to define this estimated fragment length, we collect all paired reads and model, reduce model first, then find common gaps, remove common gaps between paired-end reads, and compute the new estimated fragment length.

Value

A ggplot object when annotation = FALSE and a frame grob if annotation = TRUE

Author

Tengfei Yin

Examples

data(genesymbol)
bamfile <- system.file("extdata", "SRR027894subRBM17.bam", package="biovizBase")
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
model <- exonsBy(txdb, by = "tx")
model.new <- subsetByOverlaps(model, genesymbol["RBM17"])
exons.rbm17 <- subsetByOverlaps(exons(txdb), genesymbol["RBM17"])
exons.new <- reduce(exons.rbm17)
plotFragLength(bamfile, exons.new, geom = "line")
plotFragLength(bamfile, exons.new, geom = c("point","segment"))
plotFragLength(bamfile, exons.new, geom = c("point","segment"), annotation = FALSE)
plotFragLength(bamfile, exons.new, geom = c("point","segment"), type = "cut",
gap.ratio = 0.001)

Manhattan for GWAS

Description

A Manhattan plot is special scatter plot used to visualize data with a large number of data points, with a distribute of some higher-magnitude values. For example, in the GWAS(genome-wide association studies). Here we mainly focus on GWAS Manhattan plots. X-axis is genomic coordinates and Y-axis is negative logarithm of the associated P-value for each single nucleotide polymorphism. So higher the value, more stronger the association they are.

Usage

plotGrandLinear(obj, ..., facets, space.skip = 0.01, geom = NULL,
                 cutoff = NULL, cutoff.color = "red", cutoff.size = 1,
                 legend = FALSE, xlim, ylim, xlab, ylab, main,
                 highlight.gr = NULL, highlight.name = NULL,
                 highlight.col = "red", highlight.label = TRUE,
                 highlight.label.size = 5, highlight.label.offset =
                 0.05, highlight.label.col = "black", spaceline =
                 FALSE)

Arguments

Details

Please use seqlengths of the object and space.skip arguments to control the layout of the coordiant genome transformation.

aes(y = ...) is requried.

aes(color = ) is used to mapping to data variables, if just pass "color" without aes(), then will recycle the color to represent each chromosomes.please see the example below.

Value

Return a ggplot object.

Author

Tengfei Yin

Examples

##  load
library(ggbio)
data(hg19IdeogramCyto, package = "biovizBase")
data(hg19Ideogram, package = "biovizBase")
library(GenomicRanges)

##  simul_gr
library(biovizBase)
gr <- GRanges(rep(c("chr1", "chr2"), each = 5),
IRanges(start = rep(seq(1, 100, length = 5), times = 2),
width = 50))
autoplot(gr)

##  coord:genome
autoplot(gr, coord = "genome")
gr.t <- transformToGenome(gr)
head(gr.t)

##  is
is_coord_genome(gr.t)
metadata(gr.t)$coord


##  simul_snp
chrs <- as.character(levels(seqnames(hg19IdeogramCyto)))
seqlths <- seqlengths(hg19Ideogram)[chrs]
set.seed(1)
nchr <- length(chrs)
nsnps <- 100
gr.snp <- GRanges(rep(chrs,each=nsnps),
IRanges(start =
do.call(c, lapply(chrs, function(chr){
N <- seqlths[chr]
runif(nsnps,1,N)
})), width = 1),
SNP=sapply(1:(nchr*nsnps), function(x) paste("rs",x,sep='')),
pvalue =  -log10(runif(nchr*nsnps)),
group = sample(c("Normal", "Tumor"), size = nchr*nsnps,
replace = TRUE)
)

##  shorter
seqlengths(gr.snp)
nms <- seqnames(seqinfo(gr.snp))
nms.new <- gsub("chr", "", nms)
names(nms.new) <- nms
gr.snp <- renameSeqlevels(gr.snp, nms.new)
seqlengths(gr.snp)



##  unorder
autoplot(gr.snp, coord = "genome", geom = "point", aes(y = pvalue), space.skip = 0.01)

##  sort
gr.snp <- keepSeqlevels(gr.snp, c(1:22, "X", "Y"))
autoplot(gr.snp, coord = "genome", geom = "point", aes(y = pvalue), space.skip = 0.01)

##  with_seql
names(seqlths) <- gsub("chr", "", names(seqlths))
seqlengths(gr.snp) <- seqlths[names(seqlengths(gr.snp))]
autoplot(gr.snp, coord = "genome", geom = "point", aes(y = pvalue), space.skip = 0.01)

##  line
autoplot(gr.snp, coord = "genome", geom = "line", aes(y = pvalue, group = seqnames,
color = seqnames))



##  plotGrandLinear
plotGrandLinear(gr.snp, aes(y = pvalue))

##  morecolor
plotGrandLinear(gr.snp, aes(y = pvalue, color = seqnames))
plotGrandLinear(gr.snp, aes(y = pvalue), color = c("green", "deepskyblue"))
plotGrandLinear(gr.snp, aes(y = pvalue), color = c("green", "deepskyblue", "red"))
plotGrandLinear(gr.snp, aes(y = pvalue), color = "red")

##  cutoff
plotGrandLinear(gr.snp, aes(y = pvalue), cutoff = 3, cutoff.color = "blue", cutoff.size = 4)

##  cutoff-low
plotGrandLinear(gr.snp, aes(y = pvalue)) + geom_hline(yintercept = 3, color = "blue", size = 4)

##  longer
## let's make a long name
nms <- seqnames(seqinfo(gr.snp))
nms.new <- paste("chr00000", nms, sep = "")
names(nms.new) <- nms
gr.snp <- renameSeqlevels(gr.snp, nms.new)
seqlengths(gr.snp)

##  rotate
plotGrandLinear(gr.snp, aes(y = pvalue)) + theme(axis.text.x=element_text(angle=-90, hjust=0))

##  sessionInfo
sessionInfo()

Plot Ranges Linked with Data

Description

Plot GRanges object structure and linked to a even spaced paralell coordinates plot which represting the data in elementeMetadata.

Usage

list(list("plotRangesLinkedToData"), list("RangedSummarizedExperiment"))(data, ...,
          stat.y = seq_len(ncol(data)), stat.ylab = names(assays(data)[stat.assay]),
          stat.assay = 1L)
list(list("plotRangesLinkedToData"), list("GenomicRanges_OR_GRangesList"))(data, ...,
          stat.y = seq_len(ncol(mcols(data))),
          stat.ylab, sig, sig.col = c("black", "red"),
          stat.coord.trans = coord_trans(),
          annotation = list(), width.ratio = 0.8,
          theme.stat = theme_gray(), theme.align = theme_gray(),
          linetype = 3, heights)

Arguments

Details

Inspired by some graphics produced in some other packages, for example in package DEXseq, the author provides graphics with gene models and linked to an even spaced statistics summary. This is useful because we always plot everything along the genomic coordinates, but genomic features like exons are not evenly distributed, so we could actually treat the statistics associated with exons like categorical data, and show them as "Paralell Coordinates Plots". This is one special layout which represent the data in a nice manner and also keep the genomic structure information. With abliity of tracks , it's possible to generate such type of a graphic along with other annotations.

The data we want is a normal GRanges object, and make sure the intervals are not overlaped with each other(currently), and you may have multiple columns which store the statistics for multiple samples, then we produce the graphic we introduced above and users could pass other annotation track in the function which will be shown below the main linked track.

The reason you need to pass annotation into the function instead of binding them by tracks later is because binding manually with annotation tracks is tricky and this function doesn't return a ggplot object.

Value

return a frame grob; side-effect (plotting) if plot=T.

Author

Tengfei Yin

Examples

library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(ggbio)
data(genesymbol, package = "biovizBase")
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
model <- exonsBy(txdb, by = "tx")
model17 <- subsetByOverlaps(model, genesymbol["RBM17"])
exons <- exons(txdb)
exon17 <- subsetByOverlaps(exons, genesymbol["RBM17"])
## reduce to make sure there is no overlap
## just for example
exon.new <- reduce(exon17)
## suppose
values(exon.new)$sample1 <- rnorm(length(exon.new), 10, 3)
values(exon.new)$sample2 <- rnorm(length(exon.new), 10, 10)
values(exon.new)$score <- rnorm(length(exon.new))
values(exon.new)$significant <- sample(c(TRUE,FALSE), size = length(exon.new),replace = TRUE)

plotRangesLinkedToData(exon.new, stat.y = c("sample1", "sample2"))
plotRangesLinkedToData(exon.new, stat.y = 1:2)
plotRangesLinkedToData(exon.new, stat.y = 1:2, size = 3, linetype = 4)
plotRangesLinkedToData(exon.new, stat.y = 1:2, size = 3, linetype = 4,
sig = "significant")
plotRangesLinkedToData(exon.new, stat.y = 1:2, size = 3, linetype = 4,
sig = "significant", sig.col = c("gray90","red"))

Plot single chromosome with cytobands

Description

Plot single chromosome with cytobands.

Usage

plotIdeogram(obj, subchr = NULL, zoom.region = NULL, which = NULL, xlab, ylab, main, xlabel =
                 FALSE, color = "red", fill = "red", alpha = 0.7,
                 zoom.offset = 0.2, size = 1,
                 cytobands = TRUE, aspect.ratio = 1/20, genome)
## constructor
Ideogram(obj, subchr = NULL, which = NULL, xlabel = FALSE,
                 cytobands = TRUE, color = "red", fill = "red", alpha =
                 0.7, zoom.region = NULL, zoom.offset = 0.2, size = 1,
                 aspect.ratio = 1/20, ..., genome)

Arguments

Details

User could provide the whole ideogram and use subchr to point to particular chromosome.

Value

A ggplot object.

Author

Tengfei Yin

Examples

library(biovizBase)
p.ideo <- Ideogram(genome = "hg19")
p.ideo
library(GenomicRanges)
p.ideo + xlim(GRanges("chr2", IRanges(1e8, 1e8+10000)))
Ideogram(genome = "hg19", xlabel = TRUE)

Plot Splice Summary from RNA-seq data

Description

Plot splice summary by simply counting overlaped junction read in weighted way or not.

Usage

## For character,GRangesList
list(list("plotSpliceSum"), list("character,GRangesList"))(data, model, ..., weighted = TRUE)
## For character,TxDb
list(list("plotSpliceSum"), list("character,TxDb"))(data, model, which,
      ..., weighted = TRUE)
## For character,EnsDb
list(list("plotSpliceSum"), list("character,EnsDb"))(data, model, which,
      ..., weighted = TRUE)

Arguments

Details

Internally we use biovizBase:::spliceSummary for simple counting, but we encourage users to use their own robust way to make slicing summary and store it as GRangesList, then plot the summary by qplot function.

Value

A ggplot object.

Seealso

qplot

Author

Tengfei Yin

Examples

bamfile <- system.file("extdata", "SRR027894subRBM17.bam", package="biovizBase")
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
data(genesymbol)
exons <- exonsBy(txdb, by = "tx")
exons.rbm17 <- subsetByOverlaps(exons, genesymbol["RBM17"])
plotSpliceSum(bamfile, exons.rbm17)
plotSpliceSum(bamfile, exons.rbm17, weighted = FALSE, offset = 0.01)
plotSpliceSum(bamfile, txdb, which = genesymbol["RBM17"])
plotSpliceSum(bamfile, txdb, which = genesymbol["RBM17"], offset = 0.01)
plotSpliceSum(bamfile, txdb, which = genesymbol["RBM17"],
show.label = TRUE,
label.type = "count")

Plot stacked overview

Description

Plot stacked overview for genome with or without cytobands. It's a wrapper around layout_karyogram .

Usage

plotStackedOverview(obj, ..., xlab, ylab, main, geom = "rect",
                         cytobands = FALSE, rescale = TRUE,
                         rescale.range = c(0, 10))
plotKaryogram(obj, ..., xlab, ylab, main, geom = "rect",
                         cytobands = FALSE, rescale = TRUE,
                         rescale.range = c(0, 10))

Arguments

Details

Stacked overview is just a arbitrary layout for karyogram layout, it use facets seqnaems ~ . as default to stack the genome. For accurate mapping, you need to provide seqlengths information in your GRanges object. Otherwise, data space will be computed for stacked overview chromosome background, this is NOT the actual chromosome space!.

Value

A ggplot object.

Author

Tengfei Yin

Examples

library(biovizBase)
data(hg19IdeogramCyto, package = "biovizBase")
library(GenomicRanges)

## you can also get ideogram by biovizBase::getIdeogram

## make shorter and clean labels
old.chrs <- seqnames(seqinfo(hg19IdeogramCyto))
new.chrs <- gsub("chr", "", old.chrs)
## lst <- as.list(new.chrs)
names(new.chrs) <- old.chrs
new.ideo <- renameSeqlevels(hg19IdeogramCyto, new.chrs)
new.ideo <- keepSeqlevels(new.ideo, c(as.character(1:22) , "X", "Y"))
new.ideo


## sample data
data(darned_hg19_subset500, package = "biovizBase")
idx <- is.na(values(darned_hg19_subset500)$exReg)
values(darned_hg19_subset500)$exReg[idx] <- "unknown"

## you need to add seqlengths for accruate mapping
chrnames <- unique(as.character(seqnames(darned_hg19_subset500)))
data(hg19Ideogram, package = "biovizBase")
seqlengths(darned_hg19_subset500) <- seqlengths(hg19Ideogram)[sort(chrnames)]


dn <- darned_hg19_subset500
values(dn)$score <- rnorm(length(dn))

## plotStackedOverview is a simple wrapper around this functions to
create a stacked layout
plotStackedOverview(new.ideo, cytobands = TRUE)

plotStackedOverview(dn)
plotStackedOverview(dn, aes(color = exReg, fill = exReg))
## this will did the trick for you to rescale the space
plotStackedOverview(dn, aes(x = midpoint, y = score), geom = "line")
plotStackedOverview(dn, aes(x = midpoint, y = score), geom = "line", rescale.range = c(4, 6))
## no rescale
plotStackedOverview(dn, aes(x = midpoint, y = score), geom = "line", rescale = FALSE,
xlab = "xlab", ylab = "ylab", main  = "main") + ylab("ylab")

## no object? will ask you for species and query the data on the fly
plotStackedOverview()
plotStackedOverview(cytobands = TRUE)

rescale ggplot object

Description

Rescale a numeric vector or ggplot object, could be used for static zoom-in in ggbio.

Usage

list(list("rescale"), list("numeric"))(x, to = c(0, 1),
      from = range(x, na.rm = TRUE))
list(list("rescale"), list("ggplot"))(x, xlim, ylim, sx = 1, sy = 1)
list(list("rescale"), list("gg"))(x, xlim, ylim, sx = 1, sy = 1)

Arguments

Details

When x is numeric value, it's just call scales::rescale, please refer to the manual page to check more details. If x is ggplot object, it first try to estimate current x limits and y limits of the ggplot object, then rescale based on those information.

Value

Return the object of the same class as x after rescaling.

Author

Tengfei Yin

Examples

library(ggbio)
head(mtcars)
range(mtcars$mpg)
p <- qplot(data = mtcars, x = mpg, y = disp, geom = "point")
p.new <- rescale(p, xlim = c(20, 25))
p.new

scale color for fold change values

Description

In biology, lots of data are scaled to value around 0, and people like to show them as blue-white-red scale color, where negative value are blue, 0 is white and positive value is red, and they are scaled for continuous variables.

Usage

scale_fill_fold_change()

Value

a list.

Author

Tengfei Yin

Examples

p1 <- autoplot(volcano - 150)
p1
p1 + scale_fill_fold_change()

scale filled color to customized giemsa color.

Description

scale filled color to customized giemsa color.

Usage

scale_fill_giemsa(fill = getOption("biovizBase")$cytobandColor)

Arguments

Value

a list.

Author

Tengfei Yin

Examples

getOption("biovizBase")$cytobandColor
library(biovizBase)
data(hg19IdeogramCyto)
p1 <- autoplot(hg19IdeogramCyto, layout = "karyogram", aes(fill  =
gieStain))
p1
p1 + scale_fill_giemsa()

scale x by unit

Description

scale x by unit 'Mb','kb', 'bp'.

Usage

scale_x_sequnit(unit = c("Mb", "kb", "bp"), append = NULL)

Arguments

Value

'position_c'

Author

Tengfei Yin

Examples

library(ggplot2)
p <- qplot(x = seq(1, to = 10000, length.out = 40), y = rnorm(40), geom
= "point")
## default mb
p + scale_x_sequnit()
p + scale_x_sequnit("kb")
p + scale_x_sequnit("bp")

Generates summaries on the specified windows

Description

Generates summaries on the specified windows

Usage

% for GRangeslist(list("stat_aggregate"), list("GRanges"))(data, ..., xlab, ylab, main, by, FUN,
                          maxgap=-1L, minoverlap=0L,
                          type=c("any", "start", "end", "within", "equal"),
                          select=c("all", "first", "last", "arbitrary"),
                          y = NULL, window = NULL, facets = NULL, 
                          method = c("mean", "median","max",
                                   "min", "sum", "count", "identity"),
                          geom = NULL)

Arguments

Value

A 'Layer'.

Author

Tengfei Yin

Examples

library(GenomicRanges)
set.seed(1)
N <- 1000
## ======================================================================
##  simmulated GRanges
## ======================================================================
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))


ggplot(gr) + stat_aggregate(aes(y = value))
## or
## ggplot(gr) + stat_aggregate(y = "value")
ggplot(gr) + stat_aggregate(aes(y = value), window = 36)
ggplot(gr) + stat_aggregate(aes(y = value), select = "first")
## no hits
ggplot(gr) + stat_aggregate(aes(y = value), select = "first", type = "within")
ggplot(gr) + stat_aggregate(window = 30,  aes(y = value),fill = "gray40", geom = "bar")
ggplot(gr) + stat_aggregate(window = 100, fill = "gray40", aes(y = value),
method = "max", geom = "bar")

ggplot(gr) + stat_aggregate(aes(y = value), geom = "boxplot")
ggplot(gr) + stat_aggregate(aes(y = value), geom = "boxplot", window = 60)
## now facets need to take place inside stat_* geom_* for an accurate computation
ggplot(gr) + stat_aggregate(aes(y = value), geom = "boxplot", window = 30,
facets = sample ~ seqnames)
## FIXME:
## autoplot(gr, stat = "aggregate", aes(y = value), window = 36)
## autoplot(gr, stat = "aggregate", geom = "boxplot", aes(y = value), window = 36)

Binning method

Description

Binning method especially for Rle and RleList , for data.frame it's just calling ggplot2::stat_bin .

Usage

list(list("stat_bin"), list("ANY"))(data, ...)
list(list("stat_bin"), list("Rle"))(data, ..., binwidth, nbin = 30,
         xlab, ylab, main, geom = c("bar", "heatmap"),
         type = c("viewSums","viewMins",
                                  "viewMaxs", "viewMeans"))
list(list("stat_bin"), list("RleList"))(data, ..., binwidth, nbin = 30,
          xlab, ylab, main,
          indName = "sample",
          geom = c("bar", "heatmap"),
          type = c("viewSums","viewMins",
          "viewMaxs", "viewMeans"))

Arguments

Value

a ggplot object.

Author

Tengfei Yin

Examples

library(IRanges)
lambda <- c(rep(0.001, 4500), seq(0.001, 10, length = 500),
seq(10, 0.001, length = 500))
xVector <- rpois(1e4, lambda)
xRle <- Rle(xVector)
xRleList <- RleList(xRle, 2L * xRle)

ggplot() + stat_bin(xRle)
ggplot(xRle) + stat_bin()
ggplot(xRle) + stat_bin(nbin = 100)
ggplot(xRle) + stat_bin(binwidth = 200)

p1 <- ggplot(xRle) + stat_bin(type = "viewMeans")
p2 <- ggplot(xRle) + stat_bin(type = "viewSums")
## y scale are different.
tracks(viewMeans = p1, viewSums = p2)

ggplot(xRle) + stat_bin(geom = "heatmap")
ggplot(xRle) + stat_bin(nbin = 100, geom = "heatmap")
ggplot(xRle) + stat_bin(binwidth = 200, geom = "heatmap")

## for RleList
ggplot(xRleList) + stat_bin()
ggplot(xRleList) + stat_bin(nbin = 100)
ggplot(xRleList) + stat_bin(binwidth = 200)

p1 <- ggplot(xRleList) + stat_bin(type = "viewMeans")
p2 <- ggplot(xRleList) + stat_bin(type = "viewSums")
## y scale are different.
tracks(viewMeans = p1, viewSums = p2)

ggplot(xRleList) + stat_bin(geom = "heatmap")
ggplot(xRleList) + stat_bin(nbin = 100, geom = "heatmap")
ggplot(xRleList) + stat_bin(binwidth = 200, geom = "heatmap")

Calculate coverage

Description

Calculate coverage.

Usage

# for GRanges
list(list("stat_coverage"), list("GRanges"))(data, ..., xlim, xlab, ylab, main,
              facets = NULL, geom = NULL)
# for GRangesList
list(list("stat_coverage"), list("GRangesList"))(data, ..., xlim, xlab, ylab, main,
              facets = NULL, geom = NULL)
# for Bamfile
list(list("stat_coverage"), list("BamFile"))(data, ..., maxBinSize = 2^14,
               xlim, which, xlab, ylab,
               main, facets = NULL, geom = NULL,
               method = c("estimate", "raw"),
               space.skip = 0.1, coord = c("linear", "genome"))

Arguments

Value

A 'Layer'.

Author

Tengfei Yin

Examples

library(ggbio)
## ======================================================================
##  simmulated GRanges
## ======================================================================
set.seed(1)
N <- 1000
library(GenomicRanges)

gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))

ggplot(gr) + stat_coverage()
ggplot() + stat_coverage(gr)

ggplot(gr) + stat_coverage(geom = "point")
ggplot(gr) + stat_coverage(geom = "area")
ggplot(gr) + stat_coverage(aes(y = ..coverage..), geom = "bar")

ggplot(gr) + stat_coverage(aes(y = ..coverage..)) + geom_point()

## for bam file
## TBD

Calculate gene structure

Description

Calculate gene structure.

Usage

list(list("stat_gene"), list("TxDb"))(data, ...)

Arguments

Value

A 'Layer'.

Seealso

geom_alignment

Author

Tengfei Yin

Examples

## loading package
## Deprecated
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
data(genesymbol, package = "biovizBase")
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene

## made a track comparing full/reduce stat.
p1 <- ggplot() + geom_alignment(txdb, which = genesymbol["RBM17"])
p1 <- ggplot() + stat_gene(txdb, which = genesymbol["RBM17"])
## or
p1 <- ggplot(txdb) + stat_gene(which = genesymbol["RBM17"])

p1 <- ggplot(txdb) + stat_gene(which = genesymbol["RBM17"])
p2 <- ggplot(txdb) + stat_gene(which = genesymbol["RBM17"], stat =
"reduce")
p2 <- ggplot(txdb) + stat_gene(which = genesymbol["RBM17"], stat = "reduce")
## ggplot(txdb) + geom_alignment(which = genesymbol["RBM17"]) + stat_reduce()
## ggplot(txdb) + geom_alignment(which = genesymbol["RBM17"])
tracks(full = p1, reduce = p2, heights = c(3, 1))

## change y labels
ggplot(txdb) + stat_gene(which = genesymbol["RBM17"], names.expr =
"tx_id:::gene_id")

Transform the data to a data.frame and for multiple geoms.

Description

Transform the data to a suitable data.frame and then one could use multiple geom or even stat to re-plot the data.

Usage

list(list("stat_identity"), list("ANY"))(data, ...)
list(list("stat_identity"), list("GRanges"))(data, ..., geom = NULL)
list(list("stat_identity"), list("Rle"))(data, ..., xlab, ylab, main, geom = NULL)
list(list("stat_identity"), list("RleList"))(data, ..., xlab, ylab, main,
          geom = NULL, indName = "sample")

Arguments

Value

A 'Layer'.

Author

Tengfei Yin

Examples

##  load
set.seed(1)
N <- 50

require(GenomicRanges)
##  simul
## ======================================================================
##  simmulated GRanges
## ======================================================================
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))

##  geom_point_start
ggplot() + stat_identity(gr, aes(x = start, y = value), geom = "point")
## or more formal
ggplot(gr) + stat_identity(aes(x = start, y = value), geom = "point")

##  geom_point_midpoint
ggplot(gr) + stat_identity(aes(x = midpoint, y = value), geom = "point")

##  geom_rect_all
ggplot(gr) + stat_identity(aes(xmin = start, xmax = end,
ymin = value - 0.5, ymax = value + 0.5),
geom = "rect")

##  geom_rect_y
ggplot(gr) + stat_identity(aes(y = value), geom = "rect")

##  geom_line
ggplot(gr) + stat_identity(aes(x = start, y = value),  geom = "line")

##  geom_segment
ggplot(gr) + stat_identity(aes(y = value), geom = "segment")

## Rle/RleList
library(IRanges)
lambda <- c(rep(0.001, 4500), seq(0.001, 10, length = 500),
seq(10, 0.001, length = 500))
xVector <- rpois(1e4, lambda)
xRle <- Rle(xVector)
xRleList <- RleList(xRle, 2L * xRle)

ggplot(xRle) + stat_identity(geom = "point")
ggplot(xRleList) + stat_identity(geom = "point")

Calculate mismatch summary

Description

Calculate mismatch summary

Usage

## for GRanges
list(list("stat_mismatch"), list("GRanges"))(data, ..., bsgenome, 
              xlab, ylab, main,
              geom = c("segment", "bar"),
              show.coverage = TRUE)
## for BamFile
list(list("stat_mismatch"), list("BamFile"))(data, ..., bsgenome, which,
              xlab, ylab, main,
              geom = c("segment", "bar"),
              show.coverage = TRUE)

Arguments

Value

A 'Layer'.

Author

Tengfei Yin

Reduce an object.

Description

Reduce GRanges , IRanges or TxDb object.

Usage

list(list("stat_reduce"), list("GRanges"))(data, ...,
           xlab, ylab, main,
           drop.empty.ranges = FALSE,
           min.gapwidth = 1L,
           facets = NULL, geom = NULL)
list(list("stat_reduce"), list("IRanges"))(data, ...,
           xlab, ylab, main,
           drop.empty.ranges = FALSE,
           min.gapwidth = 1L,
           with.inframe.attrib=FALSE,
           facets = NULL, geom = NULL)
list(list("stat_reduce"), list("TxDbOREnsDb"))(data, ...)

Arguments

Value

a ggplot object.

Seealso

reduce .

Author

Tengfei Yin

Examples

set.seed(1)
N <- 1000
library(GenomicRanges)

gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))

ggplot(gr) + stat_reduce()
autoplot(gr, stat = "reduce")
strand(gr) <- "*"
ggplot(gr) + stat_reduce()

library(TxDb.Hsapiens.UCSC.hg19.knownGene)
data(genesymbol, package = "biovizBase")
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
## made a track comparing full/reduce stat.
ggplot(txdb) + stat_reduce(which = genesymbol["RBM17"])

Slice Rle/RleList to view them as bar or heatmap.

Description

Slice Rle/RleList to different view by set lower or other parameters, then view summary for all those viewed region.

Usage

list(list("stat_slice"), list("Rle"))(data, ...,
          xlab, ylab, main,
          na.rm = FALSE,
          geom = NULL,
          lower=-Inf, upper=Inf,
          includeLower=TRUE, includeUpper=TRUE,
          rangesOnly = FALSE,                                       
          type = c("viewSums","viewMins",
          "viewMaxs", "viewMeans"))
list(list("stat_slice"), list("RleList"))(data, ...,
          xlab, ylab, main,
          indName = "sample",
          na.rm = FALSE,
          geom = NULL,
          lower=-Inf, upper=Inf,
          includeLower=TRUE, includeUpper=TRUE,
          rangesOnly = FALSE,                                       
          type = c("viewSums","viewMins",
                   "viewMaxs", "viewMeans"))

Arguments

Value

a ggplot object.

Seealso

slice

Author

Tengfei Yin

Examples

library(IRanges)
lambda <- c(rep(0.001, 4500), seq(0.001, 10, length = 500),
seq(10, 0.001, length = 500))
xVector <- rpois(1e4, lambda)
xRle <- Rle(xVector)
xRleList <- RleList(xRle, 2L * xRle)

ggplot(xRle) + stat_slice(lower = 5)
ggplot(xRle) + stat_slice(lower = 5, geom = "bar")
ggplot(xRle) + stat_slice(lower = 5, geom = "heatmap")

p1 <- ggplot(xRle) + stat_slice(type = "viewMeans", lower = 5,
geom = "bar")
p2 <- ggplot(xRle) + stat_slice(type = "viewSums", lower = 5,
geom = "bar")
## y scale are different.
tracks(viewMeans = p1, viewSums = p2)

ggplot(xRleList) + stat_slice(lower = 5)
ggplot(xRleList) + stat_slice(lower = 5, geom = "bar")
ggplot(xRleList) + stat_slice(lower = 5, geom = "heatmap")

p1 <- ggplot(xRleList) + stat_slice(type = "viewMeans", lower = 5,
geom = "bar")
p2 <- ggplot(xRleList) + stat_slice(type = "viewSums", lower = 5,
geom = "bar")
## y scale are different.
tracks(viewMeans = p1, viewSums = p2)

Calculate stepping levels

Description

Calculate stepping levels.

Usage

list(list("stat_stepping"), list("GRanges"))(data, ..., xlab, ylab, main,
              facets = NULL,
              geom = c("rect", "alignment", "segment"))

Arguments

Value

A 'Layer'.

Author

Tengfei Yin

Examples

set.seed(1)
N <- 50

require(GenomicRanges)
##  simul
## ======================================================================
##  simmulated GRanges
## ======================================================================
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))

##  default
ggplot(gr) + stat_stepping()
## or
ggplot() + stat_stepping(gr)

##  facet_aes
ggplot(gr) + stat_stepping(aes(color = strand, fill = strand),
facets = sample ~ seqnames)

##  geom_segment
ggplot(gr) + stat_stepping(aes(color = strand),
geom = "segment", xlab = "Genomic coord", ylab = "y", main = "hello")


##  geom_alignment
## ggplot(gr) + stat_stepping(geom = "alignment")

##  geom_alignment_group
##  ggplot(gr) + stat_stepping(aes(group = pair),geom = "alignment")

Tabulate a GRanges object

Description

Tabulate a GRanges object

Usage

list(list("stat_table"), list("GRanges"))(data, ..., xlab, ylab, main,
            geom = NULL,stat = NULL)
list(list("stat_table"), list("GRangesList"))(data, ..., xlab, ylab, main,
            facets = NULL, geom = NULL)

Arguments

Value

A 'Layer'.

Author

Tengfei Yin

Examples

##  load
set.seed(1)
N <- 100
require(ggbio)
require(GenomicRanges)
##  simul
## ======================================================================
##  simmulated GRanges
## ======================================================================
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))

gr <- c(gr[seqnames(gr) == "chr1"][sample(1:10, size = 1e4, replace = TRUE)],gr)

##  default
ggplot(gr) + stat_table()
ggplot(gr) + stat_table(geom = "segment", aes(y = ..score.., color = ..score..))
ggplot(gr) + stat_table(aes(color = score))

theme in ggbio

Description

Theme defined in ggbio for plot or tracks.

Usage

theme_null()
  theme_noexpand()    
  theme_alignment(ylabel = FALSE, base_size = 12, base_family = "", 
  axis = TRUE, border = TRUE, grid = TRUE)
  theme_pack_panels(strip.bg = FALSE, strip.text.y = TRUE)
  theme_clear(grid.y = FALSE, grid.x.minor = FALSE, grid.x.major = FALSE,
              panel.background.fill = "white", panel.border.color = NA,
              axis.ticks.x = FALSE, axis.ticks.y = TRUE, grid.color = "gray95",
              axis.line.color = "gray80")
  theme_tracks_sunset(bg = "#fffedb", alpha = 1, ...)
  theme_genome()

Arguments

Details

Themes speciall designed for tracks, are named following naming schema themetracks*

Value

Return a theme.

Author

Tengfei Yin

Examples

##  load
library(ggbio)
p <- qplot(data = mtcars, x = mpg,  y = wt, facets = cyl ~ .)
p + theme_null()
p + theme_clear()
p + theme_pack_panels()
p + theme_alignment()
p1 <- qplot(data = mtcars, x = mpg,  y = wt)
tracks(p1 = p, p2 = p1)
tracks(p1 = p, p2 = p1) + theme_tracks_sunset()

Tracks for genomic graphics

Description

tracks is a conventient constructor for bindind graphics as trakcs. You dont' have to worry about adjusting different graphics, tracks did that for you. It's NOT just limited to bind genomic tracks, you can use this function to bind any tracks with the same defination of x axis, for example, sets of time series plots you made.

Tracks view is most common way to viewing genome features and annotation data and widely used by most genome browsers. Our assumption is that, most graphics you made with ggbio or by yourself using ggplot2, are almost always sitting on the genomic coordinates or the same x axis. And to compare annotation information along with genome features, we need to align those plots on exactly the same x axis in order to form your hypothesis. This function leaves you the flexibility to construct each tracks separately with worrying your alignments later.

Usage

tracks(..., heights, xlim, xlab = NULL, main = NULL,
            title = NULL, theme = NULL, 
            track.plot.color = NULL,
            track.bg.color = NULL,
            main.height = unit(1.5, "lines"),
            scale.height = unit(1, "lines"),
            xlab.height = unit(1.5, "lines"),
            padding = unit(-1, "lines"),
            label.bg.color =  "white",
            label.bg.fill = "gray80",
            label.text.color = "black",
            label.text.cex = 1,
            label.text.angle = 90,
            label.width = unit(2.5, "lines"))

Arguments

Details

tracks did following modification for passed plots.

• list() list(" ", " ", " remove x-axis, ticks, xlab and tile for each track and add scales ", " at bottom. We suppose a new xlab and title would be provided by the ", " ", list("tracks"), " function for the whole tracks, but we still keep ", " individual's y axis. ", "
  ", " ")

• list() list(" ", " align x-scale limits to make sure every plots sitting on exactly ", " the same x scale. ", " ")

• list() list(" ", " squeezing plots together to some extent. ", " ")

• list(" ", " labeling tracks if names are provided, please check utilities section ", " about ", list("labeled"), " method. ", " ")

• list(" ", " return a track object. This would allow many features introduced ", " in this manual. ", " ")

Value

A Tracks object.

Seealso

align.plots

Author

Tengfei Yin

Examples

## make a simulated time series data set
df1 <- data.frame(time = 1:100, score = sin((1:100)/20)*10)
p1 <- qplot(data = df1, x = time, y = score, geom = "line")
df2 <- data.frame(time = 30:120, score = sin((30:120)/20)*10, value = rnorm(120-30 + 1))
p2 <- ggplot(data = df2, aes(x = time, y = score)) +
geom_line() + geom_point(size = 4, aes(color = value))
## check p2
p1
## check p2
p2

## binding
tracks(p1, p2)

## or
tks <- tracks(p1, p2)
tks

## combine
c(tks, tks)
tks + tks

cbind(tks, tks)
rbind(tks, tks) ## different wth c()!
library(grid)
x <- as(tks, "grob")
grid.draw(cbind(x, x))


## labeling: default labeling a named graphic
## simply pass a name with it
tracks(time1 = p1, time2 = p2)
## or pass a named list with it
lst <- list(time1 = p1, time2 = p2)
tracks(lst)

## more complicated case please use quotes
tracks(time1 = p1, "second time" = p2)

## set heights
tracks(time1 = p1, time2 = p2, heights = c(1, 3))

## if you want to disable label arbitrarily
## default label is always TRUE
labeled(p2)
labeled(p2) <- FALSE
## set labeled to FALSE, remove label even the plot has a name
tracks(time1 = p1, time2 = p2)
labeled(p2) <- TRUE

## fix a plot, not synchronize with other plots
p3 <- p1
## default is always FALSE
fixed(p3)
## set to TRUE
fixed(p3) <- TRUE
fixed(p3)

tracks(time1 = p1, time2 = p2, "time3(fixed)" = p3)


fixed(p3) <- FALSE
## otherwise you could run
%% tracks(time1 = p1, time2 = p2, "time3(fixed)" = p3, fixed = c(FALSE, FALSE, TRUE))


## control axis
hasAxis(p1)
hasAxis(p1) <- TRUE
# ready for weird looking
tracks(time1 = p1, time2 = p2)
# set it back
hasAxis(p1) <- FALSE



## mutable
mutable(p1)
tracks(time1 = p1, time2 = p2) + theme_bw()
mutable(p1) <- FALSE
# mutable for "+" method
tracks(time1 = p1, time2 = p2) + theme_bw()
mutable(p1) <- TRUE

## bgColor
bgColor(p1)
tracks(time1 = p1, time2 = p2)
bgColor(p1) <- "brown"
# mutable for "+" method
tracks(time1 = p1, time2 = p2)
# set it back
bgColor(p1) <- "white"

## apply a theme to each track
tks <- tracks(time1 = p1, time2 = p2) + theme_bw()
tks
reset(tks)

## store it with tracks
tks <- tracks(time1 = p1, time2 = p2, theme = theme_bw())
tks
tks <- tks + theme_gray()
tks
## reset will be introduced later
reset(tks)

## apply a pre-defiend theme for tracks!
tracks(time1 = p1, time2 = p2) + theme_tracks_sunset()
tracks(p1, p2) + theme_tracks_sunset()

## change limits
tracks(time1 = p1, time2 = p2) + xlim(c(1, 40))
tracks(time1 = p1, time2 = p2) + xlim(1, 40)
tracks(time1 = p1, time2 = p2) + coord_cartesian(xlim = c(1, 40))
# change y
tracks(time1 = p1, time2 = p2) + xlim(1, 40) + ylim(0, 10)
library(GenomicRanges)
gr <- GRanges("chr", IRanges(1, 40))
# GRanges
tracks(time1 = p1, time2 = p2) + xlim(gr)
# IRanges
tracks(time1 = p1, time2 = p2) + xlim(ranges(gr))
tks <- tracks(time1 = p1, time2 = p2)
xlim(tks)
xlim(tks) <- c(1, 35)
xlim(tks) <- gr
xlim(tks) <- ranges(gr)

## xlab, title
tracks(time1 = p1, time2 = p2, xlab = "time")
tracks(time1 = p1, time2 = p2, main = "title")
tracks(time1 = p1, time2 = p2, title = "title")
tracks(time1 = p1, time2 = p2, xlab = "time", title = "title") + theme_tracks_sunset()


## backup and restore
tks <- tracks(time1 = p1, time2 = p2)
tks
tks <- tks + xlim(1, 40)
tks
reset(tks)
tks <- tks + xlim(1, 40)
tks
tks <- backup(tks)
tks <- tks + theme_bw()
tks
reset(tks)

## padding(need to be fixed for more delicate control)
tracks(time1 = p1, time2 = p2, padding = 2)

## track color
tracks(time1 = p1, time2 = p2, track.bg.color = "yellow")
tracks(time1 = p1, time2 = p2, track.plot.color = c("yellow", "brown"))