| Title: | Basic graphic utilities for visualization of genomic data. |
|---|---|
| Description: | The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency. |
| Authors: | Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] |
| Maintainer: | Michael Lawrence <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.53.0 |
| Built: | 2024-07-13 05:23:20 UTC |
| Source: | https://github.com/bioc/biovizBase |
biovizBase is a package which provides utilities and color scheme for higher level graphic package which aim to visualize biological data especially genetic data.
This package provides default color scheme for nucleotide, strand, amino acid, try to pass colorblind checking as possible as we can. And also provide giemsa stain result color scheme used to show cytoband. This package also provides utilites to manipulate and summarize raw data to get them ready to be visualized.
Adding disjoint levels to a GenomicRanges object
## S4 method for signature 'GenomicRanges' addStepping(obj, group.name, extend.size = 0, fix = "center", group.selfish = TRUE)## S4 method for signature 'GenomicRanges' addStepping(obj, group.name, extend.size = 0, fix = "center", group.selfish = TRUE)
obj |
A GenomicRanges object |
group.name |
Column name in the elementMetadata which specify the grouping information of all the entries. If provided, this will make sure all intervals belong to the same group will try to be on the same level and nothing falls in between. |
extend.size |
Adding invisible buffered region to the GenomicRanges object, if it's 10, then adding 5 at both end. This make the close neighbors assigned to the different levels and make your eyes easy to identify. |
fix |
"start", "end", or "center"(default) passed to |
group.selfish |
Passed to |
This is a tricky question, for example, pair-end RNA-seq data could be represented as two set of GenomicRanges object, one indicates the read, one indicates the junction. At the same time, we need to make sure pair-ended read are shown on the same level, and nothing falls in between. For better visualization of the data, we may hope to add invisible extended buffer to the reads, so closely neighbored reads will be on the different levels.
A modified GenmicRanges object with stepping as one column.
Tengfei Yin
library(GenomicRanges) set.seed(1) N <- 500 ## sample GRanges gr <- GRanges(seqnames = sample(c("chr1", "chr2", "chr3", "chrX", "chrY"), 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), group = sample(c("Normal", "Tumor"), size = N, replace = TRUE), pair = sample(letters, size = N, replace = TRUE)) ## grouping and extending head(addStepping(gr)) head(addStepping(gr, group.name = "pair")) gr.close <- GRanges(c("chr1", "chr1"), IRanges(c(10, 20), width = 9)) addStepping(gr.close) addStepping(gr.close, extend.size = 5)library(GenomicRanges) set.seed(1) N <- 500 ## sample GRanges gr <- GRanges(seqnames = sample(c("chr1", "chr2", "chr3", "chrX", "chrY"), 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), group = sample(c("Normal", "Tumor"), size = N, replace = TRUE), pair = sample(letters, size = N, replace = TRUE)) ## grouping and extending head(addStepping(gr)) head(addStepping(gr, group.name = "pair")) gr.close <- GRanges(c("chr1", "chr1"), IRanges(c(10, 20), width = 9)) addStepping(gr.close) addStepping(gr.close, extend.size = 5)
This function help users create a function based on specified color blind safe palette. And the returned function could be used for color generation.
genDichromatPalInfo() genBrewerBlindPalInfo() genBlindPalInfo() colorBlindSafePal(palette) blind.pal.info brewer.pal.blind.info dichromat.pal.blind.infogenDichromatPalInfo() genBrewerBlindPalInfo() genBlindPalInfo() colorBlindSafePal(palette) blind.pal.info brewer.pal.blind.info dichromat.pal.blind.info
palette |
A index numeric value or character. Please see
|
We get those color-blind safe palette based on http://colorbrewer2.org/ and http://geography.uoregon.edu/datagraphics/color_scales.htm those color are implemented in two packages, RColorBrewer and dichromat. But RColorBrewer doesn't provide subset of color-blind safe palette info. And dichromat doesn't group color based on "quality", "sequential" and "divergent", so we pick those color manually and create a combined palette, blind.pal.info.
colorBlindSafePal will return a function, this function will
take two arguments, n and repeatable. if n is smaller than
3(n >= 3 is required by RColorBrewer), we use 3 instead and return a
color vector. If n is over the maxcolors column in blind.pal.info, we
use maxcolors instead when repeatable set to FALSE, if
repeatable set to TRUE we repeat the color of all the
allowed colors(length equals maxcolors) in the same order. This has
special case in certain graphics which is always displayed side by
side and don't worry about the repeated colors being neighbors.
genBrewerBlindPalInfo return brewer.pal.blind.info data
frame containing all color-blind safe palettes from
brewer.pal.info defined in RColorBrewer, but it's not only just
subset of it, it also changes some maxcolors info.
genDichromatPalInfo return dichromat.pal.blind.info data
frame.
genBlindPalInfo return blind.pal.info data frame.
A color generating function with arguments n and
repeatable. n specifying how many different discrete colors
you want to get from them palette, and if repeatable turned on and set
to TRUE, you can specify n even larger than maximum color. The color
will be repeated following the same order.
Tengfei Yin
## Not run: library(scales) ## brewer subse of only color blind palette brewer.pal.blind.info genBrewerBlindPalInfo() ## dichromat info dichromat.pal.blind.info genDichromatPalInfo() ## all color blind palette, adding id/pkg. blind.pal.info ## with no parameters, just return blind.pal.info colorBlindSafePal() mypal <- colorBlindSafePal(20) ## or pass character name mypal <- colorBlindSafePal("Set2") mypal12 <- colorBlindSafePal(22) show_col(mypal(12, repeatable = FALSE)) # warning show_col(mypal(11, repeatable = TRUE)) # no warning, and repeat show_col(mypal12(12)) ## End(Not run)## Not run: library(scales) ## brewer subse of only color blind palette brewer.pal.blind.info genBrewerBlindPalInfo() ## dichromat info dichromat.pal.blind.info genDichromatPalInfo() ## all color blind palette, adding id/pkg. blind.pal.info ## with no parameters, just return blind.pal.info colorBlindSafePal() mypal <- colorBlindSafePal(20) ## or pass character name mypal <- colorBlindSafePal("Set2") mypal12 <- colorBlindSafePal(22) show_col(mypal(12, repeatable = FALSE)) # warning show_col(mypal(11, repeatable = TRUE)) # no warning, and repeat show_col(mypal12(12)) ## End(Not run)
Test if a string contain any letters
containLetters(obj, all=FALSE)containLetters(obj, all=FALSE)
obj |
String |
all |
If set to FALSE, return TRUE when any letters appears; if all is set to TRUE, return TRUE only when the string is composed of just letters. |
Useful when processing/sorting seqnames
Logical value
tengfei
containLetters("XYZ123") containLetters("XYZ123", TRUE)containLetters("XYZ123") containLetters("XYZ123", TRUE)
Genomic sequencing of colorectal adenocarcinomas identifies a recurrent VTI1A-TCF7L2 fusion sample data set
data(CRC)data(CRC)
The data used in this study is from this a paper http://www.nature.com/ng/journal/v43/n10/full/ng.936.html.
data(CRC)data(CRC)
CRC-1 mutation and structural rearrangment
data(crc1.GeRL)data(crc1.GeRL)
GenomicRangesList contains somatic mutation, reaarangment etc for a tumor sample, please check the reference for detials.
Genomic sequencing of colorectal adenocarcinomas identifies a recurrent VTI1A-TCF7L2 fusion
data(crc1.GeRL) crc1.GeRLdata(crc1.GeRL) crc1.GeRL
Fetching Granges from various data source, currently supported objects are TxDb, EnsDb, GAlignments and BamFile.
## S4 method for signature 'TxDb' crunch(obj, which, columns = c("tx_id", "tx_name","gene_id"), type = c("all", "reduce"), truncate.gaps = FALSE, truncate.fun = NULL, ratio = 0.0025) ## S4 method for signature 'EnsDb' crunch(obj, which, columns = c("tx_id", "gene_name","gene_id"), type = c("all", "reduce"), truncate.gaps = FALSE, truncate.fun = NULL, ratio = 0.0025) ## S4 method for signature 'GAlignments' crunch(obj, which, truncate.gaps = FALSE, truncate.fun = NULL, ratio = 0.0025) ## S4 method for signature 'BamFile' crunch(obj, which, ..., type = c("gapped.pair", "raw", "all"), truncate.gaps = FALSE, truncate.fun = NULL, ratio = 0.0025)## S4 method for signature 'TxDb' crunch(obj, which, columns = c("tx_id", "tx_name","gene_id"), type = c("all", "reduce"), truncate.gaps = FALSE, truncate.fun = NULL, ratio = 0.0025) ## S4 method for signature 'EnsDb' crunch(obj, which, columns = c("tx_id", "gene_name","gene_id"), type = c("all", "reduce"), truncate.gaps = FALSE, truncate.fun = NULL, ratio = 0.0025) ## S4 method for signature 'GAlignments' crunch(obj, which, truncate.gaps = FALSE, truncate.fun = NULL, ratio = 0.0025) ## S4 method for signature 'BamFile' crunch(obj, which, ..., type = c("gapped.pair", "raw", "all"), truncate.gaps = FALSE, truncate.fun = NULL, ratio = 0.0025)
obj |
supported objects are |
which |
GRanges object. For TxDb object, could aslo be a list.
For |
columns |
columns to include in the output. |
type |
default 'all' is to show the full model, 'reduce' is to show a single model. |
truncate.gaps |
logical value, default |
truncate.fun |
shrinkage function. |
ratio |
numeric value, shrinking ratio. |
... |
arguments passed to function |
GRanges object.
Tengfei Yin
library(biovizBase) library(TxDb.Hsapiens.UCSC.hg19.knownGene) data(genesymbol, package = "biovizBase") txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene obj <- txdb temp <- crunch(txdb, which = genesymbol["BRCA1"], type = "all") temp <- crunch(txdb, which = genesymbol["BRCA1"], type = "reduce") ## Fetching data from a EnsDb object. library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 gr <- genesymbol["BRCA1"] seqlevels(gr) <- sub(seqlevels(gr), pattern="chr", replacement="") temp <- crunch(edb, which = gr) ## Alternatively, use the GenenameFilter from the AnnotationFilter package: temp <- crunch(edb, which = GenenameFilter("BRCA1")) ## Or a filter expression temp <- crunch(edb, which = ~ genename == "BRCA1")library(biovizBase) library(TxDb.Hsapiens.UCSC.hg19.knownGene) data(genesymbol, package = "biovizBase") txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene obj <- txdb temp <- crunch(txdb, which = genesymbol["BRCA1"], type = "all") temp <- crunch(txdb, which = genesymbol["BRCA1"], type = "reduce") ## Fetching data from a EnsDb object. library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 gr <- genesymbol["BRCA1"] seqlevels(gr) <- sub(seqlevels(gr), pattern="chr", replacement="") temp <- crunch(edb, which = gr) ## Alternatively, use the GenenameFilter from the AnnotationFilter package: temp <- crunch(edb, which = GenenameFilter("BRCA1")) ## Or a filter expression temp <- crunch(edb, which = ~ genename == "BRCA1")
Subset of RNA editing sites in hg19
data(darned_hg19_subset500)data(darned_hg19_subset500)
This data set provides a subset(500 sites only) of hg19 RNA editing sites, and originally from DARNED http://darned.ucc.ie/ for the hg19 assembly.
data(darned_hg19_subset500) darned_hg19_subset500data(darned_hg19_subset500) darned_hg19_subset500
Estimation of Coverage
## S4 method for signature 'BamFile' estimateCoverage(x, maxBinSize = 2^14)## S4 method for signature 'BamFile' estimateCoverage(x, maxBinSize = 2^14)
x |
A |
maxBinSize |
Max bin size. |
A GRanges object.
Michael Lawrence
Transform GRangesList to GRanges.
flatGrl(object, indName = "grl_name")flatGrl(object, indName = "grl_name")
object |
a |
indName |
column named by 'indName' that groups the records by their original element in 'object'. |
This method is different from default stack, it integrate
elementMetadata of GRangesList to the final coerced GRanges.
A GRanges object.
Tengfei Yin
library(GenomicRanges) gr1 <- GRanges("chr1", IRanges(1:10, width = 5)) gr2 <- GRanges("chr2", IRanges(1:10, width = 5)) obj <- GRangesList(gr1, gr2) values(obj) <- data.frame(a = 1:2, b = letters[1:2]) stack(obj) flatGrl(obj)library(GenomicRanges) gr1 <- GRanges("chr1", IRanges(1:10, width = 5)) gr2 <- GRanges("chr2", IRanges(1:10, width = 5)) obj <- GRangesList(gr1, gr2) values(obj) <- data.frame(a = 1:2, b = letters[1:2]) stack(obj) flatGrl(obj)
Compute GC content in a certain region of a BSgenome object
GCcontent(obj, ..., view.width, as.prob = TRUE)GCcontent(obj, ..., view.width, as.prob = TRUE)
obj |
BSgenome object |
... |
Arguments passed to getSeq method for BSgenome package. |
view.width |
Passed to |
as.prob |
If TRUE return percentage of GC content, otherwise return counts. |
GC content is an interesting variable may be related to various
biological questions. So we need a way to compute GC content in a
certain region of a reference genome. GCcontent function is a
wrapper around getSeq function in BSgenome package and
letterFrequency, letterFrequencyInSlidingView
in Biostrings package.
if the view.width is specified, the GC content will be computed
in the sliding view
Numeric value indicate count or percentage
Tengfei Yin
library(BSgenome.Hsapiens.UCSC.hg19) GCcontent(Hsapiens, GRanges("chr1", IRanges(1e6, 1e6 + 1000)))library(BSgenome.Hsapiens.UCSC.hg19) GCcontent(Hsapiens, GRanges("chr1", IRanges(1e6, 1e6 + 1000)))
Gene symbols with position
data(genesymbol)data(genesymbol)
This data set provides genen symbols in human with position and starnd information, stored as GRanges object.
data(genesymbol) head(genesymbol) genesymbol["RBM17"]data(genesymbol) head(genesymbol) genesymbol["RBM17"]
This function tries to get default color scheme either from fixed palette or options for specified data set, usually just biological data.
getBioColor(type = c("DNA_BASES_N", "DNA_BASES", "DNA_ALPHABET", "RNA_BASES_N", "RNA_BASES", "RNA_ALPHABET", "IUPAC_CODE_MAP", "AMINO_ACID_CODE", "AA_ALPHABET", "STRAND", "CYTOBAND"), source = c("option", "default"))getBioColor(type = c("DNA_BASES_N", "DNA_BASES", "DNA_ALPHABET", "RNA_BASES_N", "RNA_BASES", "RNA_ALPHABET", "IUPAC_CODE_MAP", "AMINO_ACID_CODE", "AA_ALPHABET", "STRAND", "CYTOBAND"), source = c("option", "default"))
type |
Color set based on which you want to get. |
source |
"option" tries to get color scheme from |
Most data set specified in the type argument are defined in Biostrings package, such as "DNA_BASES", "DNA_ALPHABET", "RNA_BASES", "RNA_ALPHABET", "IUPAC_CODE_MAP", "AMINO_ACID_CODE", "AA_ALPHABET", please check the manual for more details.
"DNA_BASES_N" is just "DNA_BASES" with extra "N" used in certain
cases, like the result from applyPileup in Rsamtools package.
We start with the five most used nucleotides, A,T,C,G,N, In
genetics, GC-content usually has special biological
significance because GC pair is bound by three hydrogen bonds instead
of two like AT pairs. So it has higher thermostability which could
result in different significance, like higher annealing temperature in
PCR. So we hope to choose warm color for G,C and cold color for
A,T, and a color in between to represent N. They are
chosen from diverging color set of color brewer. So we should
be able to easily tell the GC enriched region. This set of color also
passed vischeck for colorblind people.
In GRanges object, we have strand which contains three
levels, +, -, *. We are using a qualitative color set from Color
Brewer. This color set pass the colorblind test. It should be a safe
color set to use to color strand.
For most default color scheme if they are under 18, we are trying to use package dichromat to set color for color blind people. But for data set that contains more than 18 objects, it's not possible to assign colorblind-safe color to them anymore. So we need to repeat some color. It should not matter too much, because even normal people cannot tell the difference anymore.
Here are the definition for the data sets.
Contains A,C,T,G.
This alphabet contains all letters from the IUPAC Extended Genetic Alphabet (see "?IUPAC_CODE_MAP") + the gap ("-") and the hard masking ("+") letters.
Contains A,C,T,G,N
Contains A,C,U,G,N
Contains A,C,T,G
This alphabet contains all letters from the IUPAC Extended Genetic Alphabet (see ?IUPAC_CODE_MAP) where "T" is replaced by "U" + the gap ("-") and the hard masking ("+") letters.
The "IUPAC_CODE_MAP" named character vector contains the mapping from the IUPAC nucleotide ambiguity codes to their meaning.
Single-Letter Amino Acid Code (see "?AMINO_ACID_CODE").
This alphabet contains all letters from the Single-Letter Amino Acid Code (see "?AMINO_ACID_CODE") + the stop ("*"), the gap ("-") and the hard masking ("+") letters
Contains "+", "-", "*"
Contains giemsa stain results:gneg, gpos25, gpos50, gpos75,
gpos100, gvar, stalk, acen. Color defined in package geneplotter.
A character of vector contains color(rgb), and the names of the vector
is originally from the name of different data set. e.g. for DNA_BASES,
it's just A,C,T,G. This allow users to get color for a vector
of specified names. Please see the examples below.
Tengfei Yin
opts <- getOption("biovizBase") opts$DNABasesNColor[1] <- "red" options(biovizBase = opts) ## get from option(default) getBioColor("DNA_BASES_N") ## get default fixed color getBioColor("DNA_BASES_N", source = "default") seqs <- c("A", "C", "T", "G", "G", "G", "C") ## get colors for a sequence. getBioColor("DNA_BASES_N")[seqs]opts <- getOption("biovizBase") opts$DNABasesNColor[1] <- "red" options(biovizBase = opts) ## get from option(default) getBioColor("DNA_BASES_N") ## get default fixed color getBioColor("DNA_BASES_N", source = "default") seqs <- c("A", "C", "T", "G", "G", "G", "C") ## get colors for a sequence. getBioColor("DNA_BASES_N")[seqs]
Get formals from functions, used for dispatching arguments inside.
getFormalNames(..., remove.dots = TRUE)getFormalNames(..., remove.dots = TRUE)
... |
functions. |
remove.dots |
logical value, indicate remove dots in formals or not, default is |
A character vector for formal arguments.
Tengfei Yin
getFormalNames(plot, sapply)getFormalNames(plot, sapply)
Get gaps for a stepping transformed GRanges object, for visualization purpose. So a extra "stepping" column is required. Please see details below for motivation.
## S4 method for signature 'GRanges' getGaps(obj, group.name = NULL, facets = NULL)## S4 method for signature 'GRanges' getGaps(obj, group.name = NULL, facets = NULL)
obj |
A |
group.name |
group name, such as transcript ID, this is the group method within each panel of facets and gaps will be computed for each group of intervals. |
facets |
formula used for creating graphics, all variables must be present in the data. |
Since faceting is a subset and group process in visualization
stage, some statistical computation need to be taken place after
that. This leaves some computation like computing gaps hard based on
solely GRanges object. Extra information like facets formula
and group method would help to generate gaps which make sure they are
aligned on the same level and within the same panel for grouped
intervals. facets variables will be added to gaps GRanges along
with group.name.
A GRanges object representing gaps and for each row, the
"stepping" column help later visualization and make sure gaps and
intervals they are generated from are showed on the expected place.
Tengfei Yin
set.seed(1) N <- 100 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)) grl <- splitByFacets(gr, sample ~ seqnames) gr <- unlist(endoapply(grl, addStepping)) gr.gaps <- getGaps(gr, group.name = "stepping", facets = sample ~ seqnames)set.seed(1) N <- 100 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)) grl <- splitByFacets(gr, sample ~ seqnames) gr <- unlist(endoapply(grl, addStepping)) gr.gaps <- getGaps(gr, group.name = "stepping", facets = sample ~ seqnames)
This function tries to parse ideogram information from
seqlengths of a GRanges object. This information is
usually used to plot chromosome background for kaytogram or esitmate
proper lengths of chromosomes from data space for showing overview.
getIdeoGR(gr)getIdeoGR(gr)
gr |
A |
A ideogram GRanges object, each row indicate one single
chromosome, and start with 1 and end with real chromosome length or
estimated lengths.
Tengfei Yin
library(GenomicRanges) data("hg19IdeogramCyto", package = "biovizBase") hg19IdeogramCyto ## without seqlengths, simply reduce getIdeoGR(hg19IdeogramCyto) ## with seqlengths gr <- GRanges("chr1", IRanges(1,3)) seqlevels(gr) <- c("chr1", "chr2", "chr3") nms <- c(100, 200, 300) names(nms) <- c("chr1", "chr2", "chr3") seqlengths(gr) <- nms gr getIdeoGR(gr)library(GenomicRanges) data("hg19IdeogramCyto", package = "biovizBase") hg19IdeogramCyto ## without seqlengths, simply reduce getIdeoGR(hg19IdeogramCyto) ## with seqlengths gr <- GRanges("chr1", IRanges(1,3)) seqlevels(gr) <- c("chr1", "chr2", "chr3") nms <- c(100, 200, 300) names(nms) <- c("chr1", "chr2", "chr3") seqlengths(gr) <- nms gr getIdeoGR(gr)
Get ideogram w/o cytoband for certain genome
getIdeogram(genome, subchr, cytobands=TRUE)getIdeogram(genome, subchr, cytobands=TRUE)
genome |
Single specie names, which must be one of the result from
|
subchr |
A character vector used to subset the result. |
cytobands |
If TRUE, return ideogram with gieStain and name column. If FALSE, simply return the genome information for each chromosome. |
This function require a network connection, it will parse the data on the fly, function is a wrapper of some functionality from rtracklayer package to get certain table like cytoBand, a full table schema could be found http://genome.ucsc.edu/cgi-bin/hgTables in UCSC genome browser.This is useful for visualization of the whole genome or single chromosome, you can see some examples in ggbio package.
A GRanges object.
Tengfei Yin
## Not run: hg19IdeogramCyto <- getIdeogram("hg19", cytoband = TRUE)## Not run: hg19IdeogramCyto <- getIdeogram("hg19", cytoband = TRUE)
Trying to get scale information from a GRanges object, used for
circular view for geom "scale".
getScale(gr, unit = NULL, n = 100, type = c("M", "B", "sci"))getScale(gr, unit = NULL, n = 100, type = c("M", "B", "sci"))
gr |
a |
unit |
A numeric value for scale unit. Default |
n |
Integer value to indicate how many scale ticks to make. |
type |
unit types to shown. |
A GRanges object, with extra column: "type" indicate it's longer
major ticks or shorter minor ticks. "scale.y" indicates y height for
major and minor ticks. Default ratio is 3:1.
Tengfei Yin
get x scale breaks and labels for GRanges with different coordintes(currently only "truncate_gaps" and "genome" supported).
## S4 method for signature 'GRanges' getXScale(obj, type = c("default", "all", "left", "right"))## S4 method for signature 'GRanges' getXScale(obj, type = c("default", "all", "left", "right"))
obj |
a |
type |
types of labels for transformed data. |
list of breaks and labels.
Tengfei Yin
library(GenomicRanges) gr1 <- GRanges("chr1", IRanges(start = c(100, 300, 600), end = c(200, 400, 800))) shrink.fun1 <- shrinkageFun(gaps(gr1), max.gap = maxGap(gaps(gr1), 0.15)) shrink.fun2 <- shrinkageFun(gaps(gr1), max.gap = 0) s1 <- shrink.fun1(gr1) getXScale(s1) # coord:genome set.seed(1) gr1 <- GRanges("chr1", IRanges(start = as.integer(runif(20, 1, 100)), width = 5)) gr2 <- GRanges("chr2", IRanges(start = as.integer(runif(20, 1, 100)), width = 5)) gr <- c(gr1, gr2) gr.t <- transformToGenome(gr, space.skip = 1) getXScale(gr.t)library(GenomicRanges) gr1 <- GRanges("chr1", IRanges(start = c(100, 300, 600), end = c(200, 400, 800))) shrink.fun1 <- shrinkageFun(gaps(gr1), max.gap = maxGap(gaps(gr1), 0.15)) shrink.fun2 <- shrinkageFun(gaps(gr1), max.gap = 0) s1 <- shrink.fun1(gr1) getXScale(s1) # coord:genome set.seed(1) gr1 <- GRanges("chr1", IRanges(start = as.integer(runif(20, 1, 100)), width = 5)) gr2 <- GRanges("chr2", IRanges(start = as.integer(runif(20, 1, 100)), width = 5)) gr <- c(gr1, gr2) gr.t <- transformToGenome(gr, space.skip = 1) getXScale(gr.t)
parse y label information, object specific.
## S4 method for signature 'TxDb' getYLab(obj) ## S4 method for signature 'GRanges' getXLab(obj) ## S4 method for signature 'GRangesList' getXLab(obj) ## S4 method for signature 'GAlignments' getXLab(obj)## S4 method for signature 'TxDb' getYLab(obj) ## S4 method for signature 'GRanges' getXLab(obj) ## S4 method for signature 'GRangesList' getXLab(obj) ## S4 method for signature 'GAlignments' getXLab(obj)
obj |
A |
a string.
Tengfei Yin
Hg19 ideogram without cytoband information
data(hg19Ideogram)data(hg19Ideogram)
This data set provides hg19 genome information wihout cytoband information.
data(hg19Ideogram) hg19Ideogramdata(hg19Ideogram) hg19Ideogram
Hg19 ideogram with cytoband information
data(hg19IdeogramCyto)data(hg19IdeogramCyto)
This data set provides hg19 genome information with cytoband information.
data(hg19IdeogramCyto) hg19IdeogramCytodata(hg19IdeogramCyto) hg19IdeogramCyto
ideogram without cytoband information
data(ideo)data(ideo)
This data set provides hg19, hg18, mm10, mm9 genome information wihout cytoband information as a lit.
data(ideo) ideodata(ideo) ideo
ideogram with cytoband information
data(ideoCyto)data(ideoCyto)
This data set provides hg19, hg18, mm10, mm9 genome information with cytoband information as a lit.
data(ideoCyto) ideoCytodata(ideoCyto) ideoCyto
Check if an object is ideogram or not
isIdeogram(obj)isIdeogram(obj)
obj |
object |
Simply test if it's the result coming
from getIdeogram function or not, make sure it's a
GRanges and with extra column
A logical value to indicate it's a ideogram or not.
Tengfei Yin
data(hg19IdeogramCyto) data(hg19Ideogram) isIdeogram(hg19IdeogramCyto) isIdeogram(hg19Ideogram)data(hg19IdeogramCyto) data(hg19Ideogram) isIdeogram(hg19IdeogramCyto) isIdeogram(hg19Ideogram)
Utilities used for summarizing isoforms
isJunctionRead(cigar) isMatchedWithModel(model, gr)isJunctionRead(cigar) isMatchedWithModel(model, gr)
cigar |
A CIGAR string vector. |
model |
A GRanges object. |
gr |
A GRanges object. |
isJunctionRead simply parsing the CIGAR string to see if there
is "N" in between and return a logical vector of the same length as
cigar parameters, indicate it's junction read or not.
isMatchedWithModel mapping gr to model, and counting overlapped
cases for each row of model, If gr contains all the read, this
will return a logical vector of the same length as gr, and
indicate if each read is the support for this model. NOTICE: we only
assume it's a full model, so each model here is simply one
isoform. So we only treat the gaped reads which only overlapped with two
consecutive exons in model as one support for it.
Logical vectors.
Tengfei Yin
library(GenomicAlignments) bamfile <- system.file("extdata", "SRR027894subRBM17.bam", package="biovizBase") ## get index of junction read which(isJunctionRead(cigar(readGAlignments(bamfile)))) ## model <- GRanges("chr1", IRanges(c(10, 20, 30, 40), width = 5)) gr <- GRanges("chr1", IRanges(c(10, 10, 12, 22, 33), c(31, 40, 22, 32, 44))) isMatchedWithModel(model, gr)library(GenomicAlignments) bamfile <- system.file("extdata", "SRR027894subRBM17.bam", package="biovizBase") ## get index of junction read which(isJunctionRead(cigar(readGAlignments(bamfile)))) ## model <- GRanges("chr1", IRanges(c(10, 20, 30, 40), width = 5)) gr <- GRanges("chr1", IRanges(c(10, 10, 12, 22, 33), c(31, 40, 22, 32, 44))) isMatchedWithModel(model, gr)
Check if an object is a simple ideogram or not
isSimpleIdeogram(obj)isSimpleIdeogram(obj)
obj |
object |
test if it's GRanges or not, doesn't require cytoband information. But it double check to see if there is only one entry per chromosome
A logical value to indicate it's a simple ideogram or not.
tengfei
data(hg19IdeogramCyto) data(hg19Ideogram) isSimpleIdeogram(hg19IdeogramCyto) isSimpleIdeogram(hg19Ideogram)data(hg19IdeogramCyto) data(hg19Ideogram) isSimpleIdeogram(hg19IdeogramCyto) isSimpleIdeogram(hg19Ideogram)
Compute an estimated max gap information, which could
be used as max.gap argument in shringkageFun.
## S4 method for signature 'GenomicRanges' maxGap(obj, ratio = 0.0025)## S4 method for signature 'GenomicRanges' maxGap(obj, ratio = 0.0025)
obj |
GenomicRanges object |
ratio |
Multiple by the range of the provided gaps as the max gap. |
This function tries to estimate an appropriate max gap to be used for creating a shrinkage function.
A numeric value
Tengfei Yin
require(GenomicRanges) gr1 <- GRanges("chr1", IRanges(start = c(100, 300, 600), end = c(200, 400, 800))) gr2 <- GRanges("chr1", IRanges(start = c(100, 350, 550), end = c(220, 500, 900))) gaps.gr <- intersect(gaps(gr1, start = min(start(gr1))), gaps(gr2, start = min(start(gr2)))) shrink.fun <- shrinkageFun(gaps.gr, max.gap = maxGap(gaps.gr)) shrink.fun(gr1) shrink.fun(gr2)require(GenomicRanges) gr1 <- GRanges("chr1", IRanges(start = c(100, 300, 600), end = c(200, 400, 800))) gr2 <- GRanges("chr1", IRanges(start = c(100, 350, 550), end = c(220, 500, 900))) gaps.gr <- intersect(gaps(gr1, start = min(start(gr1))), gaps(gr2, start = min(start(gr2)))) shrink.fun <- shrinkageFun(gaps.gr, max.gap = maxGap(gaps.gr)) shrink.fun(gr1) shrink.fun(gr2)
mold data into data.frame usued for visualization, so it's sometimes not as simple as coersion, for example, we add midpoint varialbe for mapping.
## S4 method for signature 'IRanges' mold(data) ## S4 method for signature 'GRanges' mold(data) ## S4 method for signature 'GRangesList' mold(data,indName = "grl_name") ## S4 method for signature 'Seqinfo' mold(data) ## S4 method for signature 'matrix' mold(data) ## S4 method for signature 'eSet' mold(data) ## S4 method for signature 'ExpressionSet' mold(data) ## S4 method for signature 'RangedSummarizedExperiment' mold(data, assay.id = 1) ## S4 method for signature 'Views' mold(data) ## S4 method for signature 'Rle' mold(data) ## S4 method for signature 'RleList' mold(data) ## S4 method for signature 'VRanges' mold(data)## S4 method for signature 'IRanges' mold(data) ## S4 method for signature 'GRanges' mold(data) ## S4 method for signature 'GRangesList' mold(data,indName = "grl_name") ## S4 method for signature 'Seqinfo' mold(data) ## S4 method for signature 'matrix' mold(data) ## S4 method for signature 'eSet' mold(data) ## S4 method for signature 'ExpressionSet' mold(data) ## S4 method for signature 'RangedSummarizedExperiment' mold(data, assay.id = 1) ## S4 method for signature 'Views' mold(data) ## S4 method for signature 'Rle' mold(data) ## S4 method for signature 'RleList' mold(data) ## S4 method for signature 'VRanges' mold(data)
data |
data to be mold into data.frame with additional column that helped mapping.For example we add 'midpoint' variable to a ranged converted data.frame. |
indName |
when collapsing a GRangesList, list names are aggregated into extra column named by this parameter. |
assay.id |
define the assay id you want to convert into a data.frame. |
a data.frame object.
Tengfei Yin
Utilities for parsing (un)evaluated arguments
parseArgsForAes(args) parseArgsForNonAes(args)parseArgsForAes(args) parseArgsForNonAes(args)
args |
arguments list. |
For parseArgsForAes return a unevaluated arguments.
For parseArgsNonForAes return a evaluated/quoted arguments.
Tengfei Yin
args <- alist(a = color, b = "b") # parseArgsForAes(args)args <- alist(a = color, b = "b") # parseArgsForAes(args)
This function summarize reads from bam files for nucleotides on single base unit in a given region, this allows the downstream mismatch summary analysis.
pileupAsGRanges(bams, regions, DNABases=c("A", "C", "G", "T", "N"), ...)pileupAsGRanges(bams, regions, DNABases=c("A", "C", "G", "T", "N"), ...)
bams |
A character which specify the bam file path. |
regions |
A GRanges object specifying the region to be
summarized. This passed to |
DNABases |
Nucleotide type you want to summarize in the result and in specified order. It must be one or more of A,C,G,T,N. |
... |
Extra parameters passed to |
It's a wrapper around applyPileup function in Rsamtools
package, more detailed control could be found under manual of
ApplyPileupsParam function in Rsamtools. pileupAsGRanges function
return a GRanges object which including summary of nucleotides,
depth, bam file path. This object could be read directly into
pileupGRangesAsVariantTable function for mismatch
summary.
A GRanges object, each row is one single base unit. and
elementMetadata contains summary about this position about all
nucleotides specified by DNABases. and depth for total
reads, bam for file path.
Michael Lawrence, Tengfei Yin
## Not run: library(Rsamtools) data(genesymbol) library(BSgenome.Hsapiens.UCSC.hg19) bamfile <- system.file("extdata", "SRR027894subRBM17.bam", package="biovizBase") test <- pileupAsGRanges(bamfile, region = genesymbol["RBM17"]) test.match <- pileupGRangesAsVariantTable(test, Hsapiens) head(test[,-7]) head(test.match[,-5]) ## End(Not run)## Not run: library(Rsamtools) data(genesymbol) library(BSgenome.Hsapiens.UCSC.hg19) bamfile <- system.file("extdata", "SRR027894subRBM17.bam", package="biovizBase") test <- pileupAsGRanges(bamfile, region = genesymbol["RBM17"]) test.match <- pileupGRangesAsVariantTable(test, Hsapiens) head(test[,-7]) head(test.match[,-5]) ## End(Not run)
Compare to reference genome and compute mismatch summary for certain region of reads.
pileupGRangesAsVariantTable(gr, genome, DNABases=c("A", "C", "G", "T", "N"))pileupGRangesAsVariantTable(gr, genome, DNABases=c("A", "C", "G", "T", "N"))
gr |
A GRanges object, with nucleotides summary, each base
take one column in elementMetadata or user can simply passed the
returned result from |
genome |
BSgenome object, need to be the reference genome. |
DNABases |
Nucleotide types contained in passed GRanges object. Default is A/C/G/T/N, it tries to match the column names in elementMetadata to those default nucleotides. And treat the matched column as base names. |
User need to make sure to pass the right reference genome to this function to get the right summary. This function drop the position has no reads and only keep the region with coverage in the summary. The result could be used to show stacked barchart for mismatch summary.
A GRanges object. Containing the following elementMetadata
refNucleotide in reference genome.
readNucleotide contained in the reads at particular position, if multiple nucleotide, either matched or unmatched are found, they will be summarized in different rows.
countCount for read column.
matchLogical value, whether matched to reference genome or not
bamCharacter indicate bam file path.
Michael Lawrence, Tengfei Yin
## Not run: library(Rsamtools) data(genesymbol) library(BSgenome.Hsapiens.UCSC.hg19) bamfile <- system.file("extdata", "SRR027894subRBM17.bam", package="biovizBase") test <- pileupAsGRanges(bamfile, region = genesymbol["RBM17"]) test.match <- pileupGRangesAsVariantTable(test, Hsapiens) head(test[,-7]) head(test.match[,-5]) ## End(Not run)## Not run: library(Rsamtools) data(genesymbol) library(BSgenome.Hsapiens.UCSC.hg19) bamfile <- system.file("extdata", "SRR027894subRBM17.bam", package="biovizBase") test <- pileupAsGRanges(bamfile, region = genesymbol["RBM17"]) test.match <- pileupGRangesAsVariantTable(test, Hsapiens) head(test[,-7]) head(test.match[,-5]) ## End(Not run)
Plot color legend, simple way to check your default color scheme
plotColorLegend(colors, labels, title)plotColorLegend(colors, labels, title)
colors |
A character vector of colors |
labels |
Labels to put aside colors, if |
title |
Title for the color legend. |
Show color sheme as a legend style, labels
A graphic device showing color legend.
Tengfei Yin
cols <- getOption("biovizBase")$baseColor plotColorLegend(cols, title = "strand legend")cols <- getOption("biovizBase")$baseColor plotColorLegend(cols, title = "strand legend")
Show colors with color string or names of color vectors.
showColor(colors, label = c("color", "name"))showColor(colors, label = c("color", "name"))
colors |
A color character vector. |
label |
"color" label color with simple color string, and "name" label color with names of the vectors. |
A plot.
Tengfei Yin
## Not run: showColor(getBioColor("CYTOBAND")) ## End(Not run)## Not run: showColor(getBioColor("CYTOBAND")) ## End(Not run)
Create a shrinkage function based on specified gaps and shrinkage rate.
## For IRanges ## S4 method for signature 'IRanges' shrinkageFun(obj, max.gap = 1L) ## For GenomicRanges ## S4 method for signature 'GenomicRanges' shrinkageFun(obj, max.gap = 1L) is_coord_truncate_gaps(obj)## For IRanges ## S4 method for signature 'IRanges' shrinkageFun(obj, max.gap = 1L) ## For GenomicRanges ## S4 method for signature 'GenomicRanges' shrinkageFun(obj, max.gap = 1L) is_coord_truncate_gaps(obj)
obj |
GenomicRanges object which represent gaps |
max.gap |
Gaps to be kept, it's a fixed value, if this value is bigger than certain gap interval, then that gap is not going to be shrunk. |
shrinkageFun function will read in a GenomicRanges object
which represent the gaps, and return a function which works for
another GenomicRanges object, to shrink that object based on
previously specified gaps shrinking information. You could use
this function to treat multiple tracks(e.g. GRanges) to make sure
they shrunk based on the common gaps and the same ratio.
is_coord_truncate_gaps is used to check if a GRanges
object is in "truncate_gaps" coordiantes or not.
A shrinkage function which could shrink a GenomicRanges object, this function will add coord "truncate_gaps" and max.gap to metadata, ".ori" for oringal data as extra column
Michael Lawrence, Tengfei Yin
library(GenomicRanges) gr1 <- GRanges("chr1", IRanges(start = c(100, 300, 600), end = c(200, 400, 800))) shrink.fun1 <- shrinkageFun(gaps(gr1), max.gap = maxGap(gaps(gr1), 0.1)) shrink.fun2 <- shrinkageFun(gaps(gr1, start(gr1), end(gr1)), max.gap = maxGap(gaps(gr1), 0.1)) shrink.fun3 <- shrinkageFun(gaps(gr1), max.gap = 0) s1 <- shrink.fun1(gr1) s2 <- shrink.fun2(gr1) s3 <- shrink.fun3(gr1) metadata(s1)$coord values(s1)$.ori is_coord_truncate_gaps(s1)library(GenomicRanges) gr1 <- GRanges("chr1", IRanges(start = c(100, 300, 600), end = c(200, 400, 800))) shrink.fun1 <- shrinkageFun(gaps(gr1), max.gap = maxGap(gaps(gr1), 0.1)) shrink.fun2 <- shrinkageFun(gaps(gr1, start(gr1), end(gr1)), max.gap = maxGap(gaps(gr1), 0.1)) shrink.fun3 <- shrinkageFun(gaps(gr1), max.gap = 0) s1 <- shrink.fun1(gr1) s2 <- shrink.fun2(gr1) s3 <- shrink.fun3(gr1) metadata(s1)$coord values(s1)$.ori is_coord_truncate_gaps(s1)
Split a GRanges by formula into GRangesList. Parse variables in
formula and form a interaction factors then split the GRanges
by the factos.
## S4 method for signature 'GRanges,formula' splitByFacets(object, facets) ## S4 method for signature 'GRanges,GRanges' splitByFacets(object, facets) ## S4 method for signature 'GRanges,NULL' splitByFacets(object, facets) ## S4 method for signature 'GRanges,missing' splitByFacets(object, facets)## S4 method for signature 'GRanges,formula' splitByFacets(object, facets) ## S4 method for signature 'GRanges,GRanges' splitByFacets(object, facets) ## S4 method for signature 'GRanges,NULL' splitByFacets(object, facets) ## S4 method for signature 'GRanges,missing' splitByFacets(object, facets)
object |
|
facets |
formula object, such as . ~ seqnames. Or |
if facets is formula, factors are crreated based on interaction of
variables in formula, then split it with this factor. If facets is
GRanges object, it first subset the original data by facets
GRanges, then split by each region in the facets. If facets is
NULL, split just by seqnames. This function is used to
perform computation in conjunction with facets argments in higher
level graphic function.
A GRangesList.
Tengfei Yin
library(GenomicRanges) N <- 1000 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)) facets <- sample ~ seqnames splitByFacets(gr, facets) splitByFacets(gr) gr.sub <- GRanges("chr1", IRanges(c(1, 200, 250), width = c(50, 10, 30))) splitByFacets(gr, gr.sub)library(GenomicRanges) N <- 1000 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)) facets <- sample ~ seqnames splitByFacets(gr, facets) splitByFacets(gr) gr.sub <- GRanges("chr1", IRanges(c(1, 200, 250), width = c(50, 10, 30))) splitByFacets(gr, gr.sub)
strip dots around variables in a formula.
strip_formula_dots(formula)strip_formula_dots(formula)
formula |
A formula. such as coverage ~ seqnames, or ..coverage.. ~ seqnames. |
A formula wihout ".." around for all variables.
Tengfei Yin
obj <- ..coverage.. ~ seqnames strip_formula_dots(obj)obj <- ..coverage.. ~ seqnames strip_formula_dots(obj)
find arguments matched by formals of passed functions,
subsetArgsByFormals(args, ..., remove.dots = TRUE)subsetArgsByFormals(args, ..., remove.dots = TRUE)
args |
list of arguments with names indicate the formals. |
... |
functions used to parse formals. |
remove.dots |
logical value indicate whether to include dots in formals or not. |
argumnets that matched with passed function.
Tengfei Yin
args <- list(x = 1:3, simplify = TRUE, b = "b") subsetArgsByFormals(args, plot, sapply)args <- list(x = 1:3, simplify = TRUE, b = "b") subsetArgsByFormals(args, plot, sapply)
For graphics, like linked plot, e.g. generated by
qplotRangesLinkedToData function in package ggbio.
we need to generate a new set of coordinates which is used for even
spaced statistics track.
transformGRangesForEvenSpace(gr)transformGRangesForEvenSpace(gr)
gr |
A GRanges object. |
Most used internally for special graphics, like
qplotRangesLinkedToData function in package ggbio.
A GRanges object as passed in, with new column x.new which indicate the static track coordinates, in this way, we could map the new coordinates with the old one.
Tengfei Yin
library(GenomicRanges) gr <- GRanges("chr1", IRanges(seq(1,100, length.out = 10), width = 5)) library(biovizBase) transformGRangesForEvenSpace(gr)library(GenomicRanges) gr <- GRanges("chr1", IRanges(seq(1,100, length.out = 10), width = 5)) library(biovizBase) transformGRangesForEvenSpace(gr)
Used for coordiante genome transformation, other transformation in circular view.
## S4 method for signature 'GRanges' transformToGenome(data, space.skip = 0.1, chr.weight = NULL) ## S4 method for signature 'GRangesList' transformToGenome(data, space.skip = 0.1, chr.weight = NULL) ## S4 method for signature 'GRanges' transformToArch(data, width = 1) transformToCircle(data, x = NULL, y = NULL, ylim = NULL, radius = 10, trackWidth =10, direction = c("clockwise", "anticlockwise"), mul = 0.05) transformToRectInCircle(data, y = NULL, space.skip = 0.1, trackWidth = 10, radius = 10, direction = c("clockwise", "anticlockwise"), n = 100, mul = 0.05, chr.weight = NULL) transformToBarInCircle(data, y = NULL, space.skip = 0.1, trackWidth = 10, radius = 10, direction = c("clockwise", "anticlockwise"), n = 100, mul = 0.05, chr.weight = NULL) transformToSegInCircle(data, y = NULL, space.skip = 0.1, trackWidth = 10, radius = 10, direction = c("clockwise", "anticlockwise"), n = 100, chr.weight = NULL) transformToLinkInCircle(data, linked.to, space.skip = 0.1, trackWidth = 10, radius = 10, link.fun = function(x, y, n = 100) bezier(x, y, evaluation = n), direction = c("clockwise", "anticlockwise"), chr.weight = NULL) transformDfToGr(data, seqnames = NULL, start = NULL, end = NULL, width = NULL, strand = NULL, to.seqnames = NULL, to.start = NULL, to.end = NULL, to.width = NULL, to.strand = NULL, linked.to = to.gr) ## S4 method for signature 'GRanges' transformToDf(data) is_coord_genome(data)## S4 method for signature 'GRanges' transformToGenome(data, space.skip = 0.1, chr.weight = NULL) ## S4 method for signature 'GRangesList' transformToGenome(data, space.skip = 0.1, chr.weight = NULL) ## S4 method for signature 'GRanges' transformToArch(data, width = 1) transformToCircle(data, x = NULL, y = NULL, ylim = NULL, radius = 10, trackWidth =10, direction = c("clockwise", "anticlockwise"), mul = 0.05) transformToRectInCircle(data, y = NULL, space.skip = 0.1, trackWidth = 10, radius = 10, direction = c("clockwise", "anticlockwise"), n = 100, mul = 0.05, chr.weight = NULL) transformToBarInCircle(data, y = NULL, space.skip = 0.1, trackWidth = 10, radius = 10, direction = c("clockwise", "anticlockwise"), n = 100, mul = 0.05, chr.weight = NULL) transformToSegInCircle(data, y = NULL, space.skip = 0.1, trackWidth = 10, radius = 10, direction = c("clockwise", "anticlockwise"), n = 100, chr.weight = NULL) transformToLinkInCircle(data, linked.to, space.skip = 0.1, trackWidth = 10, radius = 10, link.fun = function(x, y, n = 100) bezier(x, y, evaluation = n), direction = c("clockwise", "anticlockwise"), chr.weight = NULL) transformDfToGr(data, seqnames = NULL, start = NULL, end = NULL, width = NULL, strand = NULL, to.seqnames = NULL, to.start = NULL, to.end = NULL, to.width = NULL, to.strand = NULL, linked.to = to.gr) ## S4 method for signature 'GRanges' transformToDf(data) is_coord_genome(data)
data |
a for function |
x |
character for variable as x axis used for transformation. |
y |
character for variable as y axis used for transformation. |
ylim |
numeric range to control the ylimits on tracks when 'y' information is involved. |
space.skip |
numeric values indicates skipped ratio of whole space, not skipped space is identical between each space. |
radius |
numeric value, indicates radius when transform to a circle. |
trackWidth |
numeric value, for track width. |
direction |
"clockwise" or "counterclockwise", for layout or transform direction to circle. |
mul |
numeric value, passed to |
n |
integer value, control interpolated points numbers. |
linked.to |
a column name of |
link.fun |
function used to generate linking lines. |
seqnames |
character or integer values for column name or index indicate variable mapped to seqnames, default |
start |
character or integer values for column name or index indicate variable mapped to start, default |
end |
character or integer values for column name or index indicate variable mapped to end, default |
width |
character or integer values for column name or index indicate variable mapped to width, default |
strand |
character or integer values for column name or index indicate variable mapped to strand, default |
to.seqnames |
character or integer values for column name or index indicate variable mapped to linked seqnames, default
|
to.start |
character or integer values for column name or index indicate variable mapped to start of linked GRanges, default |
to.end |
character or integer values for column name or index indicate variable mapped to end of linked GRanges, default |
to.width |
character or integer values for column name or index indicate variable mapped to width of linked GRanges, default |
to.strand |
character or integer values for column name or index indicate variable mapped to strand, default |
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. |
A GRanges object, with calculated new variables, including
".circle.x" for transformed x position, ".circle.y" for transformed y
position, ".circle.angle" for transformed angle.
Tengfei Yin
library(biovizBase) library(GenomicRanges) set.seed(1) gr1 <- GRanges("chr1", IRanges(start = as.integer(runif(20, 1, 2e9)), width = 5)) gr2 <- GRanges("chr2", IRanges(start = as.integer(runif(20, 1, 2e9)), width = 5)) gr <- c(gr1, gr2) gr.t <- transformToGenome(gr, space.skip = 0.1) is_coord_genome(gr.t) transformToCircle(gr.t) transformToRectInCircle(gr) transformToSegInCircle(gr) values(gr1)$to.gr <- gr2 transformToLinkInCircle(gr1, linked.to = "to.gr")library(biovizBase) library(GenomicRanges) set.seed(1) gr1 <- GRanges("chr1", IRanges(start = as.integer(runif(20, 1, 2e9)), width = 5)) gr2 <- GRanges("chr2", IRanges(start = as.integer(runif(20, 1, 2e9)), width = 5)) gr <- c(gr1, gr2) gr.t <- transformToGenome(gr, space.skip = 0.1) is_coord_genome(gr.t) transformToCircle(gr.t) transformToRectInCircle(gr) transformToSegInCircle(gr) values(gr1)$to.gr <- gr2 transformToLinkInCircle(gr1, linked.to = "to.gr")