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.
columns passed to method works for TxDb, EnsDb and OrganismDb.
label.color
when label turned on for gene model, this parameter controls label color.
arrow
arrow passed to geome_alignment function to control intron arrow attributes.
indel.col
indel colors.
ideogram
Weather to call plotIdeogram or not, default is FALSE,
if TRUE, layout_karyogram will be called.
transpose
logical value, defaut TRUE, always make features from VCF as x, so
we can use it to map to genomic position.
axis.text.angle
axis text angle.
axis.text.x
logical value indicates whether to show x axis and labels or not.
axis.text.y
logical value indicates whether to show y axis and labels or not.
hjust
horizontal just for axis text.
rownames.label
logical value indicates whether to show rownames of matrix as y
label or not.
colnames.label
logical value indicates whether to show colnames of matrix as y
label or not.
na.value
color for NA value.
rotate
pheno.plot
show pheno plot or not.
main_to_pheno
main matrix plot width to pheno plot width ratio.
padding
padding between plots.
assay.id
index for assay you are going to use.
geom
Geom to use (Single character for now). Please see section Geometry
for details.
truncate.gaps
logical value indicate to truncate gaps or not.
truncate.fun
shrinkage function. Please see shrinkagefun in package biovizBase.
ratio
used in maxGap.
mode
Display mode for genomic features.
space.skip
space ratio between chromosome spaces in coordate genome.
coord
Coodinate system.
chr.weight
numeric vectors which sum to <1, the names of vectors has to be
matched with seqnames in seqinfo, and you can only specify part of the
seqnames, other lengths of chromosomes will be assined proportionally
to their seqlengths, for example, you could specify chr1 to be 0.5, so
the chr1 will take half of the space and other chromosomes squeezed to
take left of the space.
legend
A logical value indicates whether to show legend or not. Default is
TRUE
which
A GRanges object to subset the result, usually passed
to the ScanBamParam function.
show.coverage
A logical value indicates whether to show coverage or not. This is
used for geom "mismatch.summary".
resize.extra
A numeric value used to add buffer to intervals to compute stepping
levels on.
bsgenome
A BSgenome object. Only need for geom "mismatch.summary".
xlab
x label.
ylab
y label.
label
logic value, default TRUE. To show label by the side of features.
facetByRow
A logical value, default is TRUE ,facet RleList by row. If FALSE,
facet by column.
type
For Rle/RleList, "raw" plot everything, so be careful, that would be
pretty slow if you have too much data. For "viewMins", "viewMaxs",
"viewMeans", "viewSums", require extra arguments to slice the
object. so users need to at least provide lower, more details
and control please refer the the manual of slice function in
IRanges. For "viewMins", "viewMaxs", we use viewWhichMin and
viewWhichMax to get x scale, for "viewMeans", "viewSums", we
use window midpoint as x.
For ExpreesionSet, ploting types.
layout
Layout including linear, circular and karyogram. for GenomicRangesList, it only supports circular layout.
method
method used for parsing coverage from bam files. 'estimate' use fast
esitmated method and 'raw' use relatively slow parsing method.
test.method
test method
...
Extra parameters. Usually are those parameters used in autoplot to
control aesthetics or geometries.
main
title.
stat
statistical transformation.
indName
When coerce GRangesList to GRanges, names created for
each group.
coverage.col
coverage stroke color.
coverage.fill
coverage fill color.
group.selfish
Passed to addStepping, control whether to show each group as
unique level or not. If set to FALSE, if two groups are not
overlapped with each other, they will probably be layout in the same
level to save space.
names.expr
names expression used for creating labels. For
EnsDb objects either "tx_id",
"gene_name" or "gene_id".
binwidth
width of the bins.
nbin
number of bins.
genome.axis
logical value, if TRUE, whenever possible, try to parse genomic postition
for each column(e.g. RangedSummarizedExperiment), show column as exatcly
the genomic position instead of showing them side by side and
indexed from 1.
full.string
logical value. If TRUE, show full string of indels in plot for VCF.
ref.show
logical value. If TRUE, show REF in VCF at bottom track.
chr
characters indicates the seqnames to be subseted.
Value
A ggplot object, so you can use common features from ggplot2
package to manipulate the plot.
Introduction
autoplot is redefined as generic s4 method inside this package,
user could use autoplot in the way they are familiar with, and
we are also setting limitation inside this package, like
scales
X scales is always genomic coordinates in most cases, x could be
specified as start/end/midpoint when it's special geoms for
interval data like point/line
colors
Try to use default color scheme defined in biovizBase package
as possible as it can
Geometry
We have developed new geom for different objects, some of
them may require extra parameters you need to provide. Some of the
geom are more like geom + stat in ggplot2
package. e.g. "coverage.line" and "coverage.polygon".We simply combine
them together, but in the future, we plan to make it more general.
This package is designed for only biological data, especially genomic
data if users want to explore the data in a more flexible way, you
could simply coerce the GRanges to a data.frame, then
just use formal autoplot function in ggplot2, or autoplot
generic for data.frame.
Some objects share the same geom so we introduce all the geom together
in this section
full
Showing all the intervals as stepped rectangle, colored by strand
automatically.
For TxDb or EnsDb objects, showing full model.
segment
Showing all the intervals as stepped segments, colored by strand
automatically.
For object BSgenome, show nucleotides as colored segment.
For Rle/RleList, show histogram-like segments.
line
Showing interval as line, the interval data could also be just
single position when start = end, x is one of start/end/midpoint, y
value is unquoted name in elementMetadata column names. y value is
required.
point
Showing interval as point, the interval data could also be just
single position when start = end, x is one of start/end/midpoint, y
value is unquoted name in elementMetadata column names. y value is
required.
For object BSgenome, show nucleotides as colored point.
coverage.line
Coverage showing as lines for interval data.
coverage.polygon
Coverage showing as polygon for interval data.
splice
Splicing summary. The size and width of the line and rectangle
should represent the counts in each model. Need to provide model.
single
For TxDb or EnsDb objects, showing
single(reduced) model only.
tx
For TxDb or EnsDb objects, showing
transcirpts isoforms.
mismatch.summary
Showing color coded mismatched stacked bar to indicate the proportion
of mismatching at each position, the reference is set to gray.
text
For object BSgenome, show nucleotides as colored text.
rectangle
For object BSgenome, show nucleotides as colored rectangle.
Faceting
Faceting in ggbio package is a little differnt from ggplot2 in several ways
The faceted column could only be seqnames or regions
on the genome. So we limited the formula passing to facet argument,
e.g something ~ seqnames, is accepted formula, you can change
"something" to variable name in the elementMetadata. But you
can not change the second part.
Sometime, we need to view different regions, so we also have a
facet_gr argument which accept a GRanges. If
this is provided, it will override the default seqnames and use
provided region to facet the graphics, this might be useful for
different gene centric views.
Author(s)
Tengfei Yin
Examples
library(ggbio)
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")
p2 <- autoplot(ensdb, which=GenenameFilter("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, "overlapping"), 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, "overlapping"), 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
###################################################
## Not run:
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
## End(Not run)
###################################################
### 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()
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(ggbio)
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Loading required package: ggplot2
Need specific help about ggbio? try mailing
the maintainer or visit http://tengfei.github.com/ggbio/
Attaching package: 'ggbio'
The following objects are masked from 'package:ggplot2':
geom_bar, geom_rect, geom_segment, ggsave, stat_bin, stat_identity,
xlim
Warning message:
replacing previous import 'ggplot2::Position' by 'BiocGenerics::Position' when loading 'ggbio'
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/ggbio/autoplot-method.Rd_%03d_medium.png", width=480, height=480)
> ### Name: autoplot
> ### Title: Generic autoplot function
> ### Aliases: autoplot autoplot,GRanges-method autoplot,GRangesList-method
> ### autoplot,IRanges-method autoplot,Seqinfo-method
> ### autoplot,BSgenome-method autoplot,GAlignments-method
> ### autoplot,BamFile-method autoplot,BamFileList-method
> ### autoplot,TxDbOREnsDb-method autoplot,character-method
> ### autoplot,Rle-method autoplot,RleList-method autoplot,matrix-method
> ### autoplot,Views-method autoplot,ExpressionSet-method
> ### autoplot,RangedSummarizedExperiment-method autoplot,VCF-method
> ### autoplot,OrganismDb-method autoplot,VRanges-method
> ### autoplot,TabixFile-method +,Bioplot,Any-method
>
> ### ** Examples
>
> library(ggbio)
>
> set.seed(1)
> N <- 1000
> library(GenomicRanges)
Loading required package: S4Vectors
Loading required package: stats4
Attaching package: 'S4Vectors'
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: IRanges
Loading required package: GenomeInfoDb
> 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)
Warning message:
In .Seqinfo.mergexy(x, y) :
The 2 combined objects have no sequence levels in common. (Use
suppressWarnings() to suppress this warning.)
> head(gr.b)
GRanges object with 6 ranges and 2 metadata columns:
seqnames ranges strand | score value
<Rle> <IRanges> <Rle> | <numeric> <numeric>
[1] chr1 [ 1, 9] * | 8.31857306034336 89.0643922903109
[2] chr1 [11, 19] * | 9.30946753155016 69.5875372095034
[3] chr1 [21, 28] * | 14.6761249424474 64.4101745630614
[4] chr1 [31, 38] * | 10.2115251742737 99.4327078857459
[5] chr1 [41, 44] * | 10.3878632054828 65.9148741124664
[6] chr1 [51, 56] * | 15.1451949606498 71.144516348606
-------
seqinfo: 2 sequences from an unspecified genome; no seqlengths
>
>
> ###################################################
> ### code chunk number 5: bar-default
> ###################################################
> p1 <- autoplot(gr.b, geom = "bar")
use score as y by default
> ## use value to fill the bar
> p2 <- autoplot(gr.b, geom = "bar", aes(fill = value))
use score as y by default
> 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))
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
>
>
> ###################################################
> ### 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
Seqinfo object with 93 sequences from hg19 genome:
seqnames seqlengths isCircular genome
chr1 249250621 <NA> hg19
chr1_gl000191_random 106433 <NA> hg19
chr1_gl000192_random 547496 <NA> hg19
chr2 243199373 <NA> hg19
chr3 198022430 <NA> hg19
... ... ... ...
chrUn_gl000246 38154 <NA> hg19
chrUn_gl000247 36422 <NA> hg19
chrUn_gl000248 39786 <NA> hg19
chrUn_gl000249 38502 <NA> hg19
chrM 16571 <NA> hg19
>
>
> ###################################################
> ### 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
IRanges object with 100 ranges and 4 metadata columns:
start end width | value score sample
<integer> <integer> <integer> | <numeric> <numeric> <character>
[1] 80 152 73 | 8.138900 112.28206 Tumor
[2] 112 183 72 | 10.126348 150.66620 Tumor
[3] 172 242 71 | 7.267235 147.59765 Normal
[4] 273 347 75 | 10.474086 90.07277 Tumor
[5] 61 133 73 | 8.036246 31.44293 Tumor
... ... ... ... . ... ... ...
[96] 240 312 73 | 6.856047 104.03343 Tumor
[97] 137 206 70 | 14.323473 122.96797 Tumor
[98] 124 198 75 | 6.952458 128.65410 Normal
[99] 244 314 71 | 11.235924 98.48303 Tumor
[100] 182 255 74 | 8.856772 90.82554 Normal
pair
<character>
[1] y
[2] x
[3] t
[4] r
[5] q
... ...
[96] q
[97] g
[98] n
[99] u
[100] j
>
>
> ###################################################
> ### 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 ~. )
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
> p4 <- autoplot(ir, stat = "reduce")
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
> 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
integer-Rle of length 10000 with 823 runs
Lengths: 779 1 208 1 1599 1 883 ... 1 5 2 9 1 4507
Values : 0 1 0 1 0 1 0 ... 1 0 1 0 1 0
>
>
> ###################################################
> ### code chunk number 19: rle-bin
> ###################################################
> p1 <- autoplot(xRle)
Default use binwidth: range/30
> p2 <- autoplot(xRle, nbin = 80)
Default use binwidth: range/80
> p3 <- autoplot(xRle, geom = "heatmap", nbin = 200)
Default use binwidth: range/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
RleList of length 2
[[1]]
integer-Rle of length 10000 with 823 runs
Lengths: 779 1 208 1 1599 1 883 ... 1 5 2 9 1 4507
Values : 0 1 0 1 0 1 0 ... 1 0 1 0 1 0
[[2]]
integer-Rle of length 10000 with 823 runs
Lengths: 779 1 208 1 1599 1 883 ... 1 5 2 9 1 4507
Values : 0 2 0 2 0 2 0 ... 2 0 2 0 2 0
>
>
> ###################################################
> ### code chunk number 23: rlel-bin
> ###################################################
> p1 <- autoplot(xRleList)
Default use binwidth: range/30
> p2 <- autoplot(xRleList, nbin = 80)
Default use binwidth: range/80
> p3 <- autoplot(xRleList, geom = "heatmap", nbin = 200)
Default use binwidth: range/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)
Loading required package: GenomicFeatures
Loading required package: AnnotationDbi
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
> 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")
Parsing transcripts...
Parsing exons...
Parsing cds...
Parsing utrs...
------exons...
------cdss...
------introns...
------utr...
aggregating...
Done
"gap" not in any of the valid gene feature terms "cds", "exon", "utr"
Constructing graphics...
> p2 <- autoplot(txdb, which = genesymbol["ALDOA"], stat = "reduce", color = "brown",
+ fill = "brown")
Parsing transcripts...
Parsing exons...
Parsing cds...
Parsing utrs...
------exons...
------cdss...
------introns...
------utr...
aggregating...
Done
"gap" not in any of the valid gene feature terms "cds", "exon", "utr"
Constructing graphics...
reduce alignemnts...
> tracks(full = p1, reduce = p2, heights = c(5, 1)) + ylab("")
>
>
> ###################################################
> ### EnsDb
> ###################################################
> ## Fetching gene models from an EnsDb object.
> library(EnsDb.Hsapiens.v75)
Loading required package: ensembldb
> 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")
Fetching data...OK
Parsing exons...OK
Defining introns...OK
Defining UTRs...OK
Defining CDS...OK
aggregating...
Done
"gap" not in any of the valid gene feature terms "cds", "exon", "utr"
Constructing graphics...
> p2 <- autoplot(ensdb, which=GenenameFilter("ALDOA"), stat="reduce", color="brown",
+ fill="brown")
Fetching data...OK
Parsing exons...OK
Defining introns...OK
Defining UTRs...OK
Defining CDS...OK
aggregating...
Done
"gap" not in any of the valid gene feature terms "cds", "exon", "utr"
Constructing graphics...
reduce alignemnts...
> 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, "overlapping"), names.expr="gene_name")
Fetching data...OK
Parsing exons...OK
Defining introns...OK
Defining UTRs...OK
Defining CDS...OK
aggregating...
Done
"gap" not in any of the valid gene feature terms "cds", "exon", "utr"
Constructing graphics...
> ## Just submitting the GRanges object also works.
> autoplot(ensdb, gr, names.expr="gene_name")
Fetching data...OK
Parsing exons...OK
Defining introns...OK
Defining UTRs...OK
Defining CDS...OK
aggregating...
Done
"gap" not in any of the valid gene feature terms "cds", "exon", "utr"
Constructing graphics...
>
> ## Or genes encoded on both strands.
> gr <- GRanges(seqnames=16, IRanges(30768000, 30770000), strand="*")
> autoplot(ensdb, GRangesFilter(gr, "overlapping"), names.expr="gene_name")
Fetching data...OK
Parsing exons...OK
Defining introns...OK
Defining UTRs...OK
Defining CDS...OK
aggregating...
Done
"gap" not in any of the valid gene feature terms "cds", "exon", "utr"
Constructing graphics...
>
> ## 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")))
Fetching data...OK
Parsing exons...OK
Defining introns...OK
Defining UTRs...OK
Defining CDS...OK
aggregating...
Done
"gap" not in any of the valid gene feature terms "cds", "exon", "utr"
Constructing graphics...
>
> ###################################################
> ### code chunk number 28: ga-load
> ###################################################
> library(GenomicAlignments)
Loading required package: SummarizedExperiment
Loading required package: Biostrings
Loading required package: XVector
Loading required package: Rsamtools
> 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")
extracting information...
> p3 <- autoplot(ga, geom = "line", stat = "coverage")
extracting information...
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
> 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))
reading in
use score as y by default
>
>
> ###################################################
> ### 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()
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
>
> rownames(volcano) <- letters[sample(1:24, size = nrow(volcano), replace = TRUE)]
> autoplot(volcano)
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
>
> ## even with row/col names, you could also disable it and just use numeric index
> autoplot(volcano, colnames.label = FALSE)
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
> autoplot(volcano, rownames.label = FALSE, colnames.label = FALSE)
>
> ## don't want the axis has label??
> autoplot(volcano, axis.text.x = FALSE)
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
> autoplot(volcano, axis.text.y = FALSE)
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
>
>
> # 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)
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
> ## tweak with it.
> autoplot(volcano, axis.text.angle = -45, hjust = 0)
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
>
> ## 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")
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
> colnames(mx) <- LETTERS[1:20]
> autoplot(mx, color = "white")
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
> autoplot(mx, color = "white", size = 2)
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
> ## weird in aes(), though works
> ## default tile is flexible
> autoplot(mx, aes(width = 0.6, height = 0.6))
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
> autoplot(mx, aes(width = 0.6, height = 0.6), na.value = "white")
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
> autoplot(mx, aes(width = 0.6, height = 0.6)) + theme_clear()
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
>
> ###################################################
> ### 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)
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
> autoplot(v1, geom = "tile", aes(width = 0.5, height = 0.5))
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
> 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
ExpressionSet (storageMode: lockedEnvironment)
assayData: 500 features, 26 samples
element names: exprs, se.exprs
protocolData: none
phenoData
sampleNames: A B ... Z (26 total)
varLabels: sex type score
varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: hgu95av2
> set.seed(1)
> ## select 50 features
> idx <- sample(seq_len(dim(sample.ExpressionSet)[1]), size = 50)
> eset <- sample.ExpressionSet[idx,]
> eset
ExpressionSet (storageMode: lockedEnvironment)
assayData: 50 features, 26 samples
element names: exprs, se.exprs
protocolData: none
phenoData
sampleNames: A B ... Z (26 total)
varLabels: sex type score
var