Last data update: 2014.03.03

 R: Generic autoplot function
autoplotR Documentation

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

## S4 method for signature 'GRanges'
autoplot(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"))


## S4 method for signature 'GRangesList'
autoplot(object, ..., xlab, ylab, main, indName = "grl_name",
                 geom = NULL, stat = NULL, coverage.col = "gray50",
                 coverage.fill = coverage.col, group.selfish = FALSE)


## S4 method for signature 'IRanges'
autoplot(object, ..., xlab, ylab, main)

## S4 method for signature 'Seqinfo'
autoplot(object, ideogram = FALSE, ... )

## S4 method for signature 'GAlignments'
autoplot(object, ..., xlab, ylab, main, which,
      geom = NULL, stat = NULL)

## S4 method for signature 'BamFile'
autoplot(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)


## S4 method for signature 'character'
autoplot(object, ..., xlab, ylab, main, which)

## S4 method for signature 'TxDbOREnsDb'
autoplot(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)


## S4 method for signature 'BSgenome'
autoplot(object, which, ...,
                    xlab, ylab, main, geom = NULL)


## S4 method for signature 'Rle'
autoplot(object, ..., xlab, ylab, main, binwidth, nbin = 30,
          geom = NULL, stat = c("bin", "identity", "slice"),
          type = c("viewSums", "viewMins", "viewMaxs", "viewMeans"))

## S4 method for signature 'RleList'
autoplot(object, ..., xlab, ylab, main, nbin = 30, binwidth,
         facetByRow = TRUE, stat = c("bin", "identity", "slice"),
         geom = NULL, type = c("viewSums", "viewMins", "viewMaxs", "viewMeans"))

## S4 method for signature 'matrix'
autoplot(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)


## S4 method for signature 'ExpressionSet'
autoplot(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)


## S4 method for signature 'RangedSummarizedExperiment'
autoplot(object, ..., type = c("heatmap", "link", "pcp", "boxplot", "scatterplot.matrix"), pheno.plot = FALSE,
                 main_to_pheno = 4.5, padding = 0.2, assay.id = 1)


## S4 method for signature 'VCF'
autoplot(object, ...,
              xlab, ylab, main,
              assay.id,
              type = c("default", "geno", "info", "fixed"),
              full.string = FALSE,
              ref.show = TRUE,
              genome.axis = TRUE,
              transpose = TRUE)

## S4 method for signature 'OrganismDb'
autoplot(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")

## S4 method for signature 'VRanges'
autoplot(object, ...,which = NULL,
                             arrow = TRUE, indel.col = "gray30",
                             geom = NULL,
                             xlab, ylab, main)

## S4 method for signature 'TabixFile'
autoplot(object, which, ...)

Arguments

object

object to be plot.

columns

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


Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and IMSBIO